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CAREER PREPARATION SELF-EFFICACY OF ELEMENTARY-AGE CHILDREN:
AN EXAMINATION OF PERSON, SOCIAL CONTEXT, AND CAREER
PREPARATION LEARNING EXPERIENCE VARIABLES
By

Theresa Marie Ferrari

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Family and Child Ecology

1998

ABSTRACT
CAREER PREPARATION SELF-EFFICACY OF ELEMENTARY-AGE CHILDREN:
AN EXAMINATION OF PERSON, SOCIAL CONTEXT, AND CAREER
PREPARATION LEARNING EXPERIENCE VARIABLES
By

Theresa Marie Ferrari

Understanding the process of career development, particularly its early stages, is
important. Ecological (Bronfenbrenner, 1979, 1988, 1993; Bronfenbrenner & Morris,
1998), social cognitive (Bandura, 1986), and self—efﬁcacy (Bandura, 1977, 1995, 1997)
theories provided the theoretical foundations for a study of career preparation self-
efficacy. The career preparation self-efﬁcacy beliefs of 167 third- through ﬁfth-grade
students were studied. These beliefs were examined in relation to person, social context,
and career-related variables.

Fouad, Smith, & Enochs’ (1997) scale for middle school students, an adaption of
the Career Decision-Making Self-Efﬁcacy Scale (Taylor & Betz, 1983) was modiﬁed for
use with elementary-age students. Two additional measures were developed. An index of
career exposure assessed indirect inﬂuences of the social environment on career
preparation self-efﬁcacy. A checklist of 15 activities assessed interest and participation in
career preparation learning experiences.

Descriptive statistics were used to summarize the variables of interest. The
elementary students in this study had a high level of interest in career preparation learning
experiences and a high level of self-efﬁcacy beliefs. They favored activities that involved

active exploration, yet that was the area in which they were least certain of their abilities.

Univariate analyses consisted oft tests, chi—square, ANOVA, and Pearson
product-moment correlations. Several signiﬁcant differences were found for relationships
between career exposure, interest, and learning experience participation and person
(race/ethnicity) and social context (classroom and grade) variables. Post hoc multiple
comparisons using Scheffé’s test were used to examine the nature of differences between
mean scores. The career-related variables correlated positively and signiﬁcantly with each
other and with career preparation self—efﬁcacy. However, none of the person or social
cognitive variables were signiﬁcantly related to career preparation self-efﬁcacy.

Factor analysis was used to reduce the self-efﬁcacy data, yielding three revised
subscales containing a total of 17 items. Two of the three subscale scores were used in
subsequent multivariate analyses. A series of MANCOVAs followed by post hoc
univariate tests identiﬁed a combination of variables that was signiﬁcantly related to two
outcomes of career preparation self-efﬁcacy (Awareness of Interests and Goals and
Active Exploration). Only career exposure and participation in career preparation learning
experiences signiﬁcantly contributed to an explanation of career preparation self-efﬁcacy
beliefs. While career exposure had a similar effect for both outcomes, participation in
career learning experiences contributed more in the Active Exploration model. Although
signiﬁcant, neither model accounted for a large percentage of the variance in self-efﬁcacy
scores.

The ﬁndings from this study lend support to the combination of ecological and
self-efﬁcacy theories and to the continuation of research regarding elementary-age
children’s career development. Programs for this age group are encouraged. Additional

implications and recommendations for theory, research, and practice are discussed.

Copyright by
THERESA MARIE FERRARI
1 998

ACKNOWLEDGMENTS

The tasks of graduate study and writing a dissertation are not accomplished
without considerable support from a variety of people. I want to thank my committee
members, Dr. Joanne Keith, Dr. Norma Bobbitt, Dr. Patricia Farrell, Dr. Carl Taylor, and
Dr. June Youatt, for the unique contribution each one of them has made to my graduate
experience. Joanne Keith has been much more than my advisor for the past seven years.
Her advice has strengthened my work. Moreover, she has encouraged my professional
development, so that I might do the same for others. Pat Farrell was always there to listen
and to discuss the latest twists and turns as this research and dissertation took shape. June
Youatt provided a sounding board at crucial times. Norma Bobbitt inﬂuenced my
conceptual thinking. Carl Taylor was always there with just the right support and advice.

Other faculty members in the Department of Family and Child Ecology made
noteworthy contributions to my development. Dr. Linda Nelson’s courses in Qualitative
Methods and Theory Construction were among the best experiences of my graduate
career. Dr. Marjorie Kostelnik, department chairperson, also provided support and
encouragement.

I want to thank Karen Casey for her invaluable assistance during the study design
and data collection process. Mary Biasi and Elaine Drake, elementary school teachers in
the Lansing School District, assisted during the pilot study.

I am indebted to Michael Rodriguez for his statistical consultation and to Chico
Villarruel for recommending him to me. Michael’s patient explanations of statistical

techniques enabled me to write this dissertation with the necessary conﬁdence in my

methods and results.

Surviving graduate studies goes beyond the classroom. I am forever grateful to
Sheila, John, Joey, and Michelle Smith for making me part of their family. They provided
continual love and support in so many ways, without which I would not have been able to
make it through this arduous process. The friendship and support of my colleagues
Allyson Knox, Marty Covey, and Danny Perkins helped to make it all a worthwhile, and
fun, endeavor.

The high value that my family placed on education is what brought me to this
point in my life. My parents, Marie and Hal Ferrari, have supported me throughout my
educational career. Early on, through their intentional actions, they provided numerous
enriching activities that facilitated my educational and career development. Their support
has been unwavering and unconditional throughout. There are not words to thank them
for what they have done. My other family members, my siblings, their spouses, and my
nieces and nephews, have also offered much support.

I would like to acknowledge the contributions of my grandparents, particularly
my Nana Ferretti. They all came to this country in search of opportunities, and I am
deeply grateful for the opportunities their decisions afforded me. Although they were not
here to see me attain this achievement, I know that they know what I have done. I hope
that my accomplishments, past and future, in some small way repay them for the
sacriﬁces they made.

Pursuing graduate studies can be quite a ﬁnancial burden. I was fortunate to have
scholarships and fellowships from several organizations throughout the process. I would

like to acknowledge the support from the College of Human Ecology, the Department of

vi

Family and Child Ecology, the College of Human Ecology Alumni Association, the
National Association of Extension 4-H Agents, the National Association of Extension
Home Economists, and the American Home Economics Association.

During the course of my graduate studies I also received a sabbatical from the
University of Maine Cooperative Extension which allowed me to complete my course
work. I particularly want to thank my colleague Doug Babkirk for his support of my

educational and professional goals.

vii

TABLE OF CONTENTS

LIST OF TABLES .................................................... xiii

LIST OF FIGURES ................................................... xvi
Chapter 1

INTRODUCTION ....................................................... 1

Purpose of This Study .............................................. 4

Deﬁnitions ....................................................... 5

Research Questions and Hypotheses ................................... 8

Assumptions ..................................................... 10

Theory ......................................................... 11

Human Ecological Theory .................................... 13

Bronfenbrenner’s Ecology of Human Development .......... 14

Designing ecological research. .................... 18

Human Ecology and Career Development .................. 20

Research applications. ........................... 22

Social Cognitive and Self-Efﬁcacy Theory ....................... 23

Self-Efﬁcacy ........................................ 25

Social Cognitive Career Theory .......................... 29

The Relationship of Social Cognitive Theory to Human

Ecological Theory .................................... 32

Strengths and Limitations .............................. 35

Conceptual Model for This Study .................................... 36

Signiﬁcance of the Study ........................................... 37
Chapter 2

REVIEW OF LITERATURE ............................................. 39

Development of Elementary-Age Children ............................. 40

Early Career Development .......................................... 43

Career Theories ............................................ 44

Career Choices: What Do You Want to Be? ...................... 46

Career Exposure .................................................. 48

Family ................................................... 49

Community, Neighborhood, and School ........................ 50

Reading .................................................. 51

Television ................................................ 5 1

Career Preparation ................................................ 52

Awareness ................................................ 5 3

Exploration ................................................ 54

viii

Educational Milestones ...................................... 55

Career Preparation Learning Experiences .............................. 55
Role of Learning Experiences ................................. 56
Developmentally Appropriate Career Preparation Learning

Experiences ......................................... 59

Career Self-Efﬁcacy ............................................... 61
Career Decision Making Self-Efﬁcacy .......................... 63
Career Search Self-Efﬁcacy ................................... 66
Measurement of Self—Efﬁcacy ................................. 67

Social Context Factors ............................................ 69
Socioeconomic Status ....................................... 69
Social Context - School ...................................... 71

Person Variables ................................................. 72
Gender ................................................... 73
Age ..................................................... 74
Race and Ethnicity .......................................... 75

Summary ....................................................... 76

Chapter 3
METHODOLOGY ..................................................... 78

Sample Selection ................................................. 78

Data Collection .................................................. 78
Arranging Data Collection .................................... 78
Procedures for Data Collection ................................ 79

The Study’s Context .............................................. 81
School District-Level Demographics ............................ 81
School-Level Demographics .................................. 82

Educational Environment ............................... 82
Achievement ........................................ 83
Description of Career-Related Variables ......................... 95
Job/Career Preference ................................. 95

Description of Measures Used in the Analyses: Person, Social Context, and
Career-Related Variables ..................................... 98
Person Variables ........................................... 99

Gender ............................................. 99
Age ................................................ 99
Race/Ethnicity ....................................... 99
Social Context Variables .................................... 101
School Environment ................................. 101
Mother’s Reported Level of Education ................... 101
Career-Related Measures .................................... 102
Descriptive Variables ....................................... 103
Interest in Career Preparation Learning Experiences ......... 104
Career-Related Variables .................................... 104

ix

Career Exposure Index ................................ 104

Career Preparation Leaming Experiences Checklist ......... 106
Career Preparation Self-Efﬁcacy Scale ................... 108
Development ................................. 108
Locating and adapting existing measures ........... 109
Final version .................................. 116
Data Analysis ................................................... 1 17
Descriptive Statistics ....................................... 117
Inferential Statistics ........................................ 1 1 8
Multivariate Analyses ...................................... 122
Factor Analysis ..................................... 122
Multivariate Analysis of Covariance ..................... 123
Chapter 4

RESULTS ........................................................... 126
Career-Related Variables .......................................... 127
Career Exposure ........................................... 127
Career Preparation Learning Experiences ....................... 132
Interest in Career Preparation Learning Experiences ......... 132
Interests by activity ............................ 132
Interests by child .............................. 133
Participation in Career Preparation Learning Experiences . . . . 138
Participation by activity ......................... 138
Participation by child ........................... 140
Career Preparation Self-Efﬁcacy .............................. 144
Correlations ........................................ 148

Relationships Between Career-Related Variables and Career
Preparation Self-Efﬁcacy ........................ 153
Relationships between Career Preparation Self-Efﬁcacy and Person
and Social Context Variables ..................... 154
Gender ...................................... 154
Age and race/ethnicity .......................... 155
Social context variables ......................... 155
Multivariate Analyses ............................................ 157
Factor Analysis ........................................... 157
Computation of Factor Matrix .......................... 158
Interpretation of Factor Matrix ......................... 160
Revised Self-Efﬁcacy Subscale Scores ................... 161
Multivariate Analysis of Covariance ........................... 163
Post Hoe Univariate Analyses ................................ 166
Analysis of Variance ................................. 166
Multiple Regression Analyses .......................... 169
Summary of Multivariate and Post Hoc Analyses ........... 172
Limitations of This Study ......................................... 173

Chapter 5

DISCUSSION ........................................................ 175
Study Summary ................................................. 175
Purposes of This Study ........................................... 177
Discussion of Findings ............................................ 178

Children’s Career Preferences ................................ 178
The Career Environment .................................... 181
Career Exposure ..................................... 181
Interest and Participation in Career Preparation Learning
Experiences .................................. 1 83
Career Preparation Self-Efﬁcacy .............................. 187
Multivariate Analysis: Examining a System of Variables ........... 190
Implications .................................................... l 92
Implications for Theory ..................................... 193
Implications Related to Ecological Theory ................ 193
Implications Related to Self-Efﬁcacy Theory .............. 194
Implications for Research ................................... 195
Measurement Issues .................................. 196
Additional Studies ................................... 197
Implications for Practice .................................... 199
Using Theory and Research to Guide Program Development . . 201
Design of learning experiences. ................... 201
Evaluation. ................................... 202
Timing ....................................... 203
Role of adults. ................................ 204
Summary of Program Development Implications ........... 205
Contributions of This Study ........................................ 207

APPENDIXES

Appendix A

Approval Letter from School District ...................................... 209

Appendix B

Human Subjects Approval Letter .......................................... 210

Appendix C

Sample Follow-Up Letter to Teachers ...................................... 211

Appendix D

Sample Consent Letter .................................................. 212

xi

Appendix E

Instructions for Careers and Me Survey .................................... 214
Appendix F

Careers and Me Survey ................................................. 216
REFERENCES ....................................................... 224

xii

LIST OF TABLES

Table 1
Comparison of School Demographics ....................................... 83
Table 2
Comparison of Satisfactory Test Scores on MEAP Tests for Schools in Sample ...... 84
Table 3
Characteristics of Study Participants ....................................... 88
Table 4
Race/Ethnicity Composition of Sample and Comparison Among

Schools in the Sample ............................................. 89
Table 5
Racial/Ethnic Composition of Classrooms ................................... 90
Table 6
Mother ’3 Reported Level of Education ...................................... 91
Table 7
Comparison of Mother ’5 Education Level by Classroom ........................ 93
Table 8
Comparison of Mother ’s Education Level by Race/Ethnicity ..................... 94
Table 9
Summary of Student Job Preferences ........................................ 97
Table 10
Definitions, Measurement, and Coding of Person and Social Context Variables ..... 100
Table 11
Deﬁnitions, Measurement, and Coding of Descriptive Career Variables ........... 103
Table 12
Deﬁnitions, Measurement, and Coding of Career-Related Variables .............. 105
Table 13

Career Preparation Learning Experiences .................................. 107

xiii

Table 14

Modifications to Existing Self-Eﬂicacy Measures: Career Decision Making ........ 111
Table 15
Modifications to Existing Self-Eﬂicacy Measures: Career Decision-Making -

Middle School .................................................. 112
Table 16
Modiﬁcations to Existing Self-Eﬂicacy Measures: Career Search ............... 1 14
Table 17
Items Created for Career Preparation Self-Efficacy Measure ................... 1 15
Table 18
Summary of Research Questions, Hypotheses, and Analysis ..................... 1 19
Table 19
Sources of Career Exposure ............................................. 128
Table 20
Correlation Matrix for Career Exposure Variables ........................... 129
Table 21
Summary of Means, Standard Deviations, and Tests of Significance for

Amount of Career Exposure and Person and Social Context Variables ..... 131
Table 22
Degree of Participation in Career Preparation Learning Experiences ............ 134
Table 23

Means, Standard Deviations, and Tests of Signiﬁcance for Interest in
Career Preparation Learning Experiences and Person and Social

Context Variables ................................................ 137
Table 24
Level of Participation in Career Preparation Learning Experiences .............. 139
Table 25

Means, Standard Deviations, and Tests of Significance for Participation in
Career Preparation Learning Experiences and Person and Social

Context Variables ................................................ 142
Table 26
Comparison of Level of Self-Efﬁcacy for Career Preparation Tasks .............. 146

xiv

Table 27
Comparison of Career Preparation Self-Eﬂicacy Item Means, and

Standard Deviations by Gender ..................................... 147
Table 28
Zero-Order Correlations for Career Preparation Self-Efficacy Subscales ......... 148
Table 29
Zero-Order Correlation Matrix for Career Preparation Self-Eﬂicacy Scale ........ 151
Table 30
Zero-Order Correlation Matrix for Career-Related Variables .................. 153
Table 31

Summary of Means, Standard Deviations, and Tests of Signiﬁcance for
Career Preparation Self-Eﬂicacy and Person and

Social Context Variables .......................................... 156
Table 32
Factor Analysis Results ................................................. 158
Table 33
Rotated Factor Matrix ................................................. 159
Table 34
Revised Subscales for Career Preparation Self-Eﬂicacy ....................... 162
Table 35
Correlation Matrix for Revised Career Preparation Self-Eﬂicacy Subscales Scores . . 163
Table 36
Multivariate Analysis of Covariance Results ................................. 165
Table 37
ANO VA for Awareness of Interests and Goals Self-Eﬂicacy ..................... 168
Table 38
AND VA for Active Exploration ........................................... 168
Table 39
Regression Model for Self-Eﬁ‘icacy - Awareness of Interests and Goals ........... 170
Table 40
Regression Model for Self-Efficacy - Active Exploration ....................... 170

XV

LIST OF FIGURES

Figure 1
Conceptual Map of Deﬁnitions ............................................. 6
Figure 2
Relationships Between Theories and Their Contributions to the Current Study ....... 12
Figure 3
A Representation of Bronfenbrenner’s Ecological Model ........................ 15
Figure 4
Triadic Reciprocality: Relationship of Person and Environment as Viewed

by Social Cognitive Theory ......................................... 24
Figure 5
Social Cognitive Career Theory: Person, Contextual, and Experiential Factors

Affecting Career-Related Choice Behavior ............................. 31
Figure 6
Study Model ........................................................... 37
Figure 7
Racial and Ethnic Composition of Sample ................................... 86
Figure 8
Mother’s Education Level ................................................ 92
Figure 9
Comparison of Mother’s Education by Race/Ethnicity .......................... 95
Figure 10
Job/Career Preferences by Gender ......................................... 98
Figure 11
Total Sources of Career Exposure ......................................... 128
Figure 12
Students’ Degree of Interests in Career Preparation Learning Experiences ......... 135
Figure I 3
Level of Student Participation in Career Preparation Learning Experiences ........ 140

xvi

Figure 14
Scatterplot Indicating Outliers for Active Exploration Self-Efﬁcacy

and Mother’s Education ........................................... 167
Figure [5
Correlations and Partial Correlations for Final Model ......................... 171

xvii

Chapter 1

INTRODUCTION

"What do you want to be when you grow up?" is a question that has been
routinely asked by adults and answered by young children. The simplicity of this question
belies its complexity. Answering this question involves not merely the act of naming a
job, but implies a process, a journey down well-wom paths or perhaps those less traveled.
For some this process just happens, while for others it is part of a carefully executed plan.
It represents not merely one step, but a series of steps, whether they are conscious or
unconscious, that both precede and follow the statement of a preference. It is, in part, a
function of early experiences that both create and limit options.

Elementary students are still in the process of developing and forming their
interests and have not yet made ﬁnal occupational choices. But their beliefs and actions
have already begun to affect their ﬁiture. The choices they make now, though reversible,
may set in motion a developmental trajectory (Bandura, 1995; Stipek, 1992).

The choices people make during their formative periods that inﬂuence the

direction of their development shape the course of their lives. Such choices foster

different competencies, interests, and afﬁliative preferences and set boundaries on

the career options that can be realistically considered. (Bandura, 1986, p. 431)
The consideration of career options may be viewed not only as an individual concern at a

particular moment in time, but one that affects an individual successively throughout the

course of his or her life, as well as having an impact on the broader welfare of society.

Perhaps the question that should be asked is: Do you know how to prepare to be
what you want to be when you grow up? When should this process begin and what does it
involve? What enables individuals to be ready for the choices they face? Part of this
preparation includes developing the skills necessary to succeed in the workforce. Another
aspect of preparation is acquiring the skills to engage in the process of career or
workforce preparation—such as Skills in identifying interests, searching for information,
and making educational plans. The skills associated with the content of choices have
received more attention than those involved in the career preparation process.

Elementary students are beginning to form interests, express preferences, develop
abilities, and make choices that will inﬂuence their later opportunities. Their choices and
achievements in education and career are intertwined; the foundations of both are begun
at an early age, and the seeds of future careers are thus sown. That children should start in
their early school-age years to prepare for their eventual entry into the workforce is a
natural outgrowth of developmental processes that characterize this age group. Attention,
however, has not focused on the elementary school years. While theories of career
development acknowledge that career choice is a developmental process beginning in
childhood, few have actually addressed this part of the life span. Selecting an occupation
is seen primarily as a developmental task of adolescence, not as a series of tasks that
occurs, to varying degrees, across all ages. However, those who promote a developmental
life span perspective recommend that attention should be focused on events, processes,
and life periods that are antecedents to actual career decisions (V ondracek, Lerner, &
Schulenberg, 1986). Rather than focusing at one point in time, a life-span human

developmental approach to career development puts the focus on achieving optimal

development rather than waiting for problems to occur before they are addressed. While
recognizing that elementary-age children are many years away from their transition to the
workforce, "children too young to be concerned directly with vocational decision making
could, nevertheless, receive training in the component skills eventually necessary for
effective vocational decision making" (Vondracek et al., 1986, p. 170).

Recent emphasis in educational reform has focused on the lack of adequate
preparation that students receive for the transition from school to work and the skills that
will be required to make this transition successfully (American Federation of Teachers
[AFT], 1997; Commission on the Skills of the American Workforce, 1990; Mendel,
1994/1995; Mumane & Levy, 1996; National Commission on Excellence in Education,
1983; Packer & Pines, 1996; Secretary’s Commission on Achieving Necessary Skills
[SCANS], 1991). The model school-to-work system should create a foundation in the
early grades and continue to build until the student completes his or her studies (National
School-to-Work Learning & Information Center, 1997). While many are supportive of an
inclusive K-12 focus, in practice most of the attention has been directed to high school
students. In order to accomplish the goals of a school-to-work transition system,
emphasis must be placed on children in younger age groups.

Educational reform efforts, school-to-work programs, and other youth
development efforts (e.g., America’s Promise, 1997; National 4-H Council, 1993) have
put considerable emphasis on gaining skills for employability. In addition to possessing
the necessary skills, a sense of self-efﬁcacy, that is, a belief about one’s ability to perform
speciﬁc tasks, is thought to underlie successful functioning (Bandura, 1977, 1986). As

one writer put it, "students need to have both the ‘will’ and the ‘skill’ to be successful in

the classroom" (Pintrich & De Groot, cited in Pajares, 1996, p. 553). To be successful in
the classroom, the workforce, and in life, children will need to believe they can learn new
things, continue working when they are not immediately successful, and persist in the
face of challenges. Further attention to self-efﬁcacy related to career development would

help to advance our understanding Of this complex process.

Purpose of This Study

The career development process for school-age children has received relatively
little attention compared to that given to other parts of the life span (Trice & McClellan,
1994). With the increased interest in making successful school-to-work transitions,
further attention to understanding this process for young children is warranted. Although
career development has been studied extensively, little is known about its early stages.
Therefore, the overall purpose of this study is gain an understanding of third- through
ﬁfth-grade children’s beliefs about their abilities to engage in career preparation activities
(i.e., career self-efﬁcacy). Because no studies of career self-efﬁcacy have been done with
students younger than middle school, this study represents a beginning effort to describe
it and its correlates.

Speciﬁcally, this study was designed to accomplish the following purposes:

1. Examine children’s exposure to the career environment as well as their
interest and participation in learning experiences that contribute to career
preparation.

2. Gain an overall understanding of elementary-age children’s career

preparation self-efﬁcacy beliefs.

3. Understand the relationships between career preparation self-efﬁcacy and
person, social context, and career-related variables.

4. Adapt existing measures, or develop new measures, to enable the study of
career preparation self-efﬁcacy with elementary-age children.

5. Provide theoretical and empirical support that will assist with the design of
appropriate career development educational programs for upper

elementary-age children.

Deﬁnitions

The key concepts used in this study are deﬁned below. The relationships among
these concepts are represented graphically in Figure 1. In this diagram, the concepts move
from general to those that are more speciﬁc.

Human development - Development "refers to changes in structure or function
over time" (Bjorklund, 1995, p. 4). From an ecological perspective, human development
is viewed as an evolving process of organism-environment interactions (Bronfenbrenner,
1979)

Career development - "the preparation for, choice of, entry into, and adjustment
to work throughout the life span" (Hackett, 1995, p. 232). From an ecological
perspective, it is "the growing capacity of the individual to understand and act on the
career environment" (Young, 1984, p. 154).

Career preparation - the process of engaging in activities that are designed to
develop personal and interpersonal skills and competencies needed to perform career

search and decision making activities and those needed for successful transition from

school to paid work, as well as further education and training (Bloch, 1996; Solberg,
Good, Nord et al., 1994). Career preparation activities are general activities that apply
across occupational categories.

Self-efﬁcacy - a person’s perceptions or beliefs about his or her ability to perform
speciﬁc tasks. Speciﬁcally, Bandura states that self-efﬁcacy beliefs are "beliefs in one’s
capabilities to organize and execute the courses of action required to manage prospective
situations" (Bandura, 1995, p. 2).

Career self-efﬁcacy - a general term for "self-efﬁcacy expectancies in relation to
the wide range of behaviors necessary for the career choice and adjustment process" (Betz

& Hackett, 1986, p. 280).

. “Mon—mun...‘ .—
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Figure 1 Conceptual Map of Deﬁnitions

Career preparation self-efﬁcacy - belief in one’s ability to engage in career
preparation activities that foster career awareness, career exploration, and attainment of
educational milestones.

Career awareness - career tasks involving examination of knowledge, values,
interests and skills, and goals (Sears, 1982), including self-awareness and awareness of
societal issues, that inﬂuence career choices and decisions.

Career exploration - tasks that generate information about possible career
choices. Although exploration implies some sort of active engagement, these tasks may
be viewed in two ways: as primarily informational (i.e., acquiring knowledge related to
careers) and as more experiential in nature (i.e., involving direct interaction with people).

Educational milestone - a speciﬁc task or accomplishment that indicates
academic success.

Career preparation learning experiences - experiences that involve a child’s
direct participation. These activities offer information, advice, and experience relative to
understanding of oneself and careers that can be applied to career decision making.
Includes the objects, places, events, and people that comprise these learning experiences.

Social context - the social environment, which may include the physical setting,
the people it contains, and the interactions among participants; conditions in the social
environment may promote or discourage development.

Career exposure - observations of people, settings, and interactions in everyday

life that indirectly inﬂuence career choices and behaviors.

Research Questions and Hypotheses

The following are the research questions and speciﬁc hypotheses related to them
that were examined in this study. Hypotheses were advanced in areas where previous
research was sufﬁcient to indicate relationships. Other research questions were
considered exploratory, and therefore no hypotheses were presented. Person variables
considered in this study were gender, age, and race/ethnicity. Variables related to the
social context were mother’s education, grade, and classroom. Career-related variables
were career exposure, interest in career preparation learning experiences, participation in

career preparation learning experiences, and career preparation self-efﬁcacy.

Research Question I
1.1 To what degree do children receive exposure to the career environment?
1.2 In what ways does career exposure differ based on person and social
context variables?

Hypothesis: None

Research Question 2
2.1 What interests do elementary students have in participating in career
preparation learning experiences?
2.2 In what ways do interests differ based on person and social context
variables?

Hypothesis: None

Research Question 3

3.1

3.2

In what types of career preparation learning experiences have elementary
students participated?

In what way does participation in career preparation learning experiences
differ based on a variety of person and social context variables?
Hypothesis: There is a positive relationship between mother’s education

and a child’s participation in career preparation learning experiences.

Research Question 4

4.1

4.2

4.3

What career preparation self-efﬁcacy beliefs (awareness, career
exploration, and educational milestones) do children have in the upper
elementary grades?

How is career preparation self-efﬁcacy related to other career variables
(career exposure, interest in career preparation learning experiences, and
participation in career learning experiences)?

Hypothesis: There is a positive relationship between career preparation
self-efﬁcacy and career exposure.

Hypothesis: There is a positive relationship between career preparation
self-efﬁcacy and participation in learning experiences.

In what ways do career preparation self-efﬁcacy beliefs differ based on a
variety of person and social context variables?

Hypothesis: Career preparation self-efﬁcacy does not differ by gender in

upper elementary-age children.

Research Question 5

5.1

What are the relationships among the three dimensions (awareness, career
exploration, and educational milestones) that constitute career preparation
self-efﬁcacy in this study?

Hypothesis: None

Research Question 6

6.1

What combination of person, social context, and career-related variables
contribute to career preparation self-efﬁcacy?
Hypothesis: A combination of person, social context, and career-related

variables uniquely affects multiple self-efﬁcacy outcomes.

Assumptions

The research questions for this study are based on certain theoretical assumptions:

1.

Career development is within the domain of human development
(Vondracek & Fouad, 1994; Vondracek et al., 1986).

Career preparation and development are processes that begin in early
childhood and span a person’s entire life. These processes reﬂect the
cumulative nature of experiences and decisions.

There are many internal and external inﬂuences on career development.
Career development involves dynamic interaction of a person with his or

her environment.

10

5. Participation in career preparation learning experiences provides exposure
to the career environment and increases the information available for
career decision making, thereby enhancing overall career development.

6. The effects of learning experiences on future career behavior are largely
mediated cognitively (Lent, Brown, & Hackett, 1994).

7. People shape their environment proactively, not merely by responding to

external forces (Bandura, 1986; Lent et al., 1994).

Theory

A review of the theoretical and empirical literature indicates that career issues
have been considered primarily within the realm of vocational and counseling psychology
and generally these studies do not concentrate on young children. Conversely, the topic of
career development has not been found often in the child development literature. Much of
the early research on children’s career development is descriptive, examines the content
of children’s choices, and does not use a theoretical framework to guide the investigation.
As Trice and his colleagues (Trice, Hughes, Odom, Woods, & McClellan, 1995) have
noted, "not only is more research needed, but more detailed theory needs to be developed
about what cognitive, social, and emotional factors, if any, contribute to occupational
choice and the development of attitudes about wor " (p. 321). Recently, a focus on
cognitive factors in career decision making, guided by self-efﬁcacy theory, has
strengthened what is known about the processes involved in career development (Lent &
Hackett, 1987). Fifteen years ago, Osipow (1983) had noted that "fruitful career

development theory will take shape within the larger context of human development" (p.

11

324), and further noted that there is an "emerging systems view of career behavior" (p.
314). Recent efforts to consider convergence of career theories have suggested that
developmental theories could be viewed as "umbrella theories" for career development
(Vondracek & Fouad, 1994, p. 208). The combination of social cognitive and human
ecological theories has the potential to enhance studies in this area.

In this section two different but compatible theoretical perspectives will be
explored: ecological (i.e., the ecology of human development) and social cognitive. First,
the basic components of these theories and their contributions are outlined. Self-efﬁcacy
theory, a major component of social cognitive theory, is reviewed. The formulations of
these theories speciﬁcally related to career development are discussed. Finally, the

relationships between the two major theories are explored (see Figure 2).

 

 

Ecology of Human
Development

Brontenbrenner(1979.1988,1993;
Bronfenbrenner a Morris. 1998) Bandura(1977,1986,1995,1997)

Social Cognitive and Self-
Efficacy

 

 

 

 

 

 

Social Cognitive Career Theory

 

Ecology of Career Development “N" B'°‘”"- 8' Hackett.1994)
(Vondracek at at., 1986; Young, 1984 Career Self-Efficacy
% Hackett 8. Betz (1981)

 

 

Contributions

Organize review of literature
Design research questions
Identify study variables
Create research design
Provide measurement instruments
Integrate results
Guide action

Figure 2 Relationships Between Theories and Their Contributions to the
Current Study

12

Human Ecological Theory

Human ecology seeks to bring together interdisciplinary knowledge and transcend
boundaries. "It sensitizes us to the signiﬁcant questions and to new kinds of relationships
between ideas that may not be addressed in other theories" (Bubolz & Sontag, 1993, p.
442). This theory is particularly suited to addressing questions such as "what should be
done to create, manage, or enhance environments to improve the quality of life for
humans?" (p. 429). Drawing heavily from general systems theory, human ecology is a
" general theory" that can be used to "study a wide range of problems related to people

and their relationship with various environments" (p. 424).

This characterization suggests two deﬁning factors about human ecological
theory—that it addresses practical problems and that it involves the study of people in
relation to their environments. The current level of interest in school-to-work issues
establishes that career preparation is a practical problem, and a human ecological
approach would therefore be useful to understanding its multiple dimensions. Further
application of an ecological orientation to the study of children’s career development
directs one to look for the many possible inﬂuences—within the individual, in
relationships with signiﬁcant people and systems, and within the broader social
context—on this process. Garbarino (1982) stated that the most important thing about an
ecological perspective is that "it reveals connections that might otherwise go unnoticed
and helps us to look beyond the immediate and obvious to see where the most Signiﬁcant

inﬂuences lie" (p. 18).

13

Bronfenbrenner’s Ecology of Human Development

Bronfenbrenner (1979) proposes a view of human development that
conceptualizes it as an evolving process of organism-environment interactions. He
pictures the environment in which an individual functions as consisting of multiple,
interconnected levels (see Figure 3). The effects of these environments may be either
positive or negative; that is, the environment may present risks to or provide
opportunities for development (Garbarino, 1982). These environments vary in their
proximity to the individual, from face-to-face interactions to more removed social
contexts. Each of these contexts reciprocally interacts with the others, creating a dynamic
system of complex interactions.

This systematic view of the environment is coupled with a recognition that the
individual is a system as well, and brings a complex array of characteristics to these
environments. "The human organism is conceived as a functional whole, an integrated
system in its own right in which various psychological processes—cognitive, affective,
emotional, motivational, and social—operate not in isolation, but in coordinated
interaction with each other" (Bronfenbrenner, 1993, p. 4).

Furthermore, Bronfenbrenner (1993; Bronfenbrenner & Morris, 1998) proposed
that both people and environments can be thought of as developmentally instigative or
inhibitory in that they have characteristics or qualities that "invite or discourage . . .
[interactions] of a kind that can disrupt or foster" growth (1993, p. 11). It is important to
examine these developmentally instigative features to understand how they inﬂuence
development. Among these developmentally instigative characteristics of the person is

the

14

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15

increasing capacity and active propensity of children as they grow older to

conceptualize their experience . . . . [Among the ways that children do this is

through] directive belief systems about oneself as an active agent both in relation

to the self and to the environment. (Bronfenbrenner & Morris, 1998, p. 1010)
Self-efficacy beliefs are among these directive belief systems. These beliefs are not so
much characteristics of the person per se, but are viewed as an inﬂuence on a person’s
actions within particular environments.

Bronfenbrenner (1979) viewed the most immediate environments as microsystems
because they involve a person’s direct participation and interaction, thus representing a
signiﬁcant inﬂuence. Bronfenbrenner (1993) states that for development to occur,
"ultimately, this interaction must take place in the immediate, face-tO-face setting in
which the person exists" (p. 10). Examples of these settings would be the family, the
school classroom, out-of-school clubs and organizations, and the peer group. More than
likely, a person is an active participant in several microsystems simultaneously. Beyond
the physical setting, microsystems are characterized by activities, interpersonal
relationships, and roles. Therefore, two processes are the "principal engines" of
development: (a) social interaction, with various numbers and types of people, and (b)
engagement in "progressively more complex activities and tasks" (p. 11). The outcome of
these interactions, or the "product" of a healthy microsystem, "is a child whose capacity
for understanding and successﬁrlly dealing with ever wider spheres of reality increases"
(Garbarino, 1982, p. 35).

With regard to roles, Bronfenbrenner (1979) stated that assuming a new role is

easier if one is prepared to make this transition, which he calls an ecological transition.

He contends that "development is enhanced to the extent that, prior to each entry into a

16

new setting . . . the person and members of both settings involved are provided with
information, advice, and experience relevant to the impending transition" (p. 217). As it
represents the assumption of a new role and movement into a new setting involving
varied interpersonal relationships, transition to work is an ecological transition.
Therefore, engaging in activities that prepare one for the transition would foster
development.

A mesosystem is created when there is a connection between two (or more)
microsystems, such as that between home and school. Recent emphasis on making
connections between school and the workplace is an example of a mesosystem that has
particular relevance to this study. Activities, interpersonal relationships, and roles remain
important elements of mesosystems, but in this case the focus is on the "synergistic
effects created by the interaction of developmentally instigative or inhibitory features and
processes present in each setting" (Bronfenbrenner, 1993, p. 22). The richness of
mesosystems is measured by the number and quality of connections (Garbarino, 1982).

The exosystem involves a connection between two (or more) settings, but unlike
the mesosystem, the individual does not participate directly in one of them. Therefore, the
inﬂuence on development is indirect. An example is the link between the home and a
parent’s workplace, or the school classroom and the school board. Because this inﬂuence
is indirect does not mean that it is insigniﬁcant; quite the contrary is true. At the
exosystem level, decisions are made that affect a child’s life, even if they are not directly
about children. That is, these are decisions about "the whole range of things that shape the
actual context and process of a child’s microsystem" (Garbarino, 1982, p. 44). The

passage of the School-to-Work Opportunities Act in 1994 is an example of the exosystem

17

at work. The funding provided by this legislation has focused attention on the preparation
for the workforce, has made funding available for new programs, and has stimulated
partnerships between schools and employers.

Affecting all other systems, the macrosystem represents the pervasive inﬂuence of
social, cultural, economic, and political issues. The macrosystem represents the beliefs of
a society that serve as the overarching "blueprint" of cultures and subcultures and all the
other systems embedded in them. The blueprint is

the rules and general game plan—both a source and a reﬂection of its cultural

consensus on ‘the way things should be done’ . . . . That these beliefs and

assumptions often are implicit rather than explicit does not make them any less
inﬂuential. In fact, implicit, or unstated assumptions, may be some of the most

inﬂuential. (Garbarino, 1982, p. 212)

Garbarino (1982) pointed out that social policies are rooted in the macrosystem. Making
changes at the macrosystem level produces corresponding changes in development at
other levels.

Some of Bronfenbrenner’s (1986) later work includes the concept of the
chronosystem. In other writing, he incorporated this concept into the macrosystem level
(1993; Bronfenbrenner & Morris, 1998). This dimension of the environment recognizes
that development within the person and within the environment occurs over time. In
many ways, the course of a person’s development is dependent on his or her location in
history. Certainly, this is true now for individuals growing up in a time of a global
economy with an emphasis on technological skills. This emphasis means that attention is
focused on the activities, roles, and relationships within the other levels of the system.

Designing ecological research. Several factors must be considered when

designing research from an ecological perspective. "Attention to the context or the

18

environment within which any of the processes of human development under
investigation is situated is a necessary dimension of sound ecological research" (Glossop,
1988, p. 5), although it is not the sole criterion. "Our primary interest should be not in the
outcomes . . . but the processes that produce them" (Bronfenbrenner, 1993, p. 17). With
regard to appropriate research designs, Bronfenbrenner (1988, 1993) suggests what he
calls a "process-person—context model" in order to capture the multiple dimensions of the
environment. A process-person-context model must "provide systematic information in at
least three domains . . . the context in which development is taking place, the personal
characteristics of the persons in that context, and the processes through which their
development is brought about" (1988, p. 36). An advantage of this type of design is that it
"makes possible the analysis of the mediating and moderating processes that constitute
the linkages between and within the environmental systems shaping the course of human
development" (1988, p. 39).

In summary, Bronfenbrenner’s approach is useful for several reasons. It can serve
as a framework from which to examine existing literature on career issues and it can call
attention to the relevant dimensions of the social environment to consider when
examining the career development process. These ecological analyses can stimulate
research questions, can aid in identifying variables of interest, and can point to
appropriate research designs. Bronfenbrenner directs us to pay particular attention to the
dynamic interaction between individuals within the multiple settings where their
development can be enhanced or discouraged. Furthermore, an ecological approach can

be helpful when thinking about possible interventions and therefore guides action.

19

Human Ecology and Career Development

The ecological model of human development has been called "the potentially
most promising framework for conceptualizing career development in its full complexity,
that is, as properly embedded in the stream of human development" (Vondracek et al.,
1986, p. 40). Two groups of authors have made a direct connection with
Bronfenbrenner’s work and career development (Vondracek et al., 1986; Young, 1984).
Both have noted the applicability to career development of the concept of the individual
as an active agent rather than as someone who merely reacts to changes in the
environment. Both accord a central place to the context within which career development
takes place in order to further an understanding of career development. Because of its
emphasis on contextual factors, they ﬁnd the ecological framework a useﬁrl one for
integrating diverse lines of research and for guiding the development of appropriate
research and interventions.

Young (1984) views career development as the "continuous interaction of the
person with the environment" (p. 15 6). He and his colleagues have pursued a program of
research that has studied the family’s contribution to career development by examining
the career-related conversation that occurs between parents and adolescents (Young,
1994, Young & Friesen, 1992; Young, Friesen, & Borycki, 1994; Young, Friesen, &
Dillabough, 1991; Young, Friesen, & Pearson, 1988; Young, Paseluikho, & Valach,
1997). They have undertaken this research based on an action theory approach (Young,
Valach, & Collin, 1996). The goal of such research from a contextual perspective is "to
describe career processes more fully" (p. 495). They View career development as actions

that are goal directed and intentional; these actions are controlled both cognitively and

20

socially.

Vondracek and his colleagues (1986) have used Bronfenbrenner’s (1979)
ecological systems framework as a lens with which to view the career development
process. Their review of career-related literature considered cultural, temporal, historical,
and economic factors as well as family, school, and peer inﬂuences to be part of the
broader landscape of relevant contextual factors. They demonstrated how an ecological
analysis can bring to light relevant individual and ecological variables that are important
to the study of career development. It is an approach "that facilitates the study of diverse
people in the real world" (V ondracek & Fouad, 1994, p. 211), what Bronfenbrenner
(1979) would call ecological validity. It is clear that "the cultural context in which an
individual makes a career decision cannot be overemphasized" (Vondracek & F ouad,
1994, p. 209).

Vondracek and his colleagues (1986) noted three emerging themes that are
relevant to the study of career development: (a) an emphasis on development over the life
span, (b) the need for a multidisciplinary approach, and (c) the importance of a contextual
perspective. They view career development within the realm of human development,
arguing that "contextual factors are important contributors, along with a person’s
characteristics of individuality, to behavior, to human development, and thus to career
development" (p. 10). They proposed that a developmental-contextual approach is a
useful one for understanding career development, because it can be "fully understood
only from a relational perspective that focuses on the dynamic interaction between a
changing (developing) individual in a changing context" (p. 5). This perspective also

provides a way to conceptualize career-related interventions, speciﬁcally, that they are

21

"efforts to change something (systematically and deliberately) that is already changing
without these special efforts—albeit not necessarily in the direction desired" (p. 156).

Research applications. Like Young and his colleagues, the majority of
Vondracek and his colleagues’ work has concentrated on adolescents (e. g., Silbereisen,
Vondracek, & Berg, 1997; Skorikov & Vondracek, 1997; Vondracek, 1990, 1993, 1994;
Vondracek & Schulenberg, 1986; Vondracek, Schulenberg, Skorikov, Gillepsie, &
Wahlheim, 1995; Vondracek & Skorikov, 1997). Nevertheless, an ecological approach
has applicability across the life span. With attention to developmental differences
between these age groups, it provides a framework for studying career development
issues for children in elementary school, an age group that is earlier than is usually given
consideration by most researchers. Whereas in other theories attention is focused on the
point of initial choice and entry into a work or career, in a life-span or chronosystem
perspective, the career decisions of youth are viewed as "only [the] early, and perhaps not
even the initial ones, in a series of career decisions made during a lifetime" (V ondracek ct
' al., 1986, p. 5). This calls for attention to periods that precede the transition to work,
recognizing that career preparation and development is a process and not a one-time
event.

Young (1984) stated that the task of career development research is "to specify the
nature of the person’s embeddedness in the career environment and the nature of the
individual’s actions in interacting with that environment" (pp. 154-155). A researcher’s
ability to do this is enhanced by applying an ecological perspective. In summary, an
ecological approach to career development provides guidance that is useful in

conceptualizing a study of elementary-age children. It places career development within

22

the larger domain of human development, provides a rationale for a focus on young
children, and aids in the selection of relevant variables and research designs. It also
provides a framework from which to integrate studies from a variety of disciplines, as
well as a way to conceptualize theory-based educational interventions based on the results

of these studies.

Social Cognitive and Self-Efﬁcacy Theory

Social cognitive theory has been called one of the most inﬂuential theories in
developmental psychology in the past 30 years (Bjorklund, 1995, p. 303). Researchers in
the career development ﬁeld have noted that self-efﬁcacy and social cognitive theory
"represent the most recent, and most sophisticated, theoretical explanation[s] to date of
the process of career choice and development" (Fitzgerald, Fassinger & Betz, 1995, p.
94). A concern for the social context of career development invites an examination of
social cognitive theory because it considers the relationship of the individual within the
environment.

Bandura is the principal theorist in the area of social cognitive theory (1986) and
self-efficacy as a component of this theory (1977, 1995, 1997). While the theoretical
approach that Bandura detailed in Social Foundations of Thought and Action (1986) is
often termed social learning theory, he stated that social cognitive theory is a more ﬁtting
label. He justiﬁed the appropriateness of this label by explaining that "the social portion
of the terminology acknowledges the origins of much human thought and action; the
cognitive portion recognizes the inﬂuential causal contribution of thought processes to

human motivation, affect, and action" (p. xii).

23

Social cognitive theory "posits a multifaceted causal structure that addresses both
the development of competencies and the regulation of action" (Bandura, 1997, p. 34). A
deﬁning factor in social cognitive theory is its emphasis on the active role that people
play in interacting with their environment. In other words, they are viewed as "proactive
shapers" of their development rather than passive recipients of environmental inﬂuences
(Lent et al., 1994, p. 87). Central to social cognitive theory is triadic reciprocality (Figure
4), which Bandura (1986) described as

a model of reciprocal determinism . . . [in which] behavior, cognitive and other

personal factors, and environmental inﬂuences all operate interactively as

determinants of each other . . . . The relative inﬂuence exerted by the three sets of

interaction factors will vary for different activities, different individuals, and
different circumstances. (pp. 23-24)

Personal Factors

/\

A

Behavior I > Environment

 

Figure 4 Triadic Reciprocality: Relationship of Person and Environment as
Viewed by Social Cognitive Theory
Source: ©Bandura, 1997, p. 6. Reprinted with permission.

24

Several other concepts are important to social cognitive theory. " [Because] human
thought is a powerﬁil tool for understanding and dealing effectively with the
environment" (Bandura, 1986, p. 454), cognitive factors play a central role. According to
Bandura (1986), people have important capabilities that affect their interactions with the
environment: (a) the capacity to learn by observation (i.e, through behavior that is

modeled), (b) the capacity to manipulate information symbolically, (c) the capacity for
forethought (i.e, people are able to anticipate the likely effects of different events and
actions and regulate their behavior accordingly), (d) the capacity for self-reﬂection, and
(e) the capacity for self-regulation (i.e., adjusting one’s thoughts, feelings, and actions
based on an evaluation of their outcomes). These processes operate in diverse areas of

human functioning.

Self-Efﬁcacy

Self-efficacy is a major component of social cognitive theory, as it describes
ways in which cognitive processes mediate behavior. Bandura (1995) deﬁnes self-
efﬁcacy as "beliefs in one’s capabilities to organize and execute the courses of action
required to manage prospective situations" (p. 2). Furthermore, "it is concerned not with
the skills one has but with the judgments of what one can do with whatever skills one
possesses" (Bandura, 1986, p. 391). Simply put, it is a person’s perceptions or beliefs
about his or her ability to perform speciﬁc tasks. Children who believe they are
competent (even if they are not) develop feelings of positive self-efficacy. Self-efﬁcacy is
not a ﬁxed trait, but may vary over time and from domain to domain. Furthermore, self-

efﬁcacy theory recognizes the human capability for forethought, where self-efﬁcacy is

25

viewed as a " generative capability in which cognitive, social, emotional, and behavioral
subskills must be organized and effectively orchestrated" (Bandura, 1997, pp. 36-3 7).
Developing feelings of positive self-efﬁcacy has important consequences for
children’s development. While recognizing that they are not the only inﬂuence, Bandura
(1986, 1995, 1997) proposed that efﬁcacy beliefs powerfully inﬂuence the way people
behave. They may determine whether or not a behavior will be initiated, how much effort
will be expended, how long behavior will persist in the face of obstacles and challenges,
and the level of accomplishment realized. An individual’s self-efﬁcacy beliefs inﬂuence
the choices he or she makes and the courses of action pursued. As Bandura (1986)
described,
among the types of thoughts that affect action, none is more central or pervasive
than people’s judgments of their capabilities to deal effectively with different
realities. It is partly on the basis of self-precepts of efﬁcacy that they choose what
to do, how much effort to invest in activities, how long to persevere in the face of
disappointing results, and whether tasks are approached anxiously or self-
assuredly. (p. 21)
The stronger the perceived self-efﬁcacy, "the higher the goal challenges people set
for themselves and the ﬁrmer is their commitment to them" (Bandura, 1995, p. 6).
Furthermore, self-efﬁcacy theory has been shown to have explanatory and predictive
power (Bandura, 1997). For example, perceived self-efﬁcacy has been shown to be a
much stronger predictor of academic achievement than one’s self-concept of ability
(Pajares & Miller, 1995). The self-efﬁcacy beliefs that people hold may explain why their
outcomes differs markedly from those who have similar knowledge and skills. That is,

"what people do is often better predicted by their beliefs about their capabilities than by

measures of what they are actually capable of accomplishing" (Pajares & Johnson, 1994,

26

p.313)

Bandura (1977, 1986, 1995, 1997) proposed that individuals develop and modify
their efﬁcacy beliefs in four ways: (a) performance accomplishments or mastery
experiences, (b) vicarious learning experiences, (c) verbal persuasion, and (d) assessment
of physiological and affective states. This is not a simple process because it is based on
how individuals perceive the situation: "A host of personal, social, and situational factors
affect how direct and socially mediated experiences are cognitively interpreted" (1997, p.
79). Furthermore, the process is complicated by the realization that people not only
appraise the information they receive differently, but that these different sources of
inﬂuence on efﬁcacy "rarely operate separately and independently" (1997, p. 86). That is,
a situation may contain more than one source of efﬁcacy information and the same
information may be processed by different people in different ways.

Successfully performing a behavior or mastering a task provides the most
inﬂuential source of self-efﬁcacy information. While success builds a person’s sense of
efficacy, failure undermines it, particularly if efﬁcacy has not been ﬁrmly established.
Vicarious experiences represent a second way that efﬁcacy beliefs are created and
strengthened. Observing others performing a behavior provides social models against
which one’s own potential and ability can be assessed. If people view a model they
perceive as similar to themselves, the potential for inﬂuence is greater. Modeling is not
limited to behavioral skills, but applies to more abstract cognitive skills as well. This
represents an important form of modeling, because "much human learning involves the
deveIOpment of cognitive skills to gain and use knowledge for various purposes" (1997,

p. 93). Verbal information can be conveyed in a way that strengthens or undermines a

27

sense of efficacy. Verbal encouragement can be particularly effective when it leads a
person to "mobilize greater effort and sustain it" (1997, p. 101). Care must be taken to
provide realistic information; to raise beliefs unrealistically "only invites failures that will
discredit the persuaders and further undermine the recipients’ beliefs in their capabilities"
(1997, p. 101). Finally, a person’s efﬁcacy beliefs may be altered by reducing stress
levels and negative emotional reactions to situations or tasks. A person’s self-efﬁcacy
beliefs are complex in their formation and in their impact.

[They are the] product of cognitive processing of diverse sources of efﬁcacy

information . . . . Once formed, efficacy beliefs contribute to the quality of human

functioning in diverse ways. They do so by enlisting cognitive, motivational,
affective, and decisional processes through which accomplishments are realized.

(1997,p.115)

Although this body of literature is only 20 years old, it has received much
attention and support. Researchers from a number of different perspectives have studied
self-efﬁcacy beliefs for a wide range of outcomes. Studies from diverse lines of work
support the contentions of self-efﬁcacy theory. Self-efﬁcacy beliefs have been found to
play a role in athletic performance (Bandura, 1997), as well as career issues, health
behaviors, parenting, teaching, and a variety of education-related tasks (Bandura, 1995,
1997). Originating with the work of Hackett and Betz (1981), it has been suggested that
self-efﬁcacy theory is a viable one to use for understanding career development.
Subsequent empirical investigations and theory building efforts have lent considerable
support to this view (see Hackett, 1995; Lent et al., 1994; and Lent & Hackett, 1987 for
reviews). Bandura himself (Bandura, Barbaranelli, Caprara, & Pastorelli, 1996) pointed

out the usefulness of the self-efﬁcacy for understanding the interrelationship of career and

academic behaviors.

28

Efﬁcacy beliefs shape career aspirations and pursuits in the early formative years.

The stronger the students’ beliefs in their efﬁcacy, the more occupational options

they consider possible, the greater interest they Show in them, the better they

prepare themselves educationally for different career pursuits, and the greater their

persistence and success in their academic coursework. (pp. 1206-1207)

In summary, social cognitive and self-efﬁcacy theory provide guidance for
understanding issues as complex as those related to career development. These theories

provide a strong base from which to develop a study of career development issues and

from which to its interpret results.

Social Cognitive Career Theory

Derived principally from Bandura’s (1986) general social cognitive theory, social
cognitive career theory is a recent attempt to offer a "potentially unifying framework" that
brings together relevant career variables (Lent, Brown, & Hackett, 1996, p. 376). In their
recent theory-building effort, Lent and his colleagues (1994) viewed social cognitive
theory as providing "a useful framework for encompassing diverse inﬂuences upon career
development, and most importantly, for suggesting common, central pathways through
which these diverse factors affect career behavior" (p. 81). Moreover, their model has
been designed to account for the interrelated processes of interest development, choice,
and performance as they relate to both academic and career issues.

Rather than simply viewing career development outcomes as the result of past
experiences, social cognitive career theory is

concerned with the speciﬁc cognitive mediators through which learning

experiences guide career behavior; with the manner in which variables such as

interests, abilities, and values interrelate; and with the speciﬁc paths by which
person and contextual factors inﬂuence career outcome. (p. 377)

29

Although many career development theories take into account person-environment
interactions, social cognitive career theory is distinguished from others by subscribing to
Bandura’s (1986) triadic reciprocal model of causality (see Figure 4). Self-efﬁcacy
beliefs play a major role in understanding career development outcomes, and most of the
research has focused on this aspect of social cognitive career theory.

Social cognitive career theory is concerned with person and contextual variables
that are related to the development of academic and career interests, and related choices
and outcomes. A representation of this model is presented in Figure 5, showing "how
person, contextual, and learning/experiential variables are hypothesized to inﬂuence
social cognitive variables and subsequent career development outcomes" (Lent et al.,
1996, p. 386). Researchers have designed studies to test hypothesized relationships
among social cognitive variables and career outcomes, and these studies are summarized
in several comprehensive reviews (Hackett, 1995; Lent et al., 1994; Lent & Hackett,
1987; Multon, Brown, & Lent, 1991). Generally, ﬁndings have supported these
relationships. Furthermore, speciﬁc measures of self-efﬁcacy have been developed and
tested, and their design has been theoretically guided.

In summary, the social cognitive approach is informative for several reasons. It
accords a major role to the context of development. Theory development has progressed
to the point of developing a model for how different aspects relate to each other, and this
model has been used in career development research. Speciﬁc theory-based measurement
tools exist. Knowledge of sources of self-efﬁcacy beliefs makes it possible to design

efﬁcacy-based interventions that take this information into account.

30

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The Relationship of Social Cognitive Theory to Human Ecological Theory

Clearly, no one theory describes or explains all aspects of human development.
Ecological theory and social cognitive theory are compatible perspectives, and therefore
both should provide guidance for developing an investigation of the career development
process. Both theories have been explicitly applied to this topic (i.e., the ecology of
human development by Vondracek et al., 1986 and Young, 1984, and social cognitive
theory by Lent et al., 1994 and Hackett & Betz, 1981). Both theories view the process of
cognitive and social growth as a continuous, gradual one; there are no discrete labeled
stages. Human ecology and social cognitive theory are compatible with regard to the
important role and the nature of the environment. Four major aspects of this relationship
are summarized below.

1. The environment is an essential component of both human ecological and

social cognitive theories.

It is necessary to focus on the contexts and situations in which career development
occurs because children’s perceptions of work are based on what they see, hear, and
experience in the world around them. Besides having physical dimensions, human
ecologists view environments as "subjectively experienced . . . . [people] perceive,
interpret, and create [their] meaning" (Bubolz & Sontag, 1993, p. 427). Likewise, self-
efﬁcacy theory hold that "a host of personal, social, and situational factors affect how
direct and socially mediated experiences are cognitively interpreted" (Bandura, 1997, p.
79). Environments are meaningful not only for what they actually contain, but also for the

meaning that is created within them.

32

Social contexts are wide ranging in their impact on individual career behavior. In
social cognitive theory, contextual factors act as mediators, facilitators, or deterrents in
the career development process (Lent et al., 1994). These contextual factors are in all
levels of the environment. The two theories are compatible in their agreement that
"environments do not determine human behavior but pose limitations and constraints as

well as possibilities and opportunities" (Bubolz & Sontag, 1993, p. 426).

2. There is dynamic interaction between person and environment.

Development is not just something that happens to children; they are active
participants in the contexts where development occurs. People can "respond, change,
develop, act on, and modify their environment" (Bubolz & Sontag, 1993, p. 426). Social
cognitive theory also takes the interaction of person and environmental variables into
account. Bandura’s (1986) concept of reciprocal determinism (see Figure 4) is consistent
with the idea that individuals are the producers of their development (Bronfenbrenner,
1993; Lerner, 1982). Not only is there recognition of the ability of the individual to
respond to new information and experiences, but it emphasizes that people have the
capacity for forethought. Rather than merely responding to situations as they happen,
peOple can generate new behavior and act as proactive shapers of the environment,

exercising some control over events that affect their lives (Bandura, 1986, 1995, 1997).

3. Development occurs across time.
Development involves a time dimension. Thus, examining speciﬁc life transitions

and also the cumulative effects of these changes throughout life is necessary. Not only

33

does one need to consider the development that will occur throughout the life span of an
individual, but to understand the mediating effect of the past on current and future
behavior. The dynamic nature of systems is also reﬂected in Bronfenbrenner’s (1986)
concept of the chronosystem. From the social cognitive perspective, self-efﬁcacy
provides an explanation of "the means (e. g., self-efﬁcacy) by which prior experience
affects future behavior as well as how a person actively constructs meaning in interaction
with environmental events" (Fitzgerald et al., 1995, p. 101).

Not only does career development begin early, it is part of the larger lifelong
process of human development. Events, both normative and nonnonnative, can occur in a
person’s life that alter the course of development. Likewise, efﬁcacy beliefs can have
"reverberating effects on developmental trajectories" (Bandura, 1995, p. 19). Career
choices and decisions are constantly evolving as people and their social environment
develop and change. Again, individuals are active agents in this process, as "much of . . .
behavior is motivated and regulated by internal standards and self-evaluative reactions to

their own actions . . . which serve to inﬂuence subsequent behavior" (Bandura, 1995, p.

20).

4. The theories are compatible because they both stress taking action to make
changes in peoples’ lives.
Bronfenbrenner (1979) stressed the importance of public policy and taking action
on research ﬁndings. The relationship between science and policy is reciprocal; it Should
be one of "functional integration" (p. 8). He regards communicating to policymakers and

the public and involvement in policy-making efforts as "essential for progress in the

34

scientiﬁc study of human development (1979, p. xiv), and "a professional obligation"
(1993, p. 3). Garbarino (1982) concluded that the most important aspect of policy
intervention is the "personal commitment to improving the lives of children and their
families" (p. 9).

In a somewhat different vein, not only does self-efﬁcacy theory have explanatory
and predictive power, but it guides action. Hughes (1994) has argued for the need to have
an underlying theory base when developing educational programs. Because the sources of
self-efﬁcacy beliefs are explicated, this information can be used to guide the design of
programs aimed at inﬂuencing self-efﬁcacy beliefs in speciﬁc areas.

By embedding the self-efﬁcacy belief system in a broader sociocognitive theory,

it can integrate diverse bodies of ﬁndings in varied spheres of functioning. The

value of a theory is ultimately judged by the power of the methods it yields to
produce desired changes. Self-efﬁcacy theory provides explicit guidelines on how

to develop and enhance human efﬁcacy. (Bandura, 1995, p. 2)

Taken together, the compatibility of these theories strongly suggests that the

combination of human ecological and self-efﬁcacy theories provides a meaningful

framework from which to study aspects of children’s career development process.

Strengths and Limitations

Both of these major theories have strengths as well as limitations. The human
ecological theory’s strength is its attention to the environmental inﬂuences on physical,
cognitive, social, and emotional development. Vocational and counseling psychologists
themselves have suggested that some form of systems theory may serve to "bridge" career
theories (Savickas, 1995, pp. 10-11; see also Krumboltz & Nichols, 1990; Vondracek &

Fouad, 1994). Human ecological theory can therefore serve as an overarching theory in

35

which to embed a study of elementary children’s career preparation.

On the other hand, both developers and reviewers of ecological theory
acknowledge that the concepts in the theory are highly abstract (Bubolz & Sontag, 1993,
Thomas, 1996). To adequately study career development concepts, consideration of other
theories is warranted. A recent focus on cognitive factors in career decision making has
helped in this regard (Lent & Hackett, 1987). Ecological theory does not deal very
directly with internal cognitive processes, whereas this is a strength of social cognitive
theory. Within a process-person—context model, "it is important to recognize that the
person domain is also culturally rooted" (Bronfenbrenner, 1993, p. 37). A consideration
of cognitive processes is essential because most external inﬂuences operate through
cognitive processing whereby thoughts mediate action (Bandura, 1986). Self-efﬁcacy
beliefs are thought to be particularly powerﬁrl in this regard.

"Good theory" according to Bronfenbrenner (1993) has two requirements: (a) the
theory must be able to be translated into research designs and (b) the theory can be
"applied to the phenomena that it presumes to explain as they are manifested in the actual
contexts in which they usually occur" (p. 5). Because self-efficacy and social cognitive
theory are compatible with an ecological perspective, and have relevance to the career
preparation process, both have guided the design of this study. Together they will provide

a stronger foundation than if only one or the other were used.

Conceptual Model for This Study
The model for this study has been guided by a consideration of Bronfenbrenner’s

(1979, 1988, 1993; Bronfenbrenner & Morris, 1998) human ecological theory and

36

Bandura’s social cognitive (1986) and self-efﬁcacy (1977, 1995, 1997) theories. These
theories are particularly relevant because they have been applied directly to career
development (Hackett & Betz, 1981; Lent et. al, 1994, 1996; Vondracek et al., 1986;
Young, 1984). Based on this review of the theoretical literature, a model was developed
to represent the variables of interest and to guide the study’s design and plan for analysis
(Figure 6). In order to better understand the career preparation self-efficacy of
elementary-age children, the contributions of person, social context, and career
preparation learning experiences will be examined. Each of these concepts will be

explored in greater depth in the review of literature in Chapter 2.

 

Person Variables

gender, age,
race/ethnicity

     
 
  

Awareness

  

Exploration

 

 

 

 

  
 
   
  

 

C P t' Career
areer repara '0“ , Preparation Self-
Learning ' .
. Efﬁcacy
Experiences .. .

 

 

 

   

Social Context

Variables Educational

grade, classroom, Milestones

socioeconomic status,
career exposure

  

 

 

 

Figure 6 Study Model

Signiﬁcance of the Study
In the 19908, asking the question "What do you want to be when you grow up?"

reﬂects more than a passing interest. Students’ success in making the transition from

37

school to work is of interest and concern to educators, business people, parents, and
young people themselves. This effort to examine the career preparation of elementary-age
children comes at a time when the school-to-work transition has become a major concern
in our society, giving it both immediate and practical signiﬁcance.

It is expected that this study will make both theoretical and practical
contributions. An examination of career preparation self-efﬁcacy in elementary-age
students will make a contribution to understanding early inﬂuences on their choices and
preparation for the future. Adapting an existing measure for use with elementary students
will expand the scope of career self-efﬁcacy to a younger age group than previously
studied. This will provide further understanding of self-efﬁcacy in general, and career
self-efﬁcacy speciﬁcally. Once a better understanding of career self-efﬁcacy in
elementary-age children is obtained, future studies with more complex analyses can be
undertaken.

Because people are interested in extending school-to-work and career education
programs to the elementary age group, there are practical applications as well. The results
of this study may be used to guide program design. The combination of both ecological
and self-efﬁcacy theories provides a potentially useful framework for designing
educational interventions. Understanding more about this inﬂuence will help educators
plan speciﬁc interventions and evaluate their effectiveness. Such attention to
interventions based on theory would strengthen outreach educational programs (Hughes,
1994). The creation and sharing of new knowledge in this area is vital if programs are to

be effective and relevant.

38

Chapter 2

REVIEW OF LITERATURE

This section reviews and summarizes relevant literature in middle childhood
development, children’s career development, and career self-efﬁcacy. The concept of
career preparation self-efficacy is developed through a review of related concepts. The
role of learning experiences is explored, and person and social context variables are
considered. Measurement issues speciﬁc to self-efﬁcacy and to elementary-age children
as well as gaps in the literature are discussed. In many instances, studies of children in the
elementary grades related to career issues have not been done; in these cases related
literature is reviewed. In other instances, where career-related studies could not be
located, studies of elementary-age children examining different domains of self-efﬁcacy
beliefs are included.

A study of children in the upper elementary grades (3rd - 5th grades) provides some
unique research opportunities. Career research is typically located in the realms of
vocational and counseling psychology, but there has not been much integration with other
disciplines (Savickas, 1995). Conversely, the topic of career development has not been
one that is found often in the child development literature. Understanding the career
development issues related to children in this age group requires an examination of child
development theories. Researchers can beneﬁt from taking advantage of what other
disciplines have to offer. Blending career development and child development

perspectives should prove useful.

39

Development of Elementary-Age Children

During their school-age years children experience growth in the physical,
cognitive, social, and emotional domains. Overall, some general principles apply. While
recognizing the need to examine each of these domains individually, there is an equal
need to remember that they are integrated. What occurs in one domain inﬂuences
development in the others. While most aspects of development occur in a predictable
sequence, their timing is unique to the individual. Furthermore, development may
proceed unevenly; that is, cognitive development may be on target for a child’s
chronological age, while social development may lag behind that of peers. Consequently,
a child may look and act different from another child who is the same age. Despite the
predictability of some normative milestones and transitions, events can occur in a child’s
life that alter the course of development in unexpected directions.

In general, during middle childhood (i.e., the ages from 6 to 12) children develop
an increasingly speciﬁc understanding of themselves (Stone & Lemanek, 1990). Theories
of child development propose that children are developing a sense of industry, which
involves being more focused and persistent at tasks (Erikson, 1959). If children fail to
master the challenges presented by situations in their life, they develop feelings of
incompetence and inferiority. Their self-concept also becomes hierarchical and more
differentiated; that is, beyond a general overall concept of themselves, children have a
distinct picture of themselves in the academic, physical, and social realms (Harter, 1982).
AS they progress through middle childhood, these concepts become even further
differentiated; for example, academic self-concept may contain different perceptions for

math, reading, and other academic subjects. According to Harter (1998),

40

the major advance of this age period is the ability to coordinate self-

representations that were previously differentiated or considered to be opposites. .

. . Thus, concepts previously viewed as opposing, can now be integrated, leading

to both positive and negative self-evaluations. (p. 571)

Thus, children would begin to form beliefs about their capabilities in these areas.

At ﬁrst, children’s beliefs about what they can do, or the development a sense of
self-efﬁcacy, "is facilitated by a generally optimistic (and unrealistic) opinion of their
own abilities" (Bjorklund, 1995, p. 307). Therefore, it would not be surprising to ﬁnd that
children in the elementary grades have a relatively high level of career preparation self-
efﬁcacy. This initially high level of positive self-efﬁcacy may actually facilitate
children’s development, because it gives them the conﬁdence to attempt things that they
might not otherwise try (Stipek, 1984). While younger children’s self—evaluations tend to
be unrealistically positive, older children make more realistic judgments of their
competence (Stipek, 1992; Stone & Lemanek, 1990; Wigﬁeld et al., 1997). Furthermore,
Wigﬁeld and his colleagues (Wigﬁeld et al., 1997) found that boys’ and girls’
competence beliefs differed along gender-stereotyped lines, beginning in children as
young as ﬁrst grade, and that these beliefs remained relatively stable as children got
older. Speciﬁcally, boys’ competence beliefs were higher than girls’ for math and sports,
while girls’ competence beliefs were higher for reading and music.

These perceptions of their capabilities are possible because of children’s advances
in cognitive development. Whether one subscribes to a cognitive development model
with distinct stages, an information processing model of more gradual but continuous

changes, or some blending of the two, there is agreement that children’s cognitive

capabilities improve during their school-age years (Bjorklund, 1995; Fisher & Bullock,

41

1984; Harter, 1998). AS they get older, children can process information using new and
more complex strategies that enable them to organize information into conceptual
categories. They also develop more complex ways of remembering the information they
learn and use the strategies at their disposal more effectively. The ability to focus on more
than one aspect of a problem and to detect inconsistencies develops.

Researchers conclude that efﬁcacy beliefs shape academic and career aspirations
and pursuits during early years (Bandura at al.,1996). Middle childhood is also an ideal
time to begin studying self-efﬁcacy because the way children view themselves changes
around seven or eight years of age. The development of cognitive and metacognitive
skills is essential to the development of self-efﬁcacy. Children must gain knowledge of
their capabilities in broader areas of functioning and put that knowledge to work for
themselves. Whereas in the preschool years children are dependent on adult guidance,
older children have now gained knowledge of "what they can do and what different
situations require in the way of skills. With development of cognitive capabilities, self-
efﬁcacy judgment increasingly supplants external guidance" (Bandura, 1986, p. 414).

The social environment takes on new meaning for school-age children. Children
experience a wider array of environments as their social world expands to include school
and peers. Bandura (1986) noted that "children’s experiences with their environment
provide the initial basis for developing a sense of causal efﬁcacy" (p. 414). Changing
perspectives of the self are fostered by a new-found ability to engage in social
comparison (Stipek, 1992; Stone & Lemanek, 1990).

When they enter school, children’s achievements take on increasing importance.

Numerous researchers have studied in what ways children’s beliefs about their

42

capabilities in academic tasks may inﬂuence their performance (Pajares, 1996). Of
particular concern in this area are students who greatly underestimate the skills they
possess because "they are less likely to engage in tasks in which those skills are required,
and they may more quickly give up in the face of difﬁculty" (Pajares, 1996, p. 565).
Another concern is girls’ greater tendency, as they move into early adolescence, to
underestimate their abilities and to doubt their conﬁdence in certain academic subject
areas (e. g., in math; Wellesley College Center for Research on Women, 1992). For
example, in a study of ﬁfth grade students, girls were found to have higher self-efﬁcacy
for writing than boys (Pajares & Valiante, 1997). However, in a study of ninth graders,
girls reported lower self-efficacy than boys even at similar performance levels (Pajares &
Johnson, 1996). Unrealistically low perceptions, not the lack of ability or skill, may lead
students to avoid particular courses and careers to which they might otherwise aspire. The
experiences that children have in the social environment would therefore affect their
beliefs of personal efﬁcacy. It remains to be seen if there will be any differences any

differences apparent in relation to efﬁcacy for career preparation tasks.

Early Career Development
Career development, deﬁned by Young (1984, p. 154) as "the growing capacity of
the individual to understand and act on the career environment," can be viewed as a
process that begins in early childhood and spans a person’s entire life. It represents "the
preparation for, choice of, entry into, and adjustment to work throughout the life span"

(Hackett, 1995, p. 232). Early experiences are important because

43

most occupational pursuits depend on cognitive and social competencies that may
require years to master. . . . Experiences during this formative period of life leave
their mark on personal efficacy which can, in turn, set the future direction of a life

course by affecting the choices made and successes attained. (Bandura, 1986, p.
431)

Despite the recognition of the importance of early development, much of the
career-related research has focused on high school and college students and, more

recently, on adults, but not on elementary-age children.

Career Theories

Early theories assumed that children’s career choices are unrealistic, unstable, and
lacking in structure (Ginzburg, Ginsburg, Axelrad, & Herma, 1951), and that exploratory
behaviors do not really occur until adolescence (Ginzberg et al., 1951; Super, 1990).
Although there is some similarity in what theories have proposed is important in the early
years, the stages differ in several respects: (a) the name given to the stage, (b) the age
range during which the stages are experienced, and (c) the processes thought to occur
during the identiﬁed time period. A focus on distinct stages may hamper consideration of
the cumulative nature of experiences leading up to career decisions. The combination of
these factors contributes to a disregard for the childhood period (Trice & McClellan,
1994). However, some career theories (e.g., Gottfredson, 1981, 1996) propose, and past
research studies have shown, that important conceptions about the world of work take
shape in childhood (Miller & Stanford, 1987; Phipps, 1995; Trice, Hughes, Odom,
Woods, & McClellan, 1995; Trice & McClellan, 1994). Through a comprehensive review

of the literature it is possible to ﬁnd support for a focus on elementary-age children.

44

Gottfredson (1981) characterized the process of career development as one of
circumscription and compromise. She proposed that as children get older, they reduce the
occupational options acceptable to them in four stages. At each stage preferences become
more complex but more narrow. This "zone of acceptable alternatives" (p. 548) is ﬁrst
restricted by sex (beginning around ages 6 to 8), then by social class and level of
difficulty (around ages 9 to 13), and ﬁnally by their interests, capacities, and values
(around age 14). Furthermore, "once rejected according to an earlier criterion, these
rejected options will not be reconsidered except in unusual circumstances" (p. 556), a
process called foreclosure. In a test of Gottfredson’s theory, Trice and his colleagues
(Trice et al., 1995) found that children’s interests were’affecting their choices at much
earlier ages. On the other hand, Lapan and Jingeleski (1992) found that junior high school
students circumscribed expectations for occupational attainment along gender and
prestige lines as predicted by the theory. It seems, however, that this theory presents a
static portrait of children’s development. In contrast to Gottfredson’s view that children
do not reconsider options once they have eliminated them, it is possible that children’s
early occupational preferences are not their inevitable choices, but are amenable to
change. Trice and Tillapaugh (1991) contended that "the current difﬁculty with the
theories of career development during childhood is that they describe the displacement of
early aspirations without either describing what the origins of the earlier aspirations are or
the mechanism of displacement" (p. 65). It could be that self-efﬁcacy beliefs are that

mechanism.

45

Career Choices: What Do You Want to Be?

Researchers who have studied this age group found that preschool and early
elementary children can express career aspirations (Phipps, 1995; Stroeher, 1994;
Tremaine & Schau, 1979; Vondracek & Kirchner, 1974). Many of these studies have
examined gender stereotypes. Studies show that by the time children have entered
elementary school they are able to assign gender labels to occupations that match cultural
stereotypes (Biernat, 1991; Franken, 1983).

Studies of career preferences show mixed results. Early studies showed that
perhaps girls’ range of careers was more restricted than those expressed by boys (Marini
& Greenberger, 1978; Miller & Stanford, 1987; Siegel, 1973). Boys and girls tended to
express preferences for careers traditional for their gender, even among kindergartners
(Stroeher, 1994). Some studies of elementary children have found no overlap in the
choices given by boys and girls (Siegel, 1973), while others have found very little overlap
(Phipps, 1995; Trice, 1991). For example, only girls identiﬁed a preference for secretary
and homemaker (Trice, 1991) and the vast majority of those selecting teacher and nurse
were girls (Phipps, 1995). Professional sports was identiﬁed as a choice only by boys
(Phipps, 1995; Trice, 1991).

Other studies found that girls have been more open than boys to considering a
wider range of career choices as acceptable for both men and women (Hageman &
Gladding, 1983; Tremaine & Schau, 1979) and they are less constrained in choosing
cross-gender careers (Lauver & Jones, 1991). Recent studies also found that girls more
often selected careers that require a college education (Post, Williams, & Brubaker, 1996;

Phipps, 1995; Trice et al., 1995). Despite these differences, it appears that children, in

46

general, express preferences that fall into a narrow range of commonly-known jobs.
Furthermore, these differing ﬁndings suggest that the social environment to which they
are exposed may play a signiﬁcant role in children’s career choices.

Attention to the speciﬁc research questions is important. For example, even when
children identiﬁed certain occupations as open to both men and women, their willingness
to engage in these careers showed a clear preference for those that were traditional for
their gender (Gregg & Dobson, 1980; Hageman & Gladding, 1983; McKenna & F errero,
1991; Nevill & Schlecker, 1988). Although occupational stereotypes are learned early
(Grotevant & Cooper, 1988; Reid & Stephens, 1985), it is not clear how permanent this
effect is. Children’s thinking becomes more ﬂexible as they grow older (Piaget &
Inhelder, 1969), which might allow them to consider more possibilities rather than restrict
them. For example, Franken (1983) found that as age increased, perception of
occupations became less sex-typed. There is some recent evidence that in middle school
boys and girls do not differ in their interests in various careers (Post et al., 1996). These
data were cross sectional, not longitudinal, so it is not clear if this represents a
developmental or a historical trend. Also, because relatively few career interventions have
been targeted at this elementary age group, it is unclear how participating in career-
related educational experiences might affect early expressions of vocational interests. It
may be that "the extent to which such ﬂexibility is actualized and persists may depend in
large part on the social environmental input" (Tremaine & Schau, 1979, p. 318).

Children’s preferences or choices have been discounted because they appear
random and their choices are seen as unstable and unrealistic; that is, they change their

mind. Their ﬁrst choice may be different from the eventual choice at the time of entry

47

into the workforce, so it appears their ideas are not acted upon, and therefore these early
expressions are not considered seriously as part of the preparation process. The attention
has focused on the choice itself, not the process of arriving at the choice. However, as
Seligman (1980) pointed out, "whether or not choices persist into adulthood, the
childhood years are important ones in the process of career development and considerable
attention to the early stages of that process is warranted" [italics added] (p. 124). More
recently, the need to address the career preparation process is supported by the work of
those in the career self-efﬁcacy ﬁeld, suggesting that "a critical area of career
development is the skills and competencies necessary to perform career search activities"
(Solberg, Good, Nord et al., 1994, p. 112). Currently, studies of the content of career
choices outnumber those that consider career development processes, particularly studies

of young children.

Career Exposure

Children can be exposed to the career environment through Observations of
people, settings, and events in everyday life. These observations represent an indirect
source of inﬂuence on career choices and behaviors. For the purposes of this study, it is
this indirect nature that differentiates exposure from learning experiences. It is
hypothesized that there is a positive relationship between the level of career exposure and
career preparation self-efﬁcacy.

A child’s understanding of the world Of work can increase "provided she or he has
opportunities to encounter it" (Trice & McClellan, 1994, p. 36). Children have more

knowledge about those occupations with which they have had some contact (Wehrly,

48

1973). Exposure to options may be even more critical if, as has been proposed by
Gottfredson (1981), children narrow the range of options they will consider as they get
older, rather than expand them. Four sources of potential exposure to the career
environment were of interest in this study: (a) family; (b) community, neighborhood, and

school; (0) reading; and (d) television. Each of these is considered below.

Family

It is clear that families play a signiﬁcant and complex role in children’s
preparation for the workforce and career development (Grimstad & Way, 1993;
Grotevant & Cooper, 1988; Palmer & Cochran, 1988; Penick & Jepsen, 1991;
Schulenberg, Vondracek, & Crouter, 1984; Trusty, Watts, & Erdman, 1997; Young,
1994; Young & Friesen, 1992; Young et al., 1988; Young et al., 1991; Way & Rossman,
1996a; 1996b). Families are an important source of exposure to career information and
the career environment, both directly and indirectly. Children may get their ideas about
what they want to be when they grow up from observing their parents and other family
members, thus inﬂuencing the content of their choices. For example, undergraduates with
self-employed parents were more likely to aspire to self-employment themselves
(Scherer, Brodzinski, & Wiebe, 1991). Likewise, Trice and his colleagues (Trice et al.,
1995) found that about half of the students whose parents worked together (presumably in
a family business) had similar aspirations. They speculated that these children were
"exposed to a great deal of talk about work in the home and all of these children had

visited the parents’ work site" (p. 316).

49

Families provide certain opportunities for the developing individual. This includes
access to resources (such as education and ﬁnancial), role models, and information
(Schulenberg et al., 1984). Families may provide children with the opportunity to develop
general employability skills and occupationally speciﬁc skills (Grimstad & Way, 1993).

Family processes are the speciﬁc socialization practices and parent-child relations
of the family. Parents may inﬂuence their children by fostering values related to
employment (Grimstad & Way, 1993). School performance and future career plans are
often frequent topics of conversations between children and their parents (Collins &
Russell, 1991). However, although intentional, the effects of parents’ actions may be
indirect, as many parents believe that they can lay a suitable groundwork for their
children’s career development by inﬂuencing them broadly to become responsible and

capable adults (Young, 1994).

Community, Neighborhood, and School

Contexts outside the family represented by the community, neighborhood, or
school can be a resource for career information, role models, and exposure to a wide
range of careers. Direct contact with people is important because the inﬂuence of role
models may not only be indirect, through observation, but through teaching skills and
discussion of speciﬁc careers (Seligman, Weinstock, & Heﬂin, 1991). In one study, both
boys and girls chose talking with a worker as their ﬁrst choice for additional information
about the topic of work (McKenna & Ferrero, 1991). Contact with community workers
may occur in the course of everyday life, or it may be intentionally structured by parents,

teachers, and community organizations. The community may be brought into the school

50

through career fairs, guest lectures, and mentoring programs. Community organizations
may offer similar programs in the after-school hours, thereby extending the potential for
learning. Additionally, students can have experiences in the community, such as worksite
visits. These activities can provide information relevant to the content of choices and the

process of making career decisions.

Reading

Reading is one way to acquire information about careers and to increase exposure
to career options. It may be particularly important when role models are not readily
available in the community. Previous research has shown that reading information about
role models in careers that are not typical for a student’s gender can make a positive
change in students’ attitudes toward these careers (Greene, Sullivan, & Beyard-Tyler,
1982). In a study involving Hispanic students, Haas and Sullivan (1991) obtained results
favoring the use of ethnically matched role models. They found that when students read
career descriptions, they indicated signiﬁcantly greater interest in the materials that
contained Hispanic role models. This ﬁnding supports Bandura’s (1977, 1986) contention
that preferred role models tend to be similar to the person, such as belonging to the same

racial group.

Television
Studies have shown that watching television is children’s most common out-of-
school activity. In a recent study of school-age children activities in three low-income

communities, television viewing was by far the most common activity mentioned (Miller,

51

O’Connor, Sirignano, & Joshi, 1996). Viewing people performing jobs on television is
another potential source of indirect exposure and inﬂuence. In their study of the
relationship of television viewing to career variables, King and Multon (1996) found that
younger students were more likely to be inﬂuenced by television role models. Their
sample of African American junior high school students overwhelmingly chose Aﬁican
Americans as their favorite TV characters. With the exception of those who chose
professional sports as their ideal job, however, few students indicated that the African

American characters on television were those that held jobs to which they aspired.

Career Preparation

Career preparation is viewed as the process of engaging in activities that are
designed to develop personal and interpersonal skills and competencies needed to
perform career search activities and those needed for successful transition from school to
paid work, as well as further education and training (Bloch, I996; Solberg, Good & Nord,
1994; Solberg, Good, Nord et al., 1994). These are general activities that apply across
occupational categories. Measurement of self-efﬁcacy is domain speciﬁc, so the domain
of interest (i.e., career preparation) must be explained in sufﬁcient detail to appropriately
assess it. Therefore, the question is: what tasks would constitute appropriate preparation
for elementary students?

A review of the developmentally appropriate competencies for career preparation
for elementary students from several sources was made (Drummond & Ryan, 1995;
Michigan Department of Education, 1996; National Occupational Information

Coordinating Committee [NOICC], 1989; Seligman, 1980). This review suggested that

52

the following areas should be included: (a) self-awareness, (b) occupational information
(c) decision making skills, and (d) educational planning and goal setting. The focus of
decision making is the skills involved in making the decision, not the decisions
themselves (i.e., the process vs. the content of decisions).

Combining the concepts of career decision making (Taylor & Betz, 1983) and
career search (Solberg, Good, & Nord, 1994; Solberg, Good, Nord et al., 1994) with a
review of the career-related elementary literature led to the identiﬁcation of three major
types of tasks: (a) awareness, (b) career exploration, and (c) educational milestones. The
two other areas identiﬁed in the literature review, occupational information and decision
making, were incorporated into these career preparation tasks. Occupational information
is included under career exploration and aspects of decision making are subsumed under
both career exploration and awareness. These three aspects of career preparation form the
conceptual basis for the career preparation self-efﬁcacy measure to be developed for this
study. This proposed conceptualization of career preparation can be explored through

factor analysis. A description of each aspect follows.

Awareness

Awareness has been viewed as "the inventory of knowledge, values, preferences,
and self-concepts that an individual uses in the course of making career-related choices"
(Sears, 1982, p. 139). For the purposes of this study, awareness refers to career tasks
involving examination of values, skills, and goals, including self-awareness and
awareness of societal issues. Essentially, it involves children’s self-appraisal of interests

and abilities, and awareness of ideas and situations that may temper their choices. When

53

children are conscious of this information, they can use this knowledge to guide their
subsequent choices and actions. Thus, awareness is an important part of the preparation

process.

Exploration

Career exploration refers to engaging in tasks that generate information about
possible career choices. Although exploration assumes some sort of active engagement,
these tasks may be viewed in two ways: as primarily informational (i.e., acquiring
knowledge related to careers) and as more experiential in nature (i.e., involving direct
interaction with people). Phillips’ and Blustein’s (1994) deﬁnition of exploration is more
broad, as they specify that it "entails engaging in a variety of activities that serve to
expand the individual’s knowledge of self and world of work" [italics added] (p. 378). In
this study, the self aspects of exploration are considered as awareness. The goal of
exploration activities is to "provide individuals with informatiOn to foster progress in the
selection of, entry into, and adjustment to an occupation" (Blustein, 1989, p. 194).
Therefore, exploration fosters decision making.

With a sample of college undergraduates, Blustein (1989) tested the hypothesis
that career self-efﬁcacy beliefs would be positively associated with exploratory activity.
He found that "career decision making emerged as the most prominent predictor of
exploratory activity" (p. 201). Alternately, for younger students, successful engagement
in exploratory activities may be a source of positive self-efﬁcacy beliefs. In either case,

exploration makes a contribution to the career preparation process.

54

Educational Milestones

Education and career choices and outcomes are interdependent; without
appropriate educational experiences career goals are not realized. "Performance in school
and courses taken also limit or facilitate preparation for further school and different
professions in very real ways. Students who do poorly academically . . . have more
limited options" (Stipek, 1992, p. 604). Educational milestones can be thought of as the
speciﬁc accomplishments that indicate academic success. Therefore, measures of
educational preparation have a place in studies of career development.

AS one way of assessing mathematics self-efﬁcacy in college students, Betz and
Hackett (1983) asked students to rate how conﬁdent they were that they could get a grade
of B or better on each of 16 math-related college courses. Lent, Brown, and Larkin (1986)
developed a measure, also for college students but speciﬁc to science and engineering
majors, that focused on other academic behaviors rather than course grades. They found
that students with greater self-efﬁcacy achieved higher grades and greater persistence in
science and engineering majors. While the academic behaviors were beyond the
elementary level (e.g., ability to "complete the mathematics requirement for most
engineering majors"), the concept of efﬁcacy for educational milestones has relevance for
this age group. A scale containing appropriate milestones for elementary-age children

could be constructed.

Career Preparation Learning Experiences
According to Vondracek and his colleagues (1986), "very little is empirically

known about the activities that children engage in, alone or with others, which may be

55

important to their career development" (p. 50). Career preparation learning experiences
are activities, including the objects, places, events, and people that comprise them, that
offer information, advice, and experience relative to understanding of oneself and careers,
that can be applied to career decision making. These activities or learning experiences
deserve attention from both ecological and social cognitive perspectives. In social
cognitive theory, learning experiences are postulated to be sources of self-efﬁcacy beliefs
(Bandura, 1986). Social cognitive career theory (Lent et al., 1994) proposes that person
variables (e.g., gender) may affect self-efﬁcacy indirectly by operating through learning
experiences.

Previous studies of children’s career development have not considered the
contribution that this aspect of children’s experiences might make to career outcomes.
Because experiences both limit and create options (Bubolz & Sontag, 1993; Stipek,
1992), it is important to examine what career learning experiences children have had and
in what ways they might be related to their career preparation self-efﬁcacy beliefs.
Speciﬁcally, it is hypothesized that there is a positive relationship between elementary
students’ participation in career preparation learning experiences and career preparation
self-efficacy. Therefore, including a measure of learning experiences will be necessary in
this study. This will necessarily entail specifying learning experiences appropriate for the

elementary-age student.

Role of Learning Experiences
The hallmark of the school-age years is the child’s participation in a widening

circle of new settings. These settings, or microsystems, are characterized by activities,

56

roles, and interpersonal relationships (Bronfenbrenner, 1979). Development occurs,
according to Bronfenbrenner (1993; Bronfenbrenner & Morris, 1998), when children
engage in progressively more complex activities over a period of time. According to
social cognitive career theory, learning experiences "both reﬁne abilities and shape self-
efﬁcacy and outcome expectations" (Lent et al., 1996, p. 390). This idea is echoed by
Krumboltz and Nichols (1990), whose social learning career decision making theory
accords a central place to learning experiences.

People acquire their preferences for various activities through a variety of learning

experiences. They make sense of their activities because of ideas they have been

taught or have learned through experience. They acquire beliefs about themselves
and the nature of their world through direct and indirect educational experiences.

They then take action on the basis of their beliefs using skills that they have

developed over time. (Krumblotz & Nichols, 1990, p. 162)

The need for active learning experiences is supported by other child development
theories. According to Piaget’s cognitive developmental theory, children actively
construct knowledge by interacting with their environment (Thomas, 1996). In the
school-age years, using concrete objects and direct experiences rather than abstract
concepts facilitates learning. Bronfenbrenner (1979) hypothesized that "human
development is facilitated through interaction with persons who occupy a variety of roles
and through participation in an ever-broadening role repertoire" (p. 104) and that
processes of social interaction are vital to development (1993). Similarly, the work of
Vygotsky emphasized that social interaction is essential for cognitive development
(Thomas, 1996). In particular, children need to interact with more knowledgeable people

in order to learn how to solve problems. The exchange of ideas that comes from working

with others is important in the construction of knowledge. Structured learning

57

experiences allow for this type of personal contact with the world of work. Children have
more knowledge about those occupations with which they have had direct contact
(Wehrly, 1973).

Career preparation learning experiences may be viewed as an affordance
(Vondracek et al., 1986). Affordances are objects, places, events, and other people that
"Offer, provide, and/or furnish something to the organism as long as the organism can
perceive ‘it’ as such" (p. 38). Thus, participation in career learning experiences may be
viewed as providing an opportunity to develop career self-efﬁcacy. An experience’s value
as an affordance, however, depends on whether the individual perceives the event as
meaningful. Likewise, learning experiences do not have automatic outcomes. Social
cognitive career theorists contend that "the effects of learning experiences on future
career behavior are largely mediated cognitively" (Lent et al., 1994, p. 87). Efficacy
beliefs are formed and modiﬁed through active participation. Both the person and the
context are important because different combinations of experiences and interactions in
the social environment "produce the multitude of different career choices that individuals
make" (Mitchell & Krumboltz, 1990, p. 148). As a result of learning experiences, people
make generalizations about (a) their ability to perform certain tasks, (b) which activities
they do and do not enjoy (i.e., interests), and (c) their personal values. Therefore, "each
individual has a unique history of learning experiences that results in the chosen career
pa " (p. 151). Children are, however, more than the sum of their experiences. How

children cognitively process these experiences makes a difference.

58

Developmentally Appropriate Career Preparation Learning Experiences

Proponents of career education advocate early exposure to career options in order
to acquire the information necessary to make informed career choices (Seligman, 1980).
This view is supported by the social learning career decision-making theory, which
suggests that "maximum career development of all individuals requires each individual to
have the opportunity to be exposed to the widest possible array of learning experiences"
(Mitchell & Kmmboltz, 1990, pp. 167-168). This rings true with those who can recall a
speciﬁc learning experience that helped to deﬁne a career choice more clearly. These
experiences may be the source of, and can serve to modify, career self-efﬁcacy beliefs.

The literature was reviewed in order to determine appropriate activities to include
in a measure of career preparation learning experiences. There are a variety of career
learning experiences that have been recommended for school-age children. These
experiences provide the means for students to develop interests and skills that will serve
them now and in the future. While generally not guided by theoretical considerations,
these learning experiences include elements that would potentially inﬂuence the
development of self-efﬁcacy beliefs, such as experiences that promote the successful
mastery of tasks, contact with role models, and verbal encouragement. Furthermore, these
experiences occur in a variety of settings, or microsystems.

Activities such as guest lectures, career fairs, and worksite visits can introduce
students directly to the range of options available in the world of work. Utilizing guest
speakers, including parents and members of the community, is one of the most commonly
suggested strategies for exposure to role models (Bailey & Nihlen, 1989; Bearg, 1980;

Christopher & Blocker, 1980; Flick, 1990; Miller, 1986; Navin & Sears, 1980). Worksite

59

visits provide a way for children to observe and interact with role models in the
environment where they work in a way that no other can (Beale and Nugent, 1996).
While they are beneﬁcial for all students, these experiences may be particularly useful for
children who lack adequate role models in their families (Phipps, 1995) or for providing
exposure to people in nontraditional careers (Miller, 1986; National School-to-Work
Learning & Information Center, 1996).

While in many cases children are clearly able to state what they want to be when
they grow up and why, in other instances they could not state speciﬁcally what they
would have to do to attain their career goals (Phipps, 1995). Therefore, career learning
experiences that increase children’s knowledge of skills and training required for a
variety of occupations would be appropriate for elementary-age children. In order to be
successful in the work world, students need to develop strategies to acquire and organize
information, make decisions, and solve problems (Murnane & Levy, 1996; SCANS,
1991); these skills are needed to engage in career preparation activities as well.

In addition to the above-mentioned activities, activities and projects with a career
focus can be infused with other aspects of the formal elementary school curriculum or
nonformal youth programs (e.g., 4-H and after-school programs; Ferrari, 1997). These
activities would not be limited to those that occur within a classroom setting. Those who
have examined career-related interventions found that children enjoyed the activities and
learned from them (Ferrari & Farrell, 1997; Human Service Research, 1996). Therefore,
it would not be surprising to ﬁnd that children indicate a high degree of interest in career

preparation learning experiences.

60

Career Self-Efﬁcacy

A substantial body of research shows that self-efficacy beliefs play an important
role in career development. The term career self-efﬁcacy is a general term for "self-
efﬁcacy expectancies in relation to the wide range of behaviors necessary for the career
choice and adjustment process" (Betz & Hackett, 1986, p. 280). According to self-
efﬁcacy theory, high self-efﬁcacy will facilitate effective career development, whereas
"low expectations of self-efficacy with respect to some aspect of career behavior may
serve as a detriment to optimal career choice and development" (Betz & Luzzo, 1996, p.
414). Low self-efﬁcacy may be an important factor in individuals’ elimination of possible
career options from consideration. In 1981, Hackett and Betz ﬁrst applied self-efﬁcacy
theory to career development. Since then, a number of studies have been published
regarding this domain. Self-efﬁcacy has been found to play a role in a variety of speciﬁc
career-related behaviors such as the range and type of careers considered (Betz &
Hackett, 1983), academic behavior related to career choices (Hackett, 1985; Lent et al.,
1986), career search tasks (Solberg, Good, Nord et al., 1994), decision making (Taylor &
Betz, 1983), and the ability to engage in speciﬁc occupational tasks (Rooney & Osipow,
1992). Furthermore, self-efﬁcacy has been found to be a better predictor of career-related
outcomes than other models (Luzzo, 1995; Wheeler, 1983; see also Bandura, 1997).

Hackett and Betz (1981) particularly focused on how low self-efﬁcacy beliefs
may explain women’s restriction to traditional career choices. These researchers thought
that not only would self-efﬁcacy prove to be a useful framework for integrating existing
knowledge, but that it would stimulate additional research that would provide a better

understanding of career development. They began by testing the relationship of gender

61

and self-efﬁcacy expectations to the range of occupations considered in a sample of
college men and women (Betz & Hackett, 1981). Their ﬁndings supported the hypotheses
that self-efficacy is positively associated with perceived career options and that gender
differences in self-efﬁcacy beliefs are associated with gender differences in perceptions
of nontraditional occupations. That is, while males felt more conﬁdent that they could
perform the educational requirements and job duties of both traditional and nontraditional
occupations, women felt less conﬁdent that they could perform nontraditional jobs
compared to traditional ones. In other research, although women were more likely overall
to prefer traditional occupations, those with higher self-efﬁcacy were more likely to
consider nontraditional ones (N evill & Schlecker, 1988). These ﬁndings support the
contentions of self-efﬁcacy theory regarding the role of self-efficacy beliefs in the choice
of activities and support Hackett and Betz’s (1981) application of the theory to career
development issues.

Post-Kammer and Smith (1985) replicated the Betz and Hackett (1981) study with
a sample of college-bound eighth- and ninth—grade students. They found that gender
differences emerged as early as junior high school for some occupations. Like the college
sample, they found that males had higher efﬁcacy for traditionally male occupations.
However, compared to the college student sample, junior high school students did not
have as many gender differences in self-efﬁcacy. There were no gender differences for
several traditionally male occupations that had occurred in the college sample. The
authors speculated that "this may indicate greater openness among younger students . . . .
or a lack of clarity among young students regarding actual educational requirements

and/or job duties" (p. 557). These ﬁndings are particularly relevant to understanding the

62

 

career development processes of young children because individuals with low efﬁcacy
expectations would be less likely to initiate desired career preparation behaviors.
However, if children are more open to considering a variety of occupations when they are
younger, they may develop efﬁcacy beliefs that would allow them to keep their options
open. Lent and Hackett (1987) recommended that further work was necessary to clarify
the utility of career self-efﬁcacy with younger populations.

Because self-efﬁcacy is task speciﬁc, different aspects of career self-efficacy have
been studied, with two broad categories emerging. The career self-efﬁcacy studies may
be classiﬁed as those that examine content (such as academic major, occupational
choices, and occupational tasks) and those that examine tasks involved in the process of
career development (such as decision making and career search behaviors). Hackett
(1995) noted that the earlier studies of career self-efﬁcacy tended to focus on the content
of choices. A focus on career outcomes (i.e., choices) rather than processes has been
noted as a limitation of career theories (Schulenberg at al., 1984). Recently, processes
such as decision making are beginning to be seen as central to career development. Betz
and Hackett (1986) encouraged investigations of the process of career choice that would
show how self-efﬁcacy might be involved in how career and academic decisions are
made. Therefore, consideration of the process aspects of career development may help

advance the understanding of this important part of the whole in more detail.

Career Decision Making Self-Efﬁcacy
Career decision-making self-efﬁcacy is part of the more global concept of career

self-efﬁcacy. Even so, the domain of career decision making covers a broad range of

63

general tasks that apply across occupational categories. Studies of career decision-making
self-efficacy are concerned with the process of how decisions are made as opposed to
what the decision is. A focus on the process is important, because as Bandura (1997)
noted, "career decision making is not simply a matter of picking particular occupational
pursuits, but rather developing facility in solving problems when things are not easily
predictable" (p. 427).

Taylor and Betz (1983) were the ﬁrst to examine self-efficacy beliefs regarding
the skills and activities necessary for effective career decision making. They developed
the Career Decision-Making Self-Efﬁcacy Scale (CDMSE) with a sample of college
undergraduates. The 50-item CDMSE measures a person’s beliefs about whether he or
she can successfully complete tasks necessary for making career decisions. They
designed this measure with ﬁve subscales; however, factor analysis supported only one
general factor. Therefore, decision making as measured by the CDMSE is viewed as a
general measure of career self-efﬁcacy (Taylor and Pompa, 1990). The CDMSE has been
used to study the relationship between career decision-making self-efficacy and a wide
variety of career-related topics (see Hackett, 1995). These studies have established the
utility of self-efficacy theory in understanding the process aspects of career development.

Using the CDMSE with a sample of college undergraduate students, Taylor and
Betz (1983) found that higher career decision-making self-efﬁcacy was signiﬁcantly
related to less career indecision. Whereas other studies that examined preferences for
traditional and nontraditional careers found gender differences, they did not ﬁnd overall
gender differences in career decision-making self-efﬁcacy. In other research using the

CDMSE, Bergeron and Romano (1994) and Luzzo (1993b) also reported a lack of gender

64

differences in career decision making self-efficacy with samples of college
undergraduates and community college students respectively. Lent and Hackett (1987)
suggested that the lack of gender differences may be because career decision-making is
viewed as a gender-neutral task; that is, the speciﬁc tasks and behaviors necessary for
effective career decision making are not viewed as linked to either males or females.

The CDMSE instrument was adapted and used in a study of middle school
students (Fouad, Smith, & Enochs, 1997). The instrument was used in conjunction with a
career intervention designed to provide students with sources of self-efficacy in career
awareness and math and science performance (Fouad, 1995). The students were from an
inner city school with a largely Hispanic population. This program was explicitly
designed to provide sources of efﬁcacy information directly (i.e., verbal persuasion,
vicarious learning, and role models) and indirectly through opportunities to be successful
in math and science projects.

The 50 original career decision-making items were reduced to 12; they were made
relevant for middle school students by revising the wording and reducing the number of
responses. In this study, the middle school CDMSE was used along with math-science
self-efﬁcacy (MSSE) scales. Similar to what had been found for older students (Taylor &
Pompa, 1990), the results of this study showed that process and content were distinct
factors for middle schools students (Fouad et al., 1997). As Hackett (1995) suggested,
this is because "steps in the process of making a career decision are largely independent
of which careers [or subjects] are being considered" (pp. 243-244). Likewise, consistent
with the ﬁnding of Taylor and Betz (1983), there were no gender differences in the

CDMSE scores of middle school students. Extending these ﬁndings to the present study,

65

it can be hypothesized that a study of career preparation self-efﬁcacy beliefs of
elementary-age students will not ﬁnd gender differences.

In a study of math and science self-efﬁcacy, Fouad and Smith (1996) used the
middle school scales to further test parts of the social cognitive model proposed by Lent,
and his colleagues (Lent et al., 1994). Using a LISREL model that contained gender and
age, they found that these person variables did not have a signiﬁcant effect on self-
efﬁcacy. They tested the model for three different ethnic groups (African American,
White, and Hispanic) and found it to be robust. That is, they found that "self efﬁcacy had
a fairly large direct inﬂuence on interests, which in turn had a fairly large effect on
intentions" (p. 344). Their data supported the social cognitive model proposed by Lent
and his colleagues (Lent et al., 1994). Because neither background contextual inﬂuences
nor learning experiences were part of the model, they were not able to test for potential
effects of these variables; this was noted as a limitation and attention to these variables in
future research was recommended. Furthermore, they noted the need to study diverse

samples with respect to ethnicity and socioeconomic status.

Career Search Self-Efﬁcacy
The Career Search Efﬁcacy Scale (CSES) was developed by Solberg and his
colleagues (Solberg, Good, Nord et al., 1994) to "assess the degree of conﬁdence a person
has for performing a variety of career search tasks" (p. 113). The 35-item CSES was
originally designed to assess three aspects of the career search process: (a) personal
exploration, (b) career exploration, and (c) job search exploration. Factor analysis

produced a four-factor structure: personal exploration, job search, interviewing, and

66

networking. In subsequent factor analysis, CSES and CDMSE subscales loaded together
on one factor assessing career efficacy, suggesting convergent validity. Other personality
scales (e.g., assertiveness, instrumentality) loaded on a separate factor, suggesting that the
CSES has good discriminant validity. This scale was developed with a sample of college
undergraduates. Additional studies using the CSES were not located during the time of

this review.

Measurement of Self-Efficacy

A strength of self-efficacy theory has been the attention afforded to measurement.
Self-efﬁcacy beliefs are said to vary in terms of level, generality, and strength (Bandura,
1986, 1997). These characteristics are important to their measurement.

Self-perceptions of efﬁcacy vary across different activity domains, different levels

of demands within activity domains, and different environmental circumstances of

performance. Therefore, the role of perceived self-efﬁcacy in psychosocial
functioning is best elucidated by self-efﬁcacy measures tailored to particular
domains of functioning rather than as a global disposition assessed by an omnibus

test. (Bandura, 1986, pp. 371-372)

Because self-efﬁcacy is domain speciﬁc, instruments designed to measure self-
efﬁcacy must be tailored to the domain of interest. Therefore, there is no one measure of
self-efﬁcacy. Measures that are too global, and therefore not speciﬁc enough to the
domain of interest, diminish the predictive value of self-efficacy measures (Paj ares,
1996). The measure must correspond to speciﬁc tasks or situations of interest; in turn,
people must generate judgments about their capabilities with a clear activity or task in

mind. A measure that is not tailored to the domain of interest may not produce any

signiﬁcant relationships. The ﬁndings obtained may differ from other research studies of

67

that domain, thereby creating confusion when interpreting results. For example, whereas
other studies relating career self-efficacy to indecision found signiﬁcant effects, a study
by Lent, Brown, and Larkin (1987) found that self-efﬁcacy did not signiﬁcantly relate to
vocational indecision. However, the measures used in the study examined perceptions of
academic competence, which may not be speciﬁc enough to the concept of vocational
indecision to produce a relationship. It is also important to note that "different types of
efﬁcacy measures serve different explanatory and predictive purposes" (Bandura, 1997, p.
423). To examine career issues for elementary-age students, it is necessary to delineate as
clearly as possible the domain of interest and strive for a balance between speciﬁcity and
generality based on the goals of the study. For this reason, the concept of career
preparation has been explored in detail earlier in this review.

Another measurement issue concerns the suitability of the measure for
elementary-age children and the ability of the child to complete a self-report instrument.
One reason for focusing on upper elementary age students is that, due to their increased
cognitive capabilities, this is the age when self-reports become more meaningful (Stone
& Lemanek, 1990). Stone and Lemanek (1990) offered other important considerations for
constructing self-report measures for this age group: (a) the use of language suitable to
children’s vocabulary and reading abilities, (b) the use of concrete and action-oriented
questions, (c) the use of pictures to attract and maintain interest and to provide meaning

to the words. These considerations were addressed in the instrument design.

68

Social Context Factors
Consideration of human ecological and social cognitive theories calls attention to
the role that contextual factors play in affecting development. It is clear that social,
cultural, and economic conditions play a role in shaping individuals’ experiences and
beliefs. Exposure to the career environment has been discussed previously. Two
additional contextual factors deserve consideration in this review: (a) socioeconomic

status (SES) and (b) the school environment.

Socioeconomic Status

Those who have been involved in career research have recommended that
"research needs to focus on low-SES individuals to understand how various vocational
variables may be tied to economic variables" (F ouad & Smith, 1996, p. 344). Many
students are at risk for negative career outcomes because they live in conditions that
diminish their opportunities for personal and school success. Increasing social and
economic pressures may mean that families lack the "emotional, ﬁnancial, experiential,
and cognitive supports that a developing youngster requires" (Heath & McLaughlin,
1991). Because of these situations not all children have the same access to choices,
opportunities, positive role models, and relationships (Grotevant & Cooper, 1988). The
low-income environment may be problematic for the development of positive self-
efﬁcacy by limiting opportunities and thereby posing a threat to development. For
example, low-income individuals may not have access to quality educational experiences
and positive role models. In social cognitive theory, socioeconomic factors are posited to

affect children’s development indirectly, principally through their impact on family, peer,

69

and self-processes (Bandura et al., 1996). Therefore, for this study it is hypothesized that
there will be a positive relationship between elementary children’s participation in career
preparation learning experiences and SES.

Differential access to educational opportunities experienced by low-income
students affects career outcomes (W.T. Grant, 1988). For example, Bloch (1996) reported
that at-risk high school students were the group least served by workforce preparation
programs. Low-income students are more at risk for dropping out (National Center for
Education Statistics [N CES] cited in Bloch, 1996). Differences in knowledge of jobs
have also been found with respect to socioeconomic background, with children from
lower-SES backgrounds expressing more restriction of choices (Cook et al., 1996;
Hageman & Gladding, 1983; Stroeher, 1994). This may be based on lack of exposure, or
it may represent a realistic appraisal of their prospects that they have learned from
observing their environment.

Mother’s education level is an approximation of socioeconomic status. Parent’s
education has been shown to inﬂuence a child’s educational opportunities (N CES, 1997).
"In general, children of parents with higher levels of education perform better, on
average, on assessments of student achievement" (pp. 2-3). Furthermore, "parent’s level
of education remains higher for white children than for black or Hispanic children" (p. 7).
One could hypothesize that children from high-SES families would participate in a
greater number of educational learning experiences. For example, studies of children’s
out-of school activities have found differences in participation in types of activities based
on income level. Miller and her colleagues (Miller et al., 1996) found that fewer than 6

percent of the children in their low-income sample participated in formal lessons.

70

Studies need to examine the possibility that differences exist based on SES. In
their study of occupational choices, Hannah and Kahn (1989) found that the low-SES
group reported signiﬁcantly lower occupational self-efﬁcacy expectations than the high-
SES group regardless of the occupational prestige level of the job they were considering.
Interaction effects are another possibility. It is possible that person variables, such as
gender, and SES may interact. For example, high-SES 12th grade girls were more likely
than low-SES girls to choose male-dominated occupations and were less likely than low-
SES girls to consider female-dominated occupations (Hannah & Kahn, 1989). Samples
with a restricted range on this variable may produce no signiﬁcant differences (Hackett,

1995). Therefore, ﬁndings must be interpreted in light of the sample composition.

Social Context - School

Grade and classroom represent two ways to capture the school context. Grade
refers to the sequential level of placement in an educational institution. It is assumed that
different grade levels pursue a different curriculum, and successive advancement through
these courses of study implies increasing levels of complexity. Therefore, students may
have a chance to know more and to do more. The classroom is the physical setting where
learning takes place within a school. As a microsystem, it also represent a social context;
that is, it consists of the people contained within the setting and their interactions
(Bronfenbrenner, 1979). The context in which learning occurs is important because
“competence is not a ﬁxed characteristic of the child but an emergent characteristic of the
child in a speciﬁc context” (Fisher & Bullock, 1984, p. 92). Certainly the teacher plays a

signiﬁcant role in the classroom setting. The teacher creates the learning environment as

71

well as structures the educational experiences of children in the classroom. Many factors
inﬂuence these interactions, and their relationships are complex.

Of interest to educators is to understand how race, SES, and other cultural
inﬂuences combine to shape educational experiences. According to a report for the
National Center for Education Statistics (1997), "factors such as student English language
proﬁciency, family income, parents’ education, and family structure affect the social
context of education" (p. 2). Based on their level of poverty (as measured by school lunch
eligibility), schools provided different learning environments. The NCES (1997) found
that high poverty schools provided lower levels of enriching activities and technology
(such as giﬁed and talented programs, extended-day programs, and connections to the
Internet). Teachers in high-poverty schools reported more problems with school violence,
lack of parental involvement, and absenteeism. Minority students may be
disproportionately affected by these situations, as they are more likely than white students
to attend high poverty schools. Clearly, the school environment represents a signiﬁcant

inﬂuence, as what occurs in this environment may either foster or hinder development.

Person Variables
Bronfenbrenner and Morris (1998) urged the inclusion of age, gender, and
ethnicity as study variables.

They are . . . so pervasive in affecting future development that their possible
inﬂuence routinely needs to be considered in relation to the particular
phenomenon under investigation . . . . These are the familiar demographic factors
of age, gender, and ethnicity. Another reason for this recommendation is that all
three of these factors, although based on differing physical characteristics of the
Person, also place that person in a particular environmental niche that deﬁnes his
or her position and role in society. (p. 1013)

72

The effectiveness of these characteristics to inﬂuence development "derives from their

capacity to inﬂuence the emergence and operation of proximal processes" (p. 1009), that
is, the processes that occur on a fairly regular basis over an extended period of time. The
following section will review the literature regarding the importance of gender, age, and

race/ethnicity as study variables.

Gender

Self-efﬁcacy theory was originally applied to the domain of career development
to understand women’s career dilemmas (Hackett & Betz, 1981). As Hackett (1995)
describes the situation, "if female students prematurely close off viable (and
overwhelmingly higher-status) nontraditional career options due to weak self-efﬁcacy
beliefs, their chances of ultimately choosing a satisfying, well-paid career are
signiﬁcantly lowered" (pp. 235-236). Hackett’s (1995) review of gender differences
concluded that gender differences in occupational efﬁcacy are common with diverse
samples, but they are usually not found in more homogenous samples. Furthermore, they
are "particularly likely to arise in response to gender-stereotypical tasks, activities, and
careers" (p. 236) compared with those that are perceived as gender neutral.

Taylor and Betz’s (1983) study of career decision-making self-efﬁcacy did not
ﬁnd gender differences, and this ﬁnding was replicated in other studies (Bergeron &
Romano, 1994; Fouad et al., 1997; Luzzo, 1993b, 1995). This may be because decision
making, a process aspect of career preparation, is considered a gender-neutral task.
Despite the fact that all previous research has been conducted with older samples, it is

hypothesized that girls and boys in the elementary grades will not differ signiﬁcantly on

73

the career preparation self-efﬁcacy scores. However, it is possible that girls experience
differential socialization due to their level of participation in career preparation
experiences. Learning experiences are postulated to be sources of efﬁcacy information, so
an examination of possible gender differences remains an important consideration in this

exploratory study.

Initially career self-efficacy studies were done with samples consisting of college
undergraduates. Gradually, studies were extended to include high school samples and
adult samples. Of the studies that have been conducted with middle school and
elementary school samples, most of these examined efﬁcacy for the content of choices or
for a particular academic subject (e. g., mathematics or writing). Only one study was
found that addressed career decision making, a process-related aspect of career self-
efﬁcacy, in middle school students (Fouad et al., 1997); no studies were found with
elementary students. Despite Betz and Hackett’s (1986) recommendation that career self-
efﬁcacy should be studied in samples other than college students, so far this has included
only those at high school and middle school levels.

Studies examining young children’s appraisals of their capabilities indicate that
they may judge them rather highly (Bjorklund, 1995), so it would be expected to ﬁnd that
elementary-age children have high scores on a measure of career preparation self-efﬁcacy
beliefs. Self-efﬁcacy beliefs may change as children get older, with lower self-efﬁcacy
beliefs perhaps representing a more realistic self-appraisal. The effect of age may interact

with another variable, such as gender. For example, in studies of writing self-efﬁcacy,

74

ﬁfth-grade girls had higher efﬁcacy beliefs than boys (Pajares & Valiante, 1997), while a
study of ninth-grade students reported that boys had higher self-efﬁcacy beliefs in this
content area (Pajares & Johnson, 1996). Although these results are cross-sectional and not
longitudinal, it is possible that girls’ conﬁdence may erode as they progress through
school, particularly when the enter middle school. This pattern would be consistent with
studies of mathematics self-efﬁcacy research (e.g., Hackett & Betz, 1989).

The lack of signiﬁcant differences in self-efficacy may be attributable to the
restriction of the age range of the sample (Hackett, 1995). Studies that are limited to those
of college age have already lost many students from the education "pipeline" (i.e., those
who have not pursued post-secondary education). Those who have already committed to
an academic major may not differ markedly on the variables of interest. For example, by
the time students elect a major in engineering, those with low self-efﬁcacy for this ﬁeld
have chosen not to enter it and are already out of the picture. Furthermore, college
samples tend to be biased toward a higher SES level (Hannah & Kahn, 1989). The need
to use samples other than college students has been acknowledged by researchers in this
ﬁeld (Fouad & Smith, 1996; Lent & Hackett, 1987; Taylor & Pompa, 1990). A study of

children in the upper elementary grades would extend this line of research.

Race and Ethnicity
A third consideration is the racial and ethnic composition of the sample.
Researchers believe that the social cognitive model may be useful for explaining the

career behaviors of ethnically diverse populations, but indicate that more research is

needed (Fouad & Smith, 1996; Hackett & Byars, 1996; Lent & Hackett, 1987). Lauver

75

and Jones (1991) studied an ethnically diverse sample of rural high school students. They
observed ethnic differences in range of perceived career options, in career self-efﬁcacy,
and in factors associated with these variables. Speciﬁcally, American Indian students (as
compared to White and Hispanic students) gave lower efﬁcacy estimates for seven of the
occupations studied. It is not clear, however, if efficacy beliefs for content-related
variables (i.e., occupational choices) would generalize to the career preparation process.
One of the problems with studies examining race/ethnicity as a variable is that it
is often confounded with SES. Recent statistics Show that Black and Hispanic children
were more than twice as likely to live in poverty (N CES, 1997). Alternately, if most of
the sample has a similar SES level, differences may not appear. Lauver and Jones (1991)
concluded that "the absence of an SES >< ethnicity interaction may be due to a comparable
SES distribution across ethnic groups" (p. 165). Therefore, while no speciﬁc hypotheses
regarding ethnic differences are advanced for this study, race/ethnicity remained a

variable of interest.

Summary
In this chapter, relevant literature related to middle childhood development and
children’s career development was reviewed. Children are ready for career preparation
learning experiences that take into account their developmental characteristics and needs.
When children process these experiences cognitively, they make judgments about their
abilities to be successful in the situations and tasks they encounter. The resulting self-

efﬁcacy beliefs may inﬂuence career outcomes.

76

The concept of self-efﬁcacy has received considerable attention in the career
development literature. Self-efﬁcacy has been found to play a role in a variety of speciﬁc
career-related behaviors. Recently a focus on the processes involved rather than the
content of decisions has enhanced the overall understanding of career development
Furthermore, measures have been developed based on self-efﬁcacy theory.

It is clear from this review that career preparation, choices, and decision making
happen in a social context. That context may present risks that hinder development or
opportunities that promote it. An examination of career preparation self-efﬁcacy can be
accomplished best by taking into account person variables, social context, and career

preparation learning experiences.

77

Chapter 3

METHODOLOGY

Sample Selection

The sample for this study was selected from third-, fourth-, and ﬁfth-grade classes
in eight elementary schools in a medium-sized Midwestern city. These schools were
chosen because they participate in a collaboration with the school district, the state’s land
grant university, state government, and the regional Chamber of Commerce. The goals of
this collaboration are to increase community engagement that will enhance academic
achievement and broaden career awareness (Keith, Perkins, Greer, Casey, & Ferrari,
1998)

There were 16 third-grade classes, 13 fourth-grade classes, and 13 ﬁfth-grade
classes in the eight schools. Multi-grade classes were excluded from the sampling frame.
A random sample of three classrooms was selected at each of these grade levels by using
a table of random numbers. Two of the eight schools each had two classrooms selected,
and one school of the eight did not have any classrooms selected. Therefore, the ﬁnal

sample consisted of nine classrooms drawn from seven schools.

Data Collection
Arranging Data Collection
Permission was obtained from the school district to administer the research

instrument in the selected classes (Appendix A). Permission was also obtained from the

78

university human subjects review committee (Appendix B). School principals were
contacted to inform them of the classes selected to participate in the study. Teachers were
then contacted to secure their cooperation and to schedule a convenient time for their
class to complete the survey. All teachers selected in the random sample agreed to
participate. A follow-up letter was sent to teachers to conﬁrm the details of their
participation (Appendix C).

The principals provided active consent for the students to participate. A letter
informing parents about the survey was signed by the school principal and sent home
with the students (Appendix D). Parents were instructed to return the slip attached to the
letter if they did not want their child to participate. Five parents declined to give
permission for their child’s participation. The data collection in classrooms occurred

during at two-week period in March and April of 1998.

Procedures for Data Collection

The study measures were combined into one survey called Careers and Me. The
survey was administered by the researcher to elementary students in each classroom. The
classroom teacher remained in the room during the survey administration; however, in
two classrooms a substitute teacher was present instead of the regular teacher. The
researcher, who was unknown to the students, established rapport with the students by
introducing herself as a university student, and emphasized the similarities in the work
done by students at both levels. She asked the students if they knew the meaning of
research, and discussed with them that it involved asking questions and ﬁnding out

information. She indicated that the topic that interested her was children in their age

79

group and what they did and what they thought about when preparing for their future in
the world of work.

LaGreca (1990) stressed the importance of carefully worded instructions as one
way to deal with the possibility of students giving socially desirable responses. The
researcher made sure to emphasize the difference between a survey and a test, stressing
that this was not a test, and therefore there were no right or wrong answers. She indicated
that she was interested in what they thought, and it did not matter if it was different from
other students in the class; they were to choose the best answer for them. Students were
assured that no one else would read their answers, and that their names would not be used
when information about the survey was shared. After the materials (the survey and a new
pencil, which the student could keep) were distributed, the researcher explained the
format of the questions to the group.

The researcher had the students complete sample questions to familiarize them
with the response format and with how to mark their answers. Initially, it was planned
that the entire survey would be read aloud to facilitate reading comprehension. However,
while third grade students needed assistance, it became apparent that older children could
read the survey on their own. Despite instructions to the contrary, students read ahead and
consequently were answering a different question than the one being read by the
researcher. This created confusion, therefore, after the instructions were explained and
they did the sample questions, fourth- and ﬁfth-grade students were given the opportunity
to complete the survey reading at their own pace. They were instructed to ask questions if
there was something they did not understand. The total survey took approximately 45 to

60 minutes to administer, depending on the grade level and the classroom setting. The

80

researcher’s introduction and instructions for administering the survey are contained in
Appendix E.

If time permitted after the students completed the survey, the researcher
concluded by discussing the remainder of the research process with them, again making
connections to tasks that both elementary and university students did, such as making
charts and graphs and revising and editing their work. This discussion occurred, at
varying levels, in eight of the nine classrooms. To thank teachers for their participation,
each teacher received a copy of a new career-related curriculum developed at Michigan

State University (Ferrari, 1997).

The Study’s Context
School District-Level Demographics

The school district from which the sample was drawn is a mid-sized central city
with a total population of approximately 135,000. According to statistics from the
Michigan Department of Education (1998), the median household income is $26,750.
There are 21,556 children enrolled in 41 public schools. Of these children, 56 percent are
Caucasian, 27 percent African American, 13 percent Hispanic, 2 percent Asian American,
1 percent Native American, and less than 1 percent other races. More than one quarter
(27.3 percent) of the children live in poverty, compared to the statewide rate of 17.7
percent. Thirty-one percent are eligible for the free lunch program, compared with 13
percent statewide.

The Metropolitan Achievement Tests (MAT; 7th edition) is the only standardized,

norm-referenced test given in the district. Average MAT scores in reading, math, and

81

language for the district’s elementary grades in 1997 were below the 50‘" percentile (43“,
47‘", and 42“d percentiles, respectively). However, two trends appear upon further
analysis. Students whose mothers reported higher education scored above the 50th
percentile, while those with a lower educational level scored below average. In addition,
this same gap occurs when scores are disaggregated by ethnicity. The average score for
Caucasian students is above average, while students of color scored below the 50th

percentile (Lansing School District, 1997).

School-Level Demographics
Educational Environment

According to a report by the National Center for Education Statistics (1997),
"social background factors such as limited English proﬁciency, family income, and
parental education are associated with various levels of education access and different
education outcomes" (p. 2). Several of these indicators were available at the school level
for the schools from which the sample classrooms were drawn. They are provided here to
give an understanding of the social and educational context in which this study occurred.
A comparison of the factors is presented in Table 1.

From the data presented in Table 1, it can be seen that there are differences in the
schools from which the sample was drawn. A wide range of socioeconomic status is
present, ranging from 33 percent to 85 percent of the students receiving free and reduced
price school lunch. Likewise, there were differences in the number of students with
limited English proﬁciency and those receiving special education services. These factors

combine with others to contribute to the social climate of the schools.

82

Table 1
Comparison of School Demographics

 

 

 

 

School Indicator‘
Enrollment
ID n Lunch Attend Retained LEP Special Ed
1 360 83% 62% 2% 11% 19%
2 322 33% 79% 3% 7% 12%
3 252 85% 59% 3% 19% 16%
4 478 56% 60% 1% 4% 2%
5 324 72% 58% 5% 1% 14%
6 212 71% 72% 2% 2% 24%
7 293 74% 58% 5% 7% 19%
TOTA 2241

L

 

Note. Demographics are for] 996-97, the most recent school year available.

aLunch = % receiving free and reduced price school lunch; Attend = % absent 10 days
or fewer, 2“d semester; Retained = % retained at grade level; LEP = % limited English
proﬁciency; Spec Ed = % classiﬁed as special education.

Source: School Success Card, Lansing School District, Ofﬁce of Research,
Evaluation, and Pupil Accounting, March 1998 (one report for each school).

Achievement

The students in the schools in this sample had a wide range of scores on the MAT.
The mean scores were below the 50‘" percentile for reading (M = 46.0), math (M = 44.5),
and language (M = 39.0). These average percentile scores are slightly higher in reading,
but lower in math and language than the school district averages for the elementary
grades (Lansing School District, 1997).

The other achievement scores available at the school level are the Michigan

83

Educational Assessment Program (MEAP), a set of criterion-referenced tests in several
subject areas. The MEAP tests measure essential skills in reading, mathematics, writing
and science. The state requires all Michigan public schools to administer these tests each
year; these tests are scheduled in April of each year. Reading and mathematics are tested
at grades 4 and 7; writing and science are tested at grades 5 and 8. Therefore, data
presented in Table 2 are not available for all grades in the study sample, and they were
available only for the previous school year. They are presented here to provide a more

complete description of the educational context at the school level.

Table 2
Comparison of Satisfactory Test Scores on Michigan Educational Assessment
Program MEAP) Tests for Schools in Sample

 

 

 

 

Schools MEAP Tests - Satisfactory Scores
ID Enrollment Math Science Reading Reading Writing
n (Story) (Information)
1 360 30.0% 27.5% 33.0% 10.0% 78.4%
2 322 45.0% 30.0% 63.3% 39.0% 88.9%
3 252 17.6% 14.3% 73.5% 23.5% 47.1%
4 478 45.3% 26.3% 82.2% 42.2% 84.2%
5 324 16.7% 2.1% 46.8% 10.9% 41.3%
6 212 37.0% 23.1% 55.6% 29.6% 80.8%
7 293 22.6% 5.1% 58.1% 16.1% 73.7%

TOTAL 2241

Note. MEAP tests given at following elementary grade levels - reading and math:

grade 4, science and writing: grade 5.
Source: School Success Card, Lansing School District, Office of Research,
Evaluation, and Pupil Accounting, March 1998 (one report for each school).

84

The Study’s Participants

A total of 167 students from nine classrooms in the third, fourth, and ﬁfth grades
completed the survey. This sample represented all but a few of the students enrolled in
the selected classrooms. A total of 20 students did not complete the survey for one of the
following reasons: absent (n = 2), teacher’s recommendation that the student could not
attend to and complete the survey accurately (n = 4), or parent refused participation (n =
5); the remainder were not present in the classroom during the time the survey was
administered (n = 9). Each grade level accounted for approximately one third of the
sample, with 29.9 percent in third grade (n = 50), 32.4 percent in fourth grade (n = 54),

and 37.8 percent in ﬁfth grade (n = 63; see Table 3).

Description of Sample
Age
The children ranged from eight (8 years, 4 months) to thirteen years old (13 years,
0 months), with a mean age of 10.2 years (SD = 11.6 months). The mean age by grade

level and classroom is reported in Table 3.

Gender

The sample was almost equally divided by gender, with 48 percent girls and 52
percent boys. The sample’s gender composition by grade and classroom is reported in
Table 3. A chi-square test revealed no signiﬁcant difference for gender by grade level, )8
(2, 167) = 0.640, p = .73, nor were there gender differences by classroom, x2 (8, 167) =

4.78, p = .78.

85

Race/Ethnicity

In the current sample, 41.8 percent of the students were Caucasian (n = 69), 40.0
percent were African American (n = 66), 13.9 percent were Hispanic (n = 23), and 4.2
percent were Asian American (n = 7; see Figure 7). There were no Native American

students in the sample. Data were missing for two students.

 
  
   
   
 

. Am ri n
Caucasran . e ca

41.8% 400%

 

Asian American . .
Hispanlc
4.2%
13.9%

 

Figure 7 Racial/Ethnic Composition of Sample
Note. N = 165

86

An examination of the racial/ethnic composition of the schools from which the
sample classrooms were drawn shows different student proﬁles (Table 4). Native
American students constituted the smallest percentage of the population in all schools,
with three schools having no Native American students. In some schools Caucasian
students were the majority (e.g., 62% in School 2), while in others they are the minority
(e.g., 28% in School 6). Several schools had approximately equal percentages of African
American and Caucasian students. Despite these school-level differences, the
racial/ethnic composition of the sample was almost identical to the average percentages
for the seven schools included in the sample. These schools, and the sample, had a higher
percentage of African American students than the school district average.

A comparison of the racial/ethnic composition of the classrooms is reported in
Table 5. While all classrooms had a combination of students who are Aﬁican American,

Hispanic, and Caucasian, only three of the nine classrooms had Asian American students.

87

Table 3

Characteristics of Study Participanm

 

 

 

 

 

 

 

 

Participants
% of Age Gender
Grade and Class n Total M (SD) Girls Boys
n % n %

3" grade
Classroom A 16 9.5% 9.1 (0.48) 8 50.0% 8 50.0%
Classroom B 14 8.4% 9.1 (0.55) 6 42.9% 8 57.1%
Classroom C 20 12.0% 9.2 (0.54) 10 50.0% 10 50.0%
3rd grade total 50 29.9% 9.1 (0.52) 24 48.0% 26 52.0%

4'" grade
Classroom D 17 10.2% 10.3 (0.51) 8 47.1% 9 52.9%
Classroom E 19 11.4% 10.1 (0.48) 10 52.6% 9 47.4%
Classroom F 18 10.8% 10.0 (0.28) 10 55.6% 8 44.4%
4th grade total 54 32.4% 10.1 (0.44) 28 51.9% 26 48.1%

5"I grade
Classroom G 20 12.0% 11.2 (0.50) 6 30.0% 14 70.0%
Classroom H 18 10.8% 11.3 (0.55) 11 61.1% 7 38.9%
Classroom 1 25 15.0% 11.0 (0.56) 11 44.0% 14 56.0%
5"I grade total 63 37.8% 11.2 (0.54) 28 44.4% 35 55.6%
TOTAL 167 100.0% 10.2 (0.97) 80 100.0% 87 100.0%

 

88

Table 4

Race/Ethnicity Composition of Sample and Comparison Among Schools in Sample

 

 

 

 

 

 

 

 

 

 

 

 

 

Race/Ethnicity
Native African Asian
American American American Hispanic Caucasian Total
Sample
N 0 66 7 23 69 165
% 0.0% 40.0% 4.2% 13.9% 41.8% 100%
Total of Schools in Sample
N
% 0.9% 40.9% 3.4% 14.6% 40.6% 100%
Comparisons Among Sample Schools 3
School 1 (n = 360)
% 2.0% 37.0% 2.0% 19.0% 39.0%
School 2 (n = 322)
% 0.0% 26.0% 4.0% 8.0% 62.0%
School 3 (n = 252)
% 1.0% 33.0% 6.0% 33.0% 28.0%
School 4 (n = 478)
% 0.0% 35.0% 2.0% 10.0% 54.0%
School 5 (n =324)
% 0.0% 49.0% 8.0% 10.0% 33.0%
School 6 (n = 212)
% 1.0% 64.0% 1.0% 7.0% 28.0%
School 7 (n = 293)
% 2.0% 42.0% 1.0% 15.0% 40.0%

 

“Source: School Success Card, Lansing School District, Office of Research,
Evaluation, and Pupil Accounting, March 1998 (one report for each school). Data
reported for schools are for 1996-97, the most recent school year available.

89

Table 5

Racial/Ethnic Composition of Classrooms

 

 

 

 

 

 

 

Race/Ethnicity
Grade & African Asian
Classroom American American Hispanic Caucasian Total
Number of Students

3rd grade
Classroom A 10 0 l 4 15
Classroom B 6 2 3 2 13
Classroom C 10 0 6 4 20

3rd grade total 26 2 10 10 48
4“I grade
Classroom D 7 3 1 6 17
Classroom E 6 0 2 11 19
Classroom F 2 0 1 15 18

4‘" grade total 15 3 4 32 54
5‘" grade
Classroom G 7 2 2 9 20
Classroom H 7 0 5 6 18
Classroom I 11 0 2 12 25

5'" grade total 25 2 9 27 63
TOTAL 66 7 23 69 165

90

Mother ’s Reported Level of Education

Mother’s level of education was used as a measure of socioeconomic status; other
indicators of income were not available for individual students. Data on mother’s self-
reported education level were available for 82 percent of the sample (n = 137).
Approximately three quarters (76.6 percent) of the mothers who reported their education
level had at least a high school diploma. Mother’s education level of the sample as
reported to the school district is summarized in Table 6. For those children whose records
did not contain mother’s education level, some had refused to indicate and some mothers

were not in the household.

 

 

 

Table 6
Mother’s Reported Level of Education

Level of Education n Percent
Never attended school 1 0.7%
Completed elementary school 2 1.5%
Completed junior high 4 2.9%
Attended high school 25 18.3%
Graduated high school 54 39.4%
Attended college 32 23.4%
Graduated college 18 13.1%
Post graduate 1 0.7%
TOTAL3 137 100.0%

 

aRefused to indicate or missing data (n = 30)
Source: Lansing School District, Ofﬁce of Research and
Evaluation Services

91

Mother’s education data were recorded in seven categories. Because some
categories had only a small number of cases, data were aggregated to create meaningful
categories to facilitate description of the sample and subsequent data analysis. When the
data were collapsed into four categories, there were 23.4 percent who had not graduated
from high school, 39.4 percent who were high school graduates, and 23.4 percent who
had attended some college, and 13.9 who had graduated from college, including those

who had done post graduate studies (Figure 8).

 

 

 

     

100

90 1

80 ul

70 a

60 I

50 -
.03 4o .
c
or
5 so .
(I)
B 20 q
‘r‘.
2 10 u
(I? 1

high school or below some college
high school grad. college grad or more

Mother's Reported Education Level

Figure 8 Mother’s Education Level

92

A range of educational levels was represented in the nine classrooms (Table 7).
They ranged from a classroom where 6.7 percent of the mothers had less than a high
school education (Classroom F) to a classroom with 50 percent at this education level
(Classroom C). Two classrooms had no students whose mothers were college graduates
(Classrooms D and C). In comparison, one classroom (Classroom F) had one third at this

education level.

Table 7
Comparison of Mother ’s Education Level by Classroom

 

Education Level

 

 

 

 

 

 

Did not complete High school Some College
Classroom high school graduate college graduate
3" grade
A (n = 14) 28.6% 21.4% 28.6% 21.4%
B (n = 10) 30.0% 30.0% 10.0% 30.0%
C (n = 14) 50.0% 28.6% 21.4% 0.0%
4‘" grade
D (n = 13) 23.1% 46.2% 30.8% 0.0%
E (n = 18) 27.8% 33.3% 22.2% 16.7%
F (n = 15) 6.7% 20.0% 40.0% 33.3%
5‘" grade
G (n = 16) 25.0% 50.0% 6.3% 18.8%
H(n=14) 14.3% 57.1% 21.4% 7.1%
I (n = 23) 13.0% 56.5% 26.1% 4.3%
TOTAL (n = 137) 23.4% 39.4% 23.4% 13.9%

 

93

Because race and socioeconomic status (in this case, represented by mother’s

education) are often confounded, this relationship was examined (see Table 8 and Figure

9). This analysis revealed that both African American and Caucasian students had

mothers in each of the four education categories. None of the mothers of Hispanic

children had graduated from college. Furthermore, for all the Asian American students in

the sample for whom data were available (n = 3), their mothers’ education level was

below high school. Therefore it is impossible to disentangle the effects of race and

mother’s education for these students. Based on this ﬁnding, the decision was made to

drop the Asian students from the analysis.

Table 8
Comparison of Mother ’s Education Level by Race/Ethnicity

 

Education Level

 

 

 

Did not complete High school Some College Total
Race/Ethnicity high school graduate college graduate n
African American 23.2% 33.9% 32.1% 10.7% 56
Asian American 100.0% 0.0% 0.0% 0.0% 3
Hispanic 23.5% 70.6% 5.9% 0.0% 17
Caucasian 19.7% 37.7% 21.3% 21 .3% 61
TOTAL
n 32 54 32 19 137
% 23.4% 39.4% 23.4% 13.9% 100%

 

Note. Missing data, n = 30

Did not complete high school = includes never attended school, completed

elementary school, completed junior high, and attended high school but did not

graduate; College grad = includes college graduates and those attending or

completing post-graduate education

94

 

 

 

 

100

80 1

60 I
.9 4o .
c
or
U
3
(D
"5 20 1
E
i
O. O u :r:-, .41;

African American Hispanic
Asian American Caucasian
Race/Ethnicity

 

Mother's Education

.high school or below
Dhigh school graduate

[jsome college
.college grad or more

Figure 9 Comparison of Mother’s Education by Race/Ethnicity

Note. N= 137

Job/Career Preference

Description of Career-Related Variables

At the beginning of the Careers and Me survey, students were asked to indicate a

job or career they thought they might like to do. Students named a total of 48 different

jobs. The job most often listed was teacher (n = 23), followed by basketball player (n =

18), football player (n = 17), and doctor (n = 11). Some choices were mentioned by only

one student (e.g., newscaster, ﬂorist, oceanographer, insurance). Some choices were very

95

general (e. g., artist, doctor, truck driver) while others were quite speciﬁc (e.g., cartoonist,
emergency room doctor, cement truck driver). The jobs were coded using the nine
categories in the Occupational Outlook Handbook of the Bureau of Labor Statistics
(1998), with the addition of a category for professional sports. More than 85 percent of
students’ job preferences fell into one of three categories. The majority of the jobs
selected by the students were in the professional/technical category (48.8 percent).
Professional sports was the second highest category (25.6 percent), while service jobs
accounted for 12.8 percent of the jobs selected. The remaining categories each had a very
small percentage. Therefore, to facilitate data analysis, the categories were collapsed into
four categories (see Table 9). The executive/administrative/managerial category was
combined with the professional/technical; the resulting category accounted for 51.2
percent of the jobs. The second highest category, professional sports, was maintained
(25.6 percent). The combination of sales, support, and service classiﬁcations now Data
reported for schools are for the 1996-97 school year accounted for 16.2 percent of the
jobs. The jobs in the construction trades, production, transportation, and military
categories were combined into one called building and producing, which accounted for
6.2 percent of the jobs.

After the categories were collapsed, job preferences were examined by gender
(Figure 10). There were distinct preferences corresponding to gender stereotypes without
much overlap. Girls were most represented in the professional/technical/managerial
category—more than two thirds (69 percent) of the students in this category were girls.
Within the professional/technical jobs, girls more often chose teacher, entertainer (e. g.,

singer), doctor, veterinarian, and lawyer than boys. Boys also indicated these preferences,

96

but they did not outnumber girls in any job. Very few students indicated an interest in
science and engineering (n = 5). Boys dominated the professional sports category (85.7
percent). The sales and service category was almost equally split between boys and girls.
However, boys in this category chose jobs like ﬁreﬁghter and police ofﬁcer, while girls
chose hairdresser and day care. Very few students indicated an interest in jobs in the

building and producing category; the vast majority were boys (90.9 percent).

Table 9
Summary of Student Job Preferences

 

Gender

 

Female Male Total Sample

 

Job Category‘ n % n % n %

 

Professional/technical/managerial 58 73 .4 26 30.6 84 5 1 .2

Professional sports 6 7.6 36 42.4 42 25.6
Sales, service, & support 14 17.7 13 15.3 27 16.5
Building & producing 1 1.3 10 11.8 11 6.7
Total 79 100.0 85 100.0 164 100.0

 

aBased on categories in Occupational Outlook Handbook (Bureau of Labor Statistics,
1998), with the addition of a category for professional sports.

97

 

   

 

 

100

80 I

60 -
w
E 40 -
a)
'c
E!
‘0 Gender
“5 20 .
5 - female
0
L
C‘i.’ 0 . W . ‘ male

prof/tech/managerial sales/service
sports building/producing

Job/Career Preference

Figure 10 Job/Career Preferences by Gender

Description 01' Measures Used in the Analyses: Person, Social Context, and Career-
Related Variables

In the following sections, person, social context, and career-related variables are
operationalized by describing the method of measurement and coding for each variable.

The data for this study were collected using the Careers and Me survey (Appendix F).

98

Person Variables
Three person variables were included in the study. The Careers and Me survey
provided information gender and age. Student records were used to verify this
information and to obtain race/ethnicity data. The deﬁnitions, measurement, and coding

of these variables is summarized in Table 10.

Gender
Children were asked to indicate if they were a girl or a boy. Girls were coded 0

and boys were coded 1.

Age
Age was veriﬁed by checking the survey response against students’ school
records. In order to create a continuous variable, age in years and months was calculated

from the child’s birthday.

Race/Ethnicity
Information on the child’s race/ethnicity was obtained from existing school
records. Data were available in ﬁve categories that were coded 1 through 5: 1 (Native

American), 2 (Aﬁican American), 3 (Asian American), 4 (Hispanic), and 5 (Caucasian).

99

Table 10

Deﬁnitions, Measurement, and Coding of Person and Social Context Variables

 

 

 

 

 

Variable Definition Measurement Coding
Person Variables
Age Chronological age Calculated from child’s Age recorded as year
birthday obtained from and months
school records
Gender Whether a person is As reported on Careers 0=Female
male or female and Me survey, P. 1, 1=Male
Item 2
Race/Ethnicity The racial or ethnic AS reported on child’s 1=Native American
group to which a school records 2=Aﬁican American
person belongs 3=Asian American
4=Hispanic
5=Caucasian
Social Context Variables
Grade Level of placement in As reported on Careers 3 = Grade 3
an educational and Me survey, P. 1, 4 = Grade 4
institution Item 5 5 = Grade 5
Classroom Physical setting Coded by researcher 1-9
where leaming takes during data collection
place within a school
as well as the social
context of the people
in this setting
(students and teacher)
and their interactions
Mother’s The number of years Mother’s reported 0=never attended
Education of schooling that the education level as 1=completed
child’s mother has reported in school elementary
completed records 2=completed jr. high
3=attended high
While this variable is school

measured at the
individual level, it is
included to represent
a context of
development.

4=high school

graduate

5=attcnded college
=college graduate

7=post graduate

8=refused to indicate

99=missing data

 

100

Social Context Variables
In addition to career exposure discussed later in this chapter, the social context
was measured by the school environment and socioeconomic status. These variables are

summarized in Table 10.

School Environment

Measurement of the school environment was accomplished with two categorical
variables: grade and classroom. Students checked their grade (3“, 4‘", or 5’") on the
Careers and Me survey. Each classroom surveyed was coded one through nine at the time
of data collection.

Variables measured in such a way are generally considered “social address”
variables because they indicate where a person is but not the processes that occur in those
setting (Bronfenbrenner, 1979). However, because the purpose of this study was
exploratory, these variables were included to determine if grade or classroom relationship
could be detected. If signiﬁcant classroom differences were found, they would warrant

further investigation.

Mother’s Reported Level of Education

Mother’s reported education level was used to represent socioeconomic status.
The data were available from school records in eight categories: 1 (never attended
school), 2 (completed elementary school), 3 (completed junior high), 4 (attended high
school), 5 (high school graduate), 6 (attended college), 7 (college graduate), and 8 (post

graduate). Because of small cell sizes for some of the categories, data were collapsed into

101

four categories to facilitate data analysis. Because categories 1 through 4 represented
those mothers who did not have a high school diploma, they were combined into one
category. College graduates and those who had education at the post graduate level were
combined into another category. The categories high school graduate and attended

college remained the same as the initial coding.

Career-Related Measures

The career-related data for this study were collected using the Careers and Me
survey (Appendix F). This survey contained the Career Exposure Index (CEI), the Career
Preparation Learning Experiences Checklist (CPLE), and the Career Preparation Self-
Efﬁcacy Scale (CPSE) to measure career-related variables. Two variables, interest in
career preparation learning experiences and job preference, were included for descriptive
purposes only and were not used in the multivariate analyses.

The development of the career-related measures was an iterative process that took
place over a three-year period. It included not only an extensive review of the literature,
but also meetings with elementary school teachers and youth development specialists;
focus groups with youth and adults (Ferrari, 1996b); workshops with elementary school
teachers (e.g., Ferrari, 1996a); creating, implementing, and evaluating educational
programs with elementary school students (Ferrari, 1997; Ferrari & Farrell, 1997), and
pilot testing. The development of these measures is described in detail in the following

section.

102

Descriptive Variables

A summary of the descriptive career variables that includes their deﬁnitions,

measurement, and coding is presented in Table 11.

 

 

Table 11
Deﬁnitions, Measurement, and Coding of Descriptive Career Variables
Variable Definition Measurement Coding
Job/career The name of the As reported on 1 =executive/
preference job or career that Careers and Me administrative/managerial

a person indicates
a desire to do

survey, P. 1, Item 6
( is a job or

2 =professional/technical
3 = marketing & sales

 

career I think I might 4 = administrative
like to do.) support/clerical
5 = service
6 = construction trades
7 = production
8 = transportation
9 = military
10 = sports
Interest in Whether a person As reported on 0=Not like
career expresses interest Careers and Me 1=Like
preparation in an activity, survey, Part 1, Items
learning regardless of 1-15 Possible scores = 0 - 15
experiences actual (Is this something you
participation would like to do?)
Sum of responses to
Items 1-15, column 2
Job Preference

Students were asked to write in the name of a job or career that "I think I might

like to do." F orty-eight different jobs were listed. These jobs were coded using the nine

categories in the Occupational Outlook Handbook (BLS, 1998). Because 42 students

103

listed one of eight different sports as a preference, a tenth category of professional sports
was added. To facilitate data analysis, categories were collapsed and data were coded 1
(professional/technicaI/managerial), 2 (professional sports), 3 (sales/support/service),

and 4 (building/producing).

Interest in Career Preparation Learning Experiences

To assess interest in career preparation learning experiences, students indicated
whether or not they would like to do each of the 15 career preparation activities on the
CPLE, regardless of their actual participation‘. The positive responses to each item were
summed and converted to a percent to obtain a total score for each career preparation
learning experience. A total interest score for each child was also computed by summing
positive responses across the 15 learning experiences. The range of possible scores for

interest is 0 to 15, with a higher score indicating interest in more leaming experiences.

Career-Related Variables
A summary of the career-related study variables that includes their deﬁnitions,

measurement, and coding is presented in Table 12.

Career Exposure Index
The career exposure measure was created for this study and incorporated into the

Careers and Me survey (Appendix F). First, children were asked to identify a job that

 

Description of these 15 activities is included later in this chapter under the
heading for the Career Preparation Learning Experiences Checklist

104

Table 12

Deﬁnitions, Measurement, and Coding of Career-Related Study Variables

 

 

 

 

Variable Definition Measurement Coding
Career Observations of people AS reported on Careers 0=No
exposure and events in everyday and Me survey, P. 1 1=Yes
life which indirectly (Response is made in
inﬂuence career related to job
choices and behaviors preference indicated.)
Family Item 9
Community Item 10
TV Item 11
Reading Item 12
Total Sum of responses to Possible scores = 0
exposure items 9-12 - 4
Participation in Direct involvement in As reported on Careers 0=No
career activities that offer and Me survey, Part 1, 1=Yes
preparation information, advice, Items 1-15
learning and experience relative
experiences to understanding of Sum of responses to Possible scores = 0 -
onself and careers that Items 1-15, column 1 15
can be applied to career
decision making.
Includes the objects,
places, events, and
people that comprise
them.
Career Outcome Variable
Career Beliefs in one’s ability As reported on Careers 1=I’m sure I can’t
preparation to engage in career and Me survey, Part 2 2=I’m pretty sure I
self-efﬁcacy preparation activities (How sure are you that can’t
that foster awareness, you can do the 3=Maybe I can
exploration, and following. . .) 4=I’m pretty sure I
attainment of can
Awareness educational milestones Items 1-2, 5-9, 22-25 5=I’m sure I can
Exploration Items 10-19
Educational Items 3-4, 20-21, 26-27
Milestones
Total score Sum of responses to Total score = sum
Items 1-27 items 1-27 and
divide by 27;
possible scores 1.00
- 5.00

 

105

they would like to have. Then students responded to four items related to this job
preference. They were asked to indicate if they knew about this job because it was one
held by: (a) a family member; (b) a member of their community, neighborhood, or school;
(c) someone they read about; or (d) someone on TV. They answered each question with
either a yes or no response. The positive responses to these four questions were summed

to obtain a total exposure score. Possible scores ranged from 0 to 4.

Career Preparation Learning Experiences Checklist

A review of literature did not uncover any existing measures of career-related
learning experiences. Therefore the items were generated by the researcher through an
iterative process described at the beginning of the chapter.

Among the recommended activities included those that provided the opportunity
to develop self-awareness of interests and abilities, to actively explore the work
environment, to engage directly with workers, to participate in actual work tasks, and to
actively acquire information about different careers and their skill and educational
requirements (Drummond & Ryan, 1995; Michigan Department of Education, 1996;
NOICC, 1989; School-to-Work Learning & Information Center, 1996; Seligman, 1980).
Fifteen different items were developed (see Table 13). Review by youth development
specialists established face validity. The resulting checklist is also included in Appendix
F as part of the Careers and Me survey.

To assess participation in preparation learning experiences, students were asked to
indicate whether or not they had done the activity. By computing the percent who

indicated they had done the activity, the degree of participation was assessed for each

106

individual career preparation learning experience. Additionally, the afﬁrmative responses
were summed to obtain a career preparation learning experiences score for each student.
The range of possible scores is 0 to 15, with a higher score indicating participation in a

greater number of learning experiences.

Table 13
Career Preparation Learning Experiences

 

Do a job or chore at home.

Do a job in your classroom or school.

Do community service or a volunteer job in your community.
Make a list of things you are good at and like to do.

Make a list of things you would like to learn or improve.

Talk to a parent or teacher about a job you think you’d like to do.
Hear a guest speaker or talk to people about their job.

Go on a ﬁeld trip (to study about jobs) or visit a workplace (to
see someone work at their job).

Go to the library to ﬁnd out information about a job or career.
Read a book about what people do for their job.

Find out what you would have to do (or the skills you would
need) to get a job that interests you.

Learn about the subjects you would have to study in school for a
job that interests you.

Do a report or project to learn more about a job that you might
like to do.

Go to an after-school activity that helped you learn about jobs or
job skills.

Visit a college campus.

 

107

Career Preparation Self-Efficacy Scale

The Career Preparation Self-Efficacy Scale (CPSE) was developed by the
researcher for this study because no existing measures for elementary-age students could
be located. The CPSE was designed to measure students’ beliefs about their ability to
engage in career preparation activities, encompassing activities that foster awareness,
career exploration, and attainment of educational milestones.

Development. Development of the CPSE followed an iterative process described
earlier in the chapter. Because children’s career development has not received much
attention in the literature, an attempt to derive developmentally appropriate career
preparation tasks depended on related literature and existing practices. Determining
developmentally appropriate tasks was a necessary ﬁrst step in developing a domain-
speciﬁc measure of self-efﬁcacy.

A review of the literature on developmentally appropriate competencies and
activities for elementary students suggested areas to include in measures of career
preparation (Drummond & Ryan, 1995; Michigan Department of Education, 1996;
NOICC, 1989; Seligman, 1980). A variety of activities has been suggested as appropriate
for elementary age children; these were reviewed in Chapter 2.

Based on this review, three concepts were included as part of career preparation

self-efﬁcacy: (a) awareness (b) career exploration (c) educational milestones. Career

preparation tasks related to awareness involve examination of knowledge, values,
interests and skills, and goals (Sears, 1982), and includes awareness of societal issues that
inﬂuence career choices and decisions. Career exploration tasks are those that generate

information about possible career choices. Although exploration implies some sort of

108

active engagement, these tasks may be viewed in two ways: as primarily informational
(i.e., acquiring knowledge related to careers) and as more experiential in nature (i.e.,
involving direct interaction with people). Educational milestones are those tasks or
accomplishments that indicate academic success.

The emphasis for younger children is on exposure to and consideration of a wide

variety of options. Acquiring information and ideas, about oneself and the career

environment, is more important than decision making at this age. Therefore, activities

that are appropriate in measures designed for older students are not relevant for younger
ones. These include tasks such as narrowing of options resulting in a decision (e.g.,
selecting a college major), and pursuit of particular jobs (e.g., successfully managing the
job interview process). Some activities would have no meaning for children as they are
not related to their everyday experiences (e.g., get letters of recommendation from your
professors, go back to school to get a graduate degree after being out of school 5-10
years). Blustein (1988) has argued that a vocationally mature person is not necessarily one
who has a high level of career decidedness. That is, degree of decidedness may not be the
best way to characterize someone who is vocationally mature. Having made a decision
does not mean that it is a good one or that it was done while considering alternative
courses of action. Likewise, it is also possible to have different a conception of vocational
maturity for each age group. Therefore, ﬁnal decision making was not emphasized in this

measure.

Locating and adapting existing measures. Another important step in the

development process was to locate and review items from existing scales. The existing

measures that served as the starting point for this effort included career decision-making

109

self-efﬁcacy (Taylor & Betz, 1983; F ouad et al., 1997), career search self-efficacy
(Solberg, Good, Nord et al., 1994), and academic milestones (Lent et al., 1986). Each of
thee self-efﬁcacy measures is described below. Subsequently, the process consisted of
retaining those items that were appropriate in their existing form, modifying the language
to make items appropriate if necessary, eliminating those items that were not relevant,
and creating new items. The items adapted from existing measures were assigned to one

of the CPSE subscales (Awareness, Exploration, and Educational Milestones). Following

the description of existing measures, the modiﬁcations that were made to create the CPSE

are described.

1. Career Decision Making Self-Efficacy - The Career Decision Making
Self-Efﬁcacy Scale (CDMSE) is a 50-item scale consisting of ﬁve
subscales. Responses are scored on a 10-point scale ranging from 0 (no
conﬁdence) to 9 (complete conﬁdence). Taylor and Betz (1983) reported
an internal consistency reliability coefficient of 0.97 for the total scale.
Luzzo (1993) reported an internal consistency reliability of 0.93 and a test-
retest reliability of 0.83 for a community college sample. Luzzo’s study
also gave support to the construct validity of the scale. An examination of
the items included on the CDMSE indicated that the concept of decision
making used in this instrument may not appropriately address the career
preparation tasks of elementary school children. Three items from the
original CDMSE (Taylor & Betz, 1983) were included with revisions

(Table 14).

110

The CDMSE was further adapted for middle school students by
Fouad and her colleagues (Fouad et al., 1997). Twelve items from the
original 50-item scale were retained, with adjustments made for
comprehension. They reported an internal consistency reliability of 0.79
for their sample which was predominantly Hispanic and Aﬁican American
students of lower socioeconomic status.

Further adaptation of Fouad and her colleagues’ (1997) version of
the CDMSE instrument was necessary to ensure that it contained questions
appropriate to elementary students rather than those that were relevant
only to older students. All 12 items from the revised CDMSE were
retained, either in their original form (6 items), or with modiﬁcations in

wording (6 items; see Table 15).

Table 14
Modiﬁcations to Existing Self-Efﬁcacy Measures: Career Decision-Making

Career Decision-Making Self-Efﬁcacy"

 

 

Original Items Language Modiﬁcations Subscale”

Choose a career that your Choose a career that you think is right
parents do not approve of for you no matter what others say. A
Accurately assess your abilities. Figure out what you are good at doing. A
Get involved in a work Get some real experience, like
experience relevant to your volunteering or community service, E
future goals. that will help you get some skills or

decide if you like a particular kind of

job.

 

Note. Italics indicate changes in wording.

“Taylor & Betz, I983

bSubscale as assigned in new measure: A = Awareness; E= Exploration; EM =
Educational Milestones

lll

Table 15

Modiﬁcations to Existing Self-Efﬁcacy Measures: Career Decision-Making -

Middle School

 

Career Decision Making Self-Efficacy - Middle School"

 

 

 

 

Original Items Language Modiﬁcations Subscaleb
Find information in the library Find information in the library about E
about ﬁve occupations I am a job or career you are interested in.
interested in.
Determine what occupation would Figure out what job or career would A
be best for me. be best for you.
Resist attempts of parents or friends Stick to career choice even if others A
to push me into a career I believe is did not approve of it.
not right for me.
Choose a career in which most of Choose a career in which most of the A
the workers are the opposite sex. workers are men.
Choose a career in which most of the
workers are women.
Find out the average salary of Find out how much money people E
people in an occupation. make in a job or career.
Talk with a person already Talk with a person who already E
employed in a ﬁeldl am interested works in the type of job or career
in. you are interested in.
Unchanged Items Subscale
Make a plan of my educational goals for the next three years. A
Select one occupation from a list of possible occupations I am A
considering.
Decide what I value most in an occupation. A
Describe the job skills of a career I might like to enter. A
Choose a career that will ﬁt my interests. A
Decide what kind of schooling I will need to achieve my career goal. A

 

Note. Italics indicate changes in wording.

’ Fouad, Smith, & Enochs, 1997

t’Subscale as assigned in new measure: A = Awareness; E= Exploration; EM =

Educational Milestones

112

Career Search Self-Efﬁcacy - The 35-item Career Search Efficacy Scale
(CSES) was developed by Solberg and his colleagues (Solberg, Good,
Nord et al., 1994) to "assess the degree of conﬁdence a person has for
performing a variety of career search tasks" (p. 113). Factor analysis
produced four subscales: job search efﬁcacy, personal exploration
efﬁcacy, interviewing, and networking. The internal consistency reliability
was 0.97 for the full scale, with alphas for the subscales of 0.87 (personal
exploration) and 0.95 (job search), 0.91 (interviewing), and 0.92
(networking). Analysis also shows that this measure has convergent and
discriminant validity.

Items from two of the four CSES subscales, job search efﬁcacy and
personal exploration efficacy subscales, were judged to be appropriate for
an elementary-level population. Items from the remaining two factors,
interviewing and networking, contained statements related to the formal
aspects of ﬁnding employment such as preparing for interviews and
identifying potential employers. These were deemed to be beyond the
scope of an elementary student’s experience and therefore were not
appropriate for inclusion in this study. To create appropriate items,
statements from the CSES could be modiﬁed so an elementary student
could understand them (e.g., "achieve a satisfying career" could be
changed to " get a good job" and "identify and evaluate your personal
capabilities" could become "ﬁgure out what things I am good at doing").

The item modiﬁcations made to the CSES are summarized in Table 16.

113

Table 16

Modiﬁcations to Existing Self-Eﬁicacy Measures: Career Search

 

Career Search Self-Eff'icacy‘I

 

 

Original Items Language Modiﬁcations Subscaleb
Identify your work skills. Figure out what you are good at A
Identify and evaluate your personal doing.
capabilities.
Identify and evaluate your career Figure out what job or career A
preferences. would be best for you.
Meet new people in careers of interest. Talk with a person who already E
works in the type of job or career
you are interested in.
Achieve a satisfying career. Find a good job. EM

 

Note. Italics indicate changes in wording.

aSolberg, Good, Nord et al., 1994

bSubscale as assigned in new measure; A = Awareness, E= Exploration, EM =
Educational Milestones

Academic Milestones - The idea for an Educational Milestones measure
was adapted from a study by Lent et al. (1986) of college undergraduates
who were considering majors in science and engineering. Their goal was
to create a more task-speciﬁc measure than had been developed
previously. The original measure asked students to rate their ability to
perform 11 speciﬁc accomplishments critical to academic success in
science and engineering (e.g., complete the mathematics requirements for
most engineering majors). Confidence to attain these milestones was
measured on a lO-point scale. The coefﬁcient alpha for this scale was
0.89. While items of this speciﬁcity were not appropriate to this study, the

concept of educational milestones was relevant. Items were adapted from

114

others measures and several new ones were created (see Table 16 and

Table 17).

Several new items were created to supplement existing ones. New items created

for this study and their location in the subscales are reported in Table 17.

 

 

Table 17
Items Created for Career Preparation Self-Efficacy Measure
Subscale New Item
Awareness
Figure out what things you are interested in.
Exploration

Find someone who can help you think about what to do with your
future.

Visit a workplace of a person who has a job or career that interests
you.

Use the computer to ﬁnd out information about a job or career you
are interested in.

Educational Milestones

Study hard for your classes.

Get good grades.

Take math in high school.

Graduate from high school.

Go to college or other education beyond high school.

 

Testing. Earlier versions of the CPSE measure were ﬁeld tested in two elementary

school classrooms. A pilot version with three levels of responses did not produce

sufficient variability. Further adaptation of the measure for elementary students included

modifying the number and wording Of possible responses. The researcher found that

115

reading the questions and responses aloud and using color coding associated with the
responses aided data collection. This allowed the researcher to associate the response with
the symbol when giving directions for completing the measure (i.e., "Check the green
square if you are sure you can . . ."). However, the cost of color photocopying was
prohibitive for a larger sample, so different symbols to anchor each response were used
instead.

Final version. The ﬁnal version of the Career Preparation Self-Efficacy (CPSE)
measure consisted of 27 items (Appendix F). Awareness was measured by 11 items
(items 1, 2, 5-9, 22-25). Ten items comprised the career exploration section of the
measure (items 10-19). Educational milestones consisted of six items (items 3, 4, 20, 21,
26, 27). The response format used in a study of writing self-efficacy with 4th grade
students (Shell, Colvin, & Bruning, 1995) was adopted with a slight change in wording to
make the responses congruent. Students were asked to indicate how sure they were that
they could do each task on a 5-point scale as follows: I (I’m sure I can ’t), 2 (I ’m pretty
sure I can ’t), 3 (maybe I can), 4 (I’m pretty sure I can), and 5 (I’m sure I can). However,
in addition to the words, each response was represented by a symbol instead of a
corresponding number. The responses were coded and summed for a total score; the total
was then divided by the number of items (n = 27) in the scale to achieve the self-efficacy
score. A higher score indicates a greater level of career preparation self-efﬁcacy.

The Grammatik computer program was used to compute the survey’s readability.
The survey had a 3.44 grade level using the Flesch-Kincaid readability index, indicating
an acceptable reading level for third through ﬁfth graders. The vocabulary complexity

was rated zero on a scale of 0 tolOO, with 100 being complex.

ll6

Data Analysis

The surveys were edge coded and the data were subsequently entered into a ﬁle
using the computer program SPSS for Windows 7.5 (SPSS, 1997). Identiﬁcation numbers
were assigned to students to protect their conﬁdentiality. To enable examination of
internal consistency and factor analysis, and because this is an exploratory study,
constituent (i.e., raw) data were entered into the computer ﬁle (Fitz-Gibbon & Morris,
1987). Variables were created for scaled scores.

Prior to analysis each variable was examined for missing values, skewness,
kurtosis, outliers, and accuracy of data entry. Missing values were excluded pairwise

from analyses.

Descriptive Statistics

Descriptive statistics of sample characteristics and comparisons with the sample
schools and school district were reported where available. Descriptive statistics
(frequency, mean, standard deviation, range) for all measures were computed.

Data for mother’s education were missing for 30 students. As reported earlier in
the chapter, after examination of the cross tabulation of mother’s education and
race/ethnicity, the decision was made to drop the Asian American students from the data
analysis. There were only seven Asian students, and of those for whom mother’s
education data were available (n = 3), all had not graduated from high school. This
confounded the race/ethnicity and mother’s education variables for those students.

Examination of data by classroom showed that two of the variables of interest

(i.e., mother’s education and race) were not represented in all classrooms for every

117

category of the variable. In those instances, classroom and grade are confounded. While
classroom level comparisons can be made, in the multivariate analyses it will be

necessary to use grade rather than classroom to represent the social context of the school.

Inferential Statistics

Correlation matrices were computed to examine the relationships among the
continuous variables. T tests, ANOVAs and chi-squares were used to test for signiﬁcant
differences between career-related variables and person and social context variables.

For the ANOVA tests, in cases where signiﬁcant differences were found (i.e.,
overall F in the AN OVA is signiﬁcant), post hoc comparisons using Scheffé’s test were
conducted to explore differences between means. If the overall F is signiﬁcant, at least
one out of all possible comparisons between pairs of means or complex combinations of
means will be signiﬁcant. Post hoc tests permit the researcher to discover where the
differences lie by making a large number of statistical tests while maintaining an
established level of signiﬁcance. Scheffé’s test is the most widely accepted test for
making post hoc comparisons (Shavelson, 1988). Furthermore, it is considered the most
conservative with respect to controlling Type I error rate across multiple tests (Hair,
Anderson, Tatham, & Black, 1995).

A summary of the research questions that guided the study, hypotheses to be

tested, variables of interest, and the planned statistical analyses is presented in Table 18.

118

Table 18
Summary of Research Questions, Hypotheses, and Analysis

 

 

Research Hypotheses

Questions Tested Variables Analysis
1.1) To what degree None Career Exposure Descriptive statistics:
do children receive Frequencies
exposure to the M, SD

career environment?

1.2) In what ways
does career exposure
differ based on
person and social
context variables?

P_er§on variabfles:
(gender, age,
race/ethnicity)

Social Context
variables: (grade,
classroom, SES)

Inferential statistics:
t test (gender)
correlation (age)

AN OVA (race,
mother’s ed., grade,
classroom), post hoc
multiple comparisons
(Scheffé’s test)

 

2.1 ) What interests None
do elementary

students have in
participating in

career preparation

learning

experiences?

2.2) In way do
interests differ based
on person and social
context variables?

Interest in Career
Preparation
Learning
Experiences

Person variables:
(gender, age, &
race/ethnicity)

Social Context
variables: (grade,
classroom, SES)

Descriptive statistics:
Frequencies

M, SD
By item & child

Inferential statistics:
By item:
chi-square (gender)

By child:

t test (gender)
correlation (age)
ANOVA (race,
mother’s ed., grade,
classroom), post hoc
multiple comparisons

(Scheffé’s test)

(Table continues)

 

119

Table 18 (cont’d)

 

 

 

Research Hypotheses
Questions Tested Variables Analysis
3.1) In what types None Participation in Descriptive statistics:
of career preparation Career Frequencies
learning experiences Preparation M, SD
have elementary Learning By item & child
students Experiences
participated?
3.2) In what way Positive Person variables: Inferential statistics:
does participation in relationship (gender, age, By item:
career preparation between race/ethnicity) chi-square (gender)
learning experiences mother’s
differ based on education & Social Context By child:
person and social participation in variables: (grade, I test (gender)
context variables? career classroom, SES) correlation (age)
preparation AN OVA (race,
learning mother’s ed., grade,
experiences. classroom), post hoc
multiple comparisons
(Scheffé’s test)
4.1) What career None Career Descriptive statistics:
preparation self— Preparation Self— Frequencies,
efﬁcacy beliefs Efﬁcacy: M, SD
(awareness, career Awareness
exploration, & Exploration
educational Educational
milestones) do Milestones
children have?

4.2) How is career
preparation self-
efficacy related to
other career
variables?

Career Exposure;
Career
Preparation
Learning
Experiences

Inferential statistics:
correlations

(Table continues)

 

120

Table 18(cont’d)

 

 

 

 

Research Hypotheses
Questions Tested Variables Analysis
Question 4 (con’td):
4.3) In what way do -Positive Inferential statistics:
career preparation relationship t test (gender)
self-efﬁcacy (CPSE) between CPSE correlation (age)
beliefs differ based and career ANOVA (race,
on person and social exposure. mother’s ed., grade,
context variables? —Positive classroom), post hoc
relationship multiple comparisons
between CPSE (Scheffé’s test)
and participation
in learning
experiences
—CPSE does not
differ by gender
in elementary-
age children.
5) What are the None CPSE - 27 items Factor analysis:

relationships among
the three dimensions
that constitute career
preparation self-
efﬁcacy in this

Principal axis

Varimax rotation
Compute subscale
scores

Subscales (revised):

 

 

study? Internal consistency
reliability
6) What Person, social Outcome: Multivariate analysis:
combination of context, and Subscales of MANCOVA
person, social career-related CPSE (after
context, and career- variables factor analysis) Post hoc univm
related variables uniquely affect Categorical: m:
contribute to career multiple self— (gender, race, ANOVA (if categorical
preparation self— efﬁcacy grade, mother’s variables)
efﬁcacy? outcomes. ed) Multiple Regression (if
Continuous: continuous variables)
(age, career
exposure,
learning
experiences)
Interactions:
racex gender

racexmother’s ed

 

121

Multivariate Analyses

Multivariate analysis is preferred over univariate tests for several reasons (Hair et al.,
1995; Rencher, 1995; Stevens, 1996). The statistical tests employed control the overall
Type I error rate; conducting a large number of univariate tests would inﬂate it.
Multivariate tests are compatible with systems theories (e.g, ecological) because these
tests recognize the complex nature of systems. The tests assume that the variables are
intercorrelated, particularly when there are multiple outcomes, and take this into account.
Furthermore, a multivariate test will be more powerful because it is able to jointly
consider a set of variables that may differentiate groups. Two types of multivariate
analyses will be employed in this study: factor analysis (a descriptive technique) and

multivariate analysis of covariance (MANCOVA; an inferential technique).

Factor Analysis

Factor analysis is a multivariate statistical technique whose primary purpose is to
reduce a large number of variables into a smaller set. It is useful for examining the
structure of the interrelationships among a large number of variables by deﬁning a set of
common underlying dimensions, called factors. Factor analysis seeks to account for the
correlations among the variables (Rencher, 1996).

Factor analysis was deemed appropriate in the current study based on the several
criteria (Hair et al., 1995). First, the sample size was sufﬁciently large to undertake factor
analysis (i.e., at least a ﬁve-to-one ratio of observations to variables). At least ﬁve or
more variables were included that represented each proposed factor (3 factors were

proposed, with 6 to 11 variables for each). The data matrix contained sufﬁcient

122

correlations greater than 0.30 among the variables to be analyzed. Because of the
objectives of the research, common factor analysis was selected. This method, also called
principal factor analysis, allows for the identiﬁcation of latent dimensions or constructs
represented in the original variables (Rencher, 1995). The analysis derives estimations of
loadings; the loading is the correlation between the variable and the factor. It provides the
linear combination that accounts for the greatest amount of variance and repeats the
process with the remaining variables. Once a solution is derived, factors are rotated to aid
interpretation. Orthogonal rotation using the Varimax technique seeks loadings that
maximize the variance, making them either large or small (Rencher, 1996). An
orthogonal solution is advised if the subscale scores are to be used in another technique
such as regression since it maintains independence among factors (Hair et al., 1995).
Principal axis factoring with orthogonal (Varimax) rotation is available on SPSS 7.5
(1997). Once factor analysis was conducted, the internal consistency reliability of the
Career Preparation Self-Efﬁcacy Scale and subscales was performed. The subscale scores

derived were used for subsequent multivariate analysis.

Multivariate Analysis of Covariance

Mulitivariate analysis of covariance (MANCOVA) will be the multivariate test
employed in this study. This multivariate technique is useful for analyzing the differences
in a set of dependent measures; it is the use of multiple outcomes that distinguishes it
from univariate analysis of variance. As a multivariate technique, MANCOVA has

several advantages. As Hair et al. (1995) explain,

123

A series of univariate tests . . . ignores the possibility that some composite (linear
combination) of the dependent variables may provide evidence of an overall
group difference that may go undetected by examining each dependent variable
separately. Individual tests ignore the correlations among the dependent variables
and thus use less than the total information available for assessing overall group
differences. In the presence of multicollinearity among the dependent variables,

MAN OVA [and MAN COVA] will be more powerful than the separate univariate

tests. (p. 266)

Self-Efﬁcacy subscale scores derived through factor analysis were used as the
dependent measures. Subscale scores were used rather than factors scores. Factor scores
maintain independence among the factors, while in reality the subscales and the items in
them are related.

The initial model contained the categorical variables gender, race/ethnicity,
mother’s education, and grade and the continuous variables age, total learning
experiences and total career exposure. Interactions tested were race by gender and race by
mother’s education. This model was used to test the hypothesis that some combination of
person and social context variables and learning experiences uniquely affects two
dimensions of career preparation self-efﬁcacy—awareness of interests and goals self-
efficacy and active exploration self-efﬁcacy.

Variables were ordered to test complex antecedents (i.e, interactions) ﬁrst
(Rencher, 1995). In this procedure, each variable was examined to see if it made a unique
contribution after all the other variables had been taken into account. Because there are
different numbers of people in each group of categorical variables, the design is
unbalanced and the statistical tests are nonorthogonal; that is, they are not independent.

An independent test of signiﬁcance for each variable was accomplished by successively

placing each variable last in the model. After each analysis, the variable was dropped if it

124

was not signiﬁcant; signiﬁcant variables were retained. The most commonly used test
statistic for overall signiﬁcance in MANCOVA is Wilks’ lambda (Hair et al., 1995).
After each step, the model was reordered with the remaining variables, placing a different
variable last in the model. The analysis was repeated until all variables had been tested.
Once all the variables were tested, signiﬁcant ones were retained for further post hoc
analysis.

In order to protect against inﬂation of the studywise error rate, Rencher (1995)
recommends performing tests on individual variables only if the overall MANCOVA test
is rejected (i.e., that the model does not make a unique contribution). In this procedure,
the probability of rejection for the tests on individual variables is reduced, and these tests
become more conservative. When univariate tests are made only following a rejection of
the overall test, the studywise error rate is maintained at an appropriate level.

Based on the outcome of the MANCOVAS, further analysis was conducted with
the remaining signiﬁcant variables. Appropriate post hoc statistical tests (AN OVA for
models containing categorical variables, regression for models with continuous variables)

were employed.

125

Chapter 4

RESULTS

The results of the data analysis will be reported in this chapter in two main
sections. The ﬁrst section will describe analyses for the career-related variables (career
exposure and interest and participation in career learning experiences) and career
preparation self-efﬁcacy. Analyses that address the research questions and hypotheses are
reported. When hypotheses were not stated for relationships between the variables,
exploratory analyses using appropriate statistical tests were conducted. In order to control
of the greater chance of making a Type I error when computing multiple t tests or chi-
squares, the alpha for samplewise error was maintained at .05 and the testwise error was
adjusted accordingly.2 For the AN OVA tests, in cases where signiﬁcant differences were
found, post hoc comparisons using Scheffé’s test were conducted to explore differences
between means.

The second section will report the results of the multivariate analyses. Factor
analysis was conducted for the 27 items that comprised the Career Preparation Self-
Efﬁcacy Scale. Three factors were extracted a priori based on conceptual considerations.
The resulting factor matrix was interpreted and the number of items was reduced to 17.

Revised subscale scores were created and two of the three subscales (Awareness

of Interests and Goals Self-Efficacy and Active Exploration Self-Efﬁcacy) were used in

 

Formula for maintaining a studywise at .05: a testwise = l- V55 where c =
number of comparisons and 1- .05 (a studywise) = .95

126

subsequent analyses. A series of multivariate analysis of covariance (MAN COVA) tests
were conducted to understand how person variables, social context variables, and
participation in career preparation learning experiences uniquely affect career preparation
self-efficacy outcomes. After nonsigniﬁcant variables were dropped from the model,
three variables remained. To obtain a ﬁnal model, the MANCOVAs were followed by

univariate analysis of variance (ANOVA) and multiple regression.

Career-Related Variables
Career Exposure
Students reported varying degrees of exposure to their job or career preference.
This career exposure came from four possible sources: (a) family; (b) community,
neighborhood, or school; (c) reading; and (d) television. Children indicated if they knew
someone who had the job they listed, selecting as many sources as appropriate.
Television was the source most often named, while family was the least (Table 19).
Students reported that they had from zero to four of these sources of exposure related to
their career preference; the mean number of career exposure sources was 2.03 (SD =
1.11). One third of the students (33.5%) indicated only zero or one source of career

exposure (see Figure 11).

127

Percent of Students

Figure 11

Table 19

Sources of Career Exposure

 

Percent Indicating

 

Type of Exposure Yes
Family 38.9
Community 48.7
Reading 53.2
TV 62.7

 

Note. Students were asked to respond to exposure in relation
to a stated career/j ob preference

 

100

901

BOI

701

60-l

30-

20d

10-

 

0

 

1 2 3 4

Sources of Career Exposure

Total Sources of Career Exposure

128

 

The four sources of career exposure are not highly correlated (Table 20). The one

signiﬁcant correlation is a moderate one between reading and televison, r (158) = .32.

Table 20
Correlation Matrix for Career Exposure Variables

 

Sources of Career Exposure

 

Total Family Community Reading TV

 

 

Career Exposure (total) —

Exposure through family .48*** —

Exposure through community 56*" .15 —

Exposure through reading .62*** -0. 16 .07 ——

Exposure through TV .60*** .003 .01 .32*** —
"*p < .001

No predictions were made about the relationships between career exposure and
person and social context variables. To explore for possible signiﬁcant differences
appropriate statistical tests were conducted; these analyses are summarized in Table 21.
Signiﬁcant differences were found for one person variable (race/ethnicity) and for one
social context variable (classroom).

The mean score for girls and boys was 2.0 sources of career exposure. Therefore,
a t test of independent means showed no signiﬁcant difference between boys’ and girls’
total sources of career exposure, t (158) = -0.132, p = .90. There was no signiﬁcant
correlation between age and career exposure, r (l 5 8) = 0.14, p = .08. The one-way
ANOVA to test for differences between students of different racial and ethnic

129

backgrounds was signiﬁcant, F (2, 155) = 3.86, p = .03. Post hoc multiple comparison
tests revealed that the African American students (M = 2.2) had signiﬁcantly more career
exposure than the Hispanic students (M = 1.5).

Students whose mothers had not completed high school reported slightly less
career exposure than those whose mothers had more education (Ms = 1.9 and 2.1,
respectively). Similarly, third-grade students reported somewhat less exposure to their
preferred career than older students (Ms = 1.8 and 2.1, respectively). A series of one-way
AN OVAs was used to test for differences with social context variables. There were no
signiﬁcant differences for mother’s education F (3, 130) = 0.294, p = .83 or for grade
level, F (2, 155) = 1.075, p = .34. There were, however, signiﬁcant differences by
classroom, F (8, 149) = 2.57, p = .012. Exposure ranged from a low of 1.6 sources in
Classrooms C and F to a high of 2.7 sources in classrooms D and G. Post hoc multiple
comparisons were not able to reveal signiﬁcant differences in amount of career exposure
between classrooms. Because of the conservative nature of the Scheffé test it is possible
to ﬁnd no signiﬁcant differences even though the F for the ANOVA was signiﬁcant
(Gay, 1987). It is possible that a more complex combination than pairs of means

contributed to this result.

130

Table 21
Summary of Means, Standard Deviations, and Tests of Signiﬁcance for Amount of
Career Exposure and Person and Social Context Variables

 

Total Career

 

 

 

 

 

 

 

Exposure
Variable n M SD Test of Signiﬁcance
Gender
Girls 76 2.0 0.61 t (158) = -O.132,p = .90
Boys 82 2.0 0.65
Age
M=10.2 . r(158)=0.14,p=.08
Race/Ethnicity
African American 66 2.2 1.2 F (2, 155) = 3.86, p =.03*
Hispanic 23 1.5 1.0
Caucasian 69 2.1 1.0
Classroom
A 15 2.2 1.2 F(8, 149) = 2.57,
B 11 1.8 1.3 19:91?”
C 20 1.6 0.9
D 14 2.7 1.1
E 19 2.1 1.1
F 18 1.6 1.0
G 18 2.7 0.9
H 18 2.0 1.3
I 25 1.8 0.9
Grade
3 46 1.8 1.1 F(2,155)=l.075,p=.34
51 2.1 1.1
5 61 2.1 1 1

 

(Table continues)

131

Table 21 (cont’d)

 

Total Career

 

 

 

Exposure
Variable n M SD Test of Signiﬁcance
Mother’s Education
Not completed high 29 1.9 1.1 F (3, 130) = 0.294, p = .83
school
High school graduate 54 2.1 1.2
Attended college 32 2.1 0.9

College graduate or more 19 2.1 1.2

 

*p s .05 "p s .01

Career Preparation Learning Experiences
The Career Preparation Learning Experiences (CPLE) Checklist measured both
participation and interest in 15 career preparation activities. CPLE-Participation was a
variable in the multivariate study model. CPLE-Interest was used only as a descriptive

variable.

Interest in Career Preparation Learning Experiences

Interests by activity. Although no formal hypotheses were advanced regarding
children’s interest in speciﬁc career preparation learning experiences, this variable was
examined with descriptive and inferential statistics. Overall, children indicated a high
degree of interest in career preparation learning experiences. The majority of students

indicated interest in all but 1 of the 15 activities. Students indicated the least amount of

132

interest in doing a job or chore at home (45.3 percent). They were most interested in
doing a job in their school or classroom (84.0 percent), visiting a college campus (84.9
percent), and going on a job-related ﬁeld trip (89.3 percent). For the remaining 11
activities, approximately three quarters of the students indicated interest. The students’
degree of interest in the 15 career preparation learning experiences is summarized in
Table 22.

Chi-square tests were conducted to test for signiﬁcant gender differences in
interest in each of the career preparation learning experiences. Using a testwise alpha of
.003 to control the Type I error rate due to multiple comparisons, there were no
differences in interest based on gender.

Interests by child. The mean interest score was 11.25 (SD = 3.13). Three quarters
(75.2 percent) of the students indicated an interest in 10 or more of the 15 activities
(Figure 12).

While no formal hypotheses were advanced regarding relationships of person and
social context variables to the level of interest in career preparation learning experiences,
these relationships were tested. Several signiﬁcant differences were found for person
variables (race/ethnicity) and social context variables (grade and classroom). Post hoc
multiple comparison tests were conducted in these cases to examine the nature of the

differences. The results of these statistical tests are summarized in Table 23.

133

Table 22
Degree of Interest in Career Preparation Learning Experiences

 

 

Career Preparation Children Indicating

Learning Experience Interest
Do a job or chore at home 45.3%
Do a report or project 70.1%
Read a book 71.8%
Hear a guest speaker 72.4%
Do community service or volunteer 73.0%
G0 to library 73.7%
Make a list of things to improve 74.2%
Make a list of skills 75.3%
Find out skills needed 75.5%
Talk to parent or teacher 76.8%
Attend after-school activity 77.0%
Find out about school subjects 77.1%
Do a job in classroom or school 84.0%
Visit college campus 84.9%
G0 on job-related ﬁeld trip 89.3%

 

I34

 

 

 

 

100

90 1

80 I

70 1

60 l

50 ..
.9 40.
c:
8
a 30 I
(D
"5 20 1
E
8 10 1
b
o. 0 ,

1-3 interests 7-9 interests 13-15 interests
4—6 interests 10-12 interests

Interest in Career Preparation Learning Experiences

Figure 12 Students’ Degree of Interest in Career Preparation Learning
Experiences

Girls and boys had almost equal interest in career preparation learning experiences
(Ms = 11.4 and 11.1, respectively). Therefore, a t test of independent means indicated no
signiﬁcant difference based on gender t (139) = 0.611, p = .54. There was no signiﬁcant
correlation with interest and age, r (141) = 0.005, p = .95. However, total interest score
differed by as much as two activities across students of different racial and ethnic
backgrounds, and this difference was signiﬁcant F (2, 138) = 5.88, p = .004. Post hoc
multiple comparison tests revealed that the African American and Hispanic students (Ms

= 12.0 and 12.2, respectively) expressed signiﬁcantly more interest in career preparation

135

learning experiences than the Caucasian students (M = 10.3).

For the social context variables, AN OVAs were conducted to examine the mean
differences in level of interest in career preparation learning experiences. Those children
whose mothers had a higher level of education indicated less interest in these learning
experiences. However, these differences were not enough to be signiﬁcant, F (3, 115) =
0.231, p = .87. There was a signiﬁcant difference in total interest score by grade level, F
(2, 138) = 6.54, p = .002. Post hoc multiple comparison tests revealed that third- (M =
12.4) and ﬁfth-grade (M = 11.5) students had signiﬁcantly more interest than fourth grade
students (M = 10.1). There was also a signiﬁcant difference by classroom, F (2, 132) =
2.89, p = .005. Post hoc multiple comparison tests revealed that students in Classroom F
(M = 9.3) expressed signiﬁcantly less interest than students in Classroom B (M = 13.8)

and C (M= 12.5).

136

Table 23
Summary of Means, Standard Deviations, and Tests of Signiﬁcance for Interest in
Career Preparation Learning Experiences and Person and Social Context Variables

 

 

 

 

 

 

 

Learning
Experiences
Variable n M SD Test of Signiﬁcance
Gender
Girls 71 11.4 3.0 t(141)=0.611,p=.54
Boys 70 11.1 3.3
Age
M=10.2 r(141)=0.005,p= .95
Race/Ethnicity
African American 53 12.0 2.3 F(2, 138) = 5.88,
Hispanic 22 12.2 2.6 P = 004”
Caucasian 66 10.3 3.6
Classroom
A 10.9 2.7 F(2, 132) = 2.89,
B 8 13.8 1.8 P: 005"
C 19 12.5 1.5
D 12 11.4 2.3
E 18 9.9 3.6
F 18 9.3 4.1
G 15 11.7 3.5
H 18 12.2 1.5
I 24 10.9 3.4
Grade

 

36 12.4 2.1 F(2, 138) = 6.54,
4 48 10.1 3.6 P = '002"
57 11.5 3.0

 

(Table continues)

137

Table 23 (cont’d)

 

 

 

Learning
Experiences
Variable n M SD Test of Signiﬁcance
Mother’s Education

Not completed high 24 11.2 3.2 F (3, 115) = 0.231, p = .87
school
High school graduate 47 11.4 3.5
Some college 30 10.7 2.7
College grad or more 18 11.0 3.6

 

**p< .01

Participation in Career Preparation Learning Experiences

Participation by activity. Although no formal hypotheses were stated regarding

children’s participation in speciﬁc career preparation learning experiences, these variables

were explored using descriptive and inferential statistics. The vast majority of students

(89.2 percent) had done a job or chore at home. At the other extreme, approximately half

of the students had not done community service or a volunteer job (48.7 percent) nor had

they participated in a career-related after-school activity (54.1 percent). For the remaining

12 activities, approximately 25 to 45 percent of the students indicated that they had not

participated. The degree of participation in the 15 career preparation learning experiences

is reported in Table 24.

138

Table 24
Level of Participation in Career Preparation Learning Experiences

 

 

Career Preparation Students Who
Learning Experience Have Not Participated
Do a job or chore at home 10.8%
Do a job in classroom or school 25.3%
G0 on job-related ﬁeld trip 28.5%
Talk to parent or teacher 29.9%
Find out about school subjects 31.2%
Visit college campus 35.4%
Hear a guest speaker 37.6%
Make of list of what I’m good at 39.2%
Make a list of things to improve 39.2%
G0 to library for job information 39.2%
Find out skills needed 40.5%
Read a book 41.4%
Do a report or project 44.6%
Do community service or volunteer 48.7%
Attend after-school activity 54.1%

 

139

Participation by child. Children indicated that they had participated in between
one and ﬁfteen career preparation learning experiences. The mean number of learning
experiences was 9.5 (SD = 3.21). One quarter of the students (24.2 percent) have had
seven or fewer of the 15 learning experiences. The students’ level of participation is

summarized in Figure 13.

 

100

90:

80-

70-

60:

50:

40-

SCI

20:

10:

 

 

Percent of Students

 

 

 

1-3 4-6 7-9 10-12 13-15

Number of Learning Experiences

Figure 13 Level of Student Participation in Career Preparation Learning
Experiences

Chi-square tests were conducted to see if the rate of participation in each of the 15
activities differed by gender. Using an adjusted alpha of .003 to account for a greater

chance of making a Type I error, no signiﬁcant gender differences were found.

140

Analyses were conducted to test for signiﬁcant differences with participation in
career preparation learning experiences and person and social context variables. One
hypothesis (for the relationships between participation and mother’s education) was
tested and rejected. Several signiﬁcant differences were found for person (race/ethnicity)
and social context (grade and classroom) variables. Post hoc multiple comparison tests
were conducted in these cases to examine the nature of the differences. The results of
these statistical tests are summarized in Table 25.

Although no formal hypotheses were advanced regarding the relationship with
participation in career preparation learning experiences and person variables, these
relationships were tested. The mean number of learning experiences for girls was 9.9 (SD
= 3.0) and for boys 9.2 (SD = 3.4). A t test of independent means indicated that there was
no signiﬁcant difference in the total number of learning experiences based on gender, t
(153) = l.352,p = 0.18.

One-way ANOVAs were conducted to further examine possible differences in the
degree of participation in career learning experiences. Mean scores for participation
differed by the student’s race/ethnicity, F (2, 150) = 9.49, p < 0.001. Post hoc multiple
comparison tests revealed that the African American students (M = 10.7) reported
participation in signiﬁcantly more learning experiences than the Caucasian students (M =

8.3).

141

Table 25

Summary of Means, Standard Deviations, and Tests of Significance for
Participation in Career Preparation Learning Experiences and Person and Social

Context Variables

 

 

 

 

 

 

 

 

Learning
Experiences
Variable n M SD Test of Signiﬁcance

Gender

Girls 73 9.9 3.0 t (153) = l.352,p = 0.18

Boys 80 9.2 3.4
Age

M=10.2 r(141)=-0.04,p= .63
Race/Ethnicity

African American 63 10.7 2.7 F (2, 150) = 9.49,

Hispanic 23 9.7 2.8 P < 901*"

Caucasian 67 8.3 3.4
Classroom

A 13 11.2 2.7 F(8,144)=3.81,

B 9 10.2 3.7 P< 001*"

C 20 10.4 2.5

D 13 11.3 1.4

E 19 8.4 3.4

F 18 6.8 3.0

G 18 9.2 3.2

H 18 10.4 3.0

I 25 9.0 3.4
Grade

3 42 10.6 2.8 F (2,150) = 4.62,p = .01*

4 50 8.6 3.3

5 61 9.5 3.2

 

142

(Table continues)

Table 25 (cont’d)

 

 

 

 

Learning
Experiences
Variable n M SD Test of Signiﬁcance
Mother’s Education

Not completed high 29 9.8 2.9 F (3, 127) = 0.753, p = .52
school
High school graduate 54 9.8 3.5
Attended college 30 8.9 3.3
College grad or more 18 8.8 3.5

 

**p = .01 mp < .001

For the social context variables, it was hypothesized that participation in career
preparation learning experiences would be positively related to mother’s level of
education. Instead, the scores were negatively related to education level. Students whose
mother’s education level was that of high school graduate or less reported an average of
one more learning experience than those whose mothers had attended or graduated from
college (Ms = 9.8, 8.9,and 8.8, respectively). Using a one-way ANOVA, there was no
difference in mean scores based on mother’s level of education, F (3, 127) = 0.753, p =
0.52. Therefore this hypothesis was not supported.

By grade level there was a difference of two learning experiences between the
lowest and highest scores. A comparison of mean scores for participation in career
preparation learning experiences by grade yielded a signiﬁcant difference, F (2, 150) =
4.62, p = 0.01. Post hoc multiple comparison tests revealed that fourth graders (M = 8.6)

had signiﬁcantly fewer learning experiences than third graders (M = 10.6). Individual

143

classroom means showed an even greater range, differing by as much as four learning
experiences (Ms = 6.8 to 11.3). Consequently, there was also a signiﬁcant difference by
classroom, F (8,144) = 3.81, p < .001. Post hoc multiple comparison tests revealed that

signiﬁcant differences existed between Classroom F and Classrooms D, A, H, and C.

Career Preparation Self-Efﬁcacy

A primary purpose of this study was to describe the nature of elementary-age
children’s career preparation self-efficacy (CPSE) beliefs along three dimensions
(Awareness, Exploration, and Educational Milestones). The mean score on the CPSE was
4.28 (SD = 0.49). The mean Self-Efﬁcacy subscale scores were 4.10 (SD = 0.65) for
Exploration, 4.33 (SD = 0.51) for Awareness, and 4.50 (SD = 0.55) for Educational
Milestones.

The responses for this measure were collected on a ﬁve-point scale. Examining
the pattern of responses, most of the students rated their self-efficacy for the various
career preparation tasks at 3 or above, indicating a moderate to very high degree of
certainty in their ability to complete the career preparation tasks. Because of the small
numbers in some cells, categories indicating some degree of uncertainty (1 = I ’m sure I
can’t, 2 = I ’m pretty sure I can’t, and 3 = maybe I can) were collapsed into one category.
The resulting category indicated those who had lower self-efficacy for these tasks.
Overall, more than one third (34.6 percent) of the students had an Exploration Self-
Efﬁcacy subscale score less than 4.0, approximately one quarter (23.1 percent) for the
Awareness Self-Efﬁcacy subscale, and 14.7 percent for the Educational Milestones Self-

Efﬁcacy subscale. The students’ efﬁcacy levels for each career preparation task are

144

summarized in Table 26. For 12 out of the 27 career preparation tasks, approximately one
quarter to two ﬁfths of the students (24.8 percent to 39.9 percent) indicated that they were
uncertain about their abilities to perform them. All of these activities were in either
Exploration or Awareness activities. They were most uncertain about those activities that
required active engagement and more complex planning and decision making.

Further examination of the item means indicated a range ﬁom a low of 3.75 (SD =
1.26) for get job-related experience to a high of 4.68 (SD = 0.63) for get a good job
(Table 27). The students had greater self-efficacy for educational tasks versus exploration
activities. All items in the Educational Milestones Self-Efﬁcacy subscale had a mean
score of 4.24 or above. This was higher than all but one of the items in the Exploration
Self-Efficacy subscale; the career preparation task of ﬁnding information in the library

had a mean score of 4.44.

145

Table 26

Comparison of Level of Self-Efﬁcacy for Career Preparation Tasks

 

 

 

 

 

 

 

 

Level of Self-Efﬁcacy“

Item Unsure Pretty Sure Sure
Exploration
Get job-related experience 39.9% 22.8% 37.3%
Talk with someone 31.6% 13.9% 54.4%
Find out salary 30.4% 27.2% 42.4%
Find out subjects to take 29.3% 24.8% 45.9%
Visit workplace 29.1% 21.5% 49.4%
Use computer to ﬁnd job information 28.5% 20.3% 51.3%
Describe job skills 26.6% 29.7% 43.7%
Find out schooling needed 25.9% 25.3% 48.7%
Think about future 22.3% 21.7% 56.1%
Find job information in library 1 5.7% 22.9% 61 .4%
Awareness
Make an educational plan 38.6% 24.1% 37.3%
Choose career with mostly women 29.3% 16.6% 54.1%
Choose career with mostly men 28.7% 19.7% 51.6%
Decide what is valued most 24.8% 24.8% 50.3%
Select one job 23.4% 27.8% 48.7%
Stick to choice 16.6% 16.6% 65.1%
Choose job to ﬁt interests 14.4% 23.4% 66.9%
Figure out interests 13.3% 22.2% 64.6%
Choose, not matter what others say 10.8% 23.6% 65.6%
Figure out best job 10.1% 27.2% 62.7%
Figure out skills 8.9% 16.5% 74.7%
Educational Milestones
Go to college or post high school ed. 21.0% 23.6% 55.4%
Graduate from high school 14.6% 19.0% 66.5%
Take math in high school 14.6% 19.0% 66.5%
Get good grades 12.1% 21 .7% 66.2%
Get good job 8.9% 14.0% 77.1%
Study hard 8.2% 25.3% 66.5%

 

’N = 158; Responses on a 5-point scale, categories collapsed - l to 3 = unsure; 4 = pretty

sure; 5 = sure

146

Table 27

Comparison of Career Preparation Self-Efﬁcacy Items Means and Standard

Deviations by Gender

 

 

 

 

 

 

 

Gender
Total Sample Girls Boys
N = 158 n = 79 n = 85
Item M (SD) M (SD) M (SD)

Awareness
Make an educational plan 3.87 (1.08) 3.96 (1.03) 3.79 (1.13)
Choose career with women 4.04 (1.28) 4.56 (0.79)*** 3.56 (1.45)***
Choose career with men 4.08 (1.17) 3.81 (1.25) 4.32 (1.04)
Select one job 4.16 (1.02) 4.24 (0.81) 4.10 (1.18)
Decide what is valued most 4.24 (0.88) 4.16 (0.97) 4.31 (0.78)
Stick to choice 4.43 (0.98) 4.31 (1.08) 4.54 (0.86)
Find job to ﬁt interests 4.49 (0.80) 4.49 (0.70) 4.49 (0.88)
Figure out best job 4.51 (0.71) 4.45 (0.77) 4.57 (0.65)
Choose, no matter what 4.52 (0.77) 4.51 (0.78) 4.54 (0.77)
Figure out interests 4.59 (0.67) 4.66 (0.66) 4.54 (0.67)
Figure out skills 4.64 (0.71) 4.54 (0.81) 4.73 (0.59)

TOTAL 4.33 (0.51) 4.33 (0.50) 4.33 (0.51)
Exploration
Get job related experience 3.75 (1.26) 3.61 (1.21) 3.88 (1.30)
Find out salary 3.99 (1.11) 3.88 (1.11) 4.10 (1.11)
Visit workplace 4.03 (1.20) 3.97 (1.22) 4.07 (1.18)
Talk with someone 4.04 (1.25) 3.96 (1.34) 4.12 (1.17)
Describe job skills 4.07 (1.05) 4.04 (1.05) 4.10 (1.05)
Use computer 4.08 (1.16) 4.09 (1.05) 4.07 (1.26)
Find out subjects to take 4.09 (1.02) 4.05 (1.07) 4.12 (0.97)
Find out schooling needed 4.13 (1.05) 4.07 (1.06) 4.18 (1.03)
Think about future 4.23 (1.07) 4.16 (1.21) 4.30 (0.93)
Find info in library 4.44 (0.86) 4.41 (0.90) 4.47 (0.82)

TOTAL 4.10 (0.65) 4.02 (0.67) 4.17 (0.63)

 

147

(Table continues)

Table 27 (cont’d)

 

 

 

 

 

 

Gender
Total Sample Girls Boys
N = 158 n = 79 n = 85
Item M (SD) M (SD) M (SD)
Educational Milestones

Take math in high school 4.48 (0.85) 4.37 (0.88) 4.59 (0.82)
Graduate from high school 4.49 (0.84) 4.43 (0.84) 4.54 (0.83)
Study hard 4.53 (0.81) 4.59 (0.80) 4.48 (0.82)
Get good grades 4.53 (0.75) 4.53 (0.70) 4.53 (0.79)
Find good job 4.68 (0.63) 4.67 (0.62) 4.70 (0.64)
TOTAL 4.50 (0.55) 4.39 (0.53) 4.50 (0.57)
Total CPSE 4.28 (0.49) 4.25 (0.49) 4.31 (0.50)

 

Note. Maximum score = 5.0
"*t (155) = 5.29,p <.001

Correlations
A zero-order correlation matrix was computed to examine relationships between

the CPSE subscales (Table 28). The Self-Efficacy subscales had moderately high and

signiﬁcant correlations (approximately r = 0.60) with each other.

 

 

 

 

Table 28
Zero-Order Correlations for Career Preparation Self-Efﬁcacy Subscales
Self-Efﬁcacy Subscales
Awareness Exploration Educational Total CPSE
Milestones
Awareness —
Exploration .62" —
Educational Milestones .59" .60“ —
Total CPSE .87" .90" .79M —
** p s .01

148

Additionally, a zero-order correlation matrix was computed for the 27 items that
comprise the CPSE (Table 29). Correlations occurred both between items within
subscales and between subscales. Examination of this matrix shows that most of the
correlations were weak to moderate, and that all correlations of r = 0.16 and above were
signiﬁcant, p = .05, with those above r = 0.21 signiﬁcant at the p = .01 level. Only those
correlations of r = 0.40 or greater are discussed here. Most of the correlations were
computed for a sample size of N = 158; in cases of missing data, the sample size was 157
or156.

Signiﬁcant correlations were found among the items in the Educational
Milestones Self-Efﬁcacy subscale. The strongest relationship was between studying hard
and graduating ﬁom high school, r = 0.54. Studying hard correlated with getting good
grades, r = 0.48 and getting a good job, r = 0.42. There were several moderate
correlations with items in the Exploration and Awareness Self-Efﬁcacy subscales.
Graduating ﬁ'om high school correlated with knowing schooling needed, r = 0.43, and
ﬁnding someone to help me think about the future, r = 0.40. Going to college correlated
with ﬁnding someone to help me think about the future, r = 0.42, from the Exploration
Self-Efficacy subscale, as well as sticking to a choice, r = 0.44, and choosing no matter
what others say, r = 0.41, items that are from the Awareness Self-Efﬁcacy subscale.

Within the Awareness Self-Efﬁcacy subscale choosing no matter what others say
correlated with sticking to a choice, r = 0.45, ﬁguring out what I ’m interested in, r = 0.44,
ﬁnding a job to ﬁt my interests, r = 0.42, and deciding what I value most, r = 0.40. The
item choose a career in which most of the workers are women stands out because of the

lack of signiﬁcant correlations with other items in the subscale. It did correlate weakly

149

but signiﬁcantly with choosing no matter what others say and sticking to choice. There
were also several moderate correlations between items in the Awareness and Exploration
Self-Efﬁcacy subscales. Choosing no matter what others say correlated with ﬁnding out
the schooling needed, r = 0.51; sticking to choice correlated with ﬁnding out the salary,
r = 0.47; and choosing a career with mostly men correlated with describing the job skills,
r=040

Within the Exploration Self-Efﬁcacy subscale, talking with someone about a job I
am interested in was related to visiting a workplace, r = 0.52, and with knowing the job ’s
salary, r = 0.40. Using the computer to ﬁnd out job information correlated with ﬁguring
out the amount of schooling needed, r = 0.44. Knowing the job skills was also related to

ﬁguring out the amount of schooling needed, r = 0.41.

150

Table 29
Zero-Order Correlation Matrix for Career Preparation Self-Efﬁcacy Scale (CPSE)

 

Item 1 2 5 6 7 8 9 22 23 24 25 10 ll 12

 

CPSEl —

CPSE 2 .23 — Awareness Subscale

CPSE5 .34 .27 —

CPSE6 .26 .30 .37 —

CPSE 7 .22 .17 .20 .33 —

CPSE 8 .30 .25 .27 .27 .36 —

CPSE 9 .15 .37 .36 .13 .29 .28 --

CPSE 22 .28 .44 .34 .40 .28 .42 .16 —

CPSE 23 .32 .23 .37 .30 .33 .26 .23 .45 —

CPSE 24 .37 .20 .19 .28 .28 .18 .15 .32 .33 —
CPSE 25 .11 .08 .12 .05 .16 .14 .02 .24 .17 .07 —
CPSEIO .13 .17 .12 .06 .16 .15 .15 .22 .17 .23 .12 —Exploration
CPSEll .18 .04 .27 .14 .15 .03 .12 .13 .17 .24 .11 .34 -Subscale
CPSE12 .31 .21 .26 .35 .38 .30 .12 .23 .24 .11 .31 .26 .30 —
CPSE13 .33 .25 .21 .31 .30 .33 .35 .32 .33 .40 .10 .10 .28 .33
CPSE14 .23 .19 .24 .24 .17 .18 .08 .32 .47 .33 -.004 .23 .35 .20
CPSE 15 .19 .23 .30 .27 .29 .32 .26 .51 .23 .23 .22 .33 .44 .39
CPSE16 .22 .24 .18 .17 .19 .11 .03 .38 .29 .36 .18 .11 .24 .17
CPSE17 .23 .09 .24 .16 .14 .06 -.02 .23 .25 .29 .18 .03 .21 .25
CPSE18 .10 .03 .11 .10 .09 .07 .10 .20 .19 .28 .21 .26 .35 .18
CPSE 19 .20 .27 .23 .21 .17 .16 .18 .32 .38 .29 .22 .30 .23 .24

 

 

CPSE 3 .18 .15 .32 .12 .30 21 .23 .33 .41 .16 .25 .30 .22 .18
CPSE 4 .21 .31 .28 .12 .29 .34 17 .34 .29 .10 .05 .26 .26 .27
CPSE 20 .28 .24 .28 .24 .06 .03 .19 .21 .17 .30 -.06 .23 .26 .20
CPSE 21 .17 .17 .30 .20 .30 .18 .17 .24 .33 .27 .11 .22 .29 .20
CPSE 26 .15 .30 .29 .17 .20 .07 .22 .41 .43 .21 .28 .18 .12 .26
CPSE 27 .20 .13 .18 .16 .29 .25 .09 .37 .24 .16 .12 .14 .23 .22

 

 

151

Table 29 (cont’d)

 

Item l3 14 15 l6 17 18 19 3 4 20 21 26 27

 

CPSE 13 -

CPSE 14 .28 — Exploration Subscale
CPSE 15 .41 .28 —

CPSE 16 .32 .40 .35 —

CPSE 17 .26 .30 .28 .52 —

CPSE 18 .26 .14 .38 .30 .30 —
CPSE 19 .36 .33 .37 .28 .38 .36 --

 

CPSE 3 .19 .32 .27 .28 .08 .15 .26 —

CPSE 4 .20* .33 .32 .21 .18 .07 .24 .48 — Educational Milestones
CPSE 20 .27 .14 .19 .20 .10 .14 .31 .32 .24 — Subscale
CPSE21 .30 .25 .43 .15 .21 .15 .40 .54 .32 .33 —

CPSE 26 .26 .15 .36 .22 .37 .21 .42 .33 .21 .30 .36 —
CPSE 27 .31 .29 .29 .40 .27 .09 .32 .42 .27 .17 .37 .18 —

 

 

Note. Matrix is grouped by subscales; heavy line indicates items included in subscale.
Awareness subscale = Items 1-2, 5-9, 22-25; Exploration subscale = Items 10-19;
Educational Milestones = Items 3-4, 20-21, 26-27.

All correlations of .40 and above are in bold type. All correlations 0.16 and above
signiﬁcant at p < .05, all correlations 0.21 and above signiﬁcant at p < .01

152

Relationships Between Career-Related Variables and Career Preparation Self-Efficacy
A zero-order correlation matrix was computed to examine the relationships among
the dichotomous and continuous variables in the study (Table 30). It was hypothesized that
there would be a positive relationship between career preparation self-efﬁcacy and career
exposure, and between career preparation self-efﬁcacy and participation in career
preparation learning experiences. The relationships were in the predicted direction and the
correlations were moderate and signiﬁcant, r (155) = 0.30, and r (150) = 0.37, p < .001,
respectively. Therefore these hypothesis were supported. A higher correlation for learning
experiences than exposure makes sense conceptually, because learning experiences

involve direct participation, while exposure is more indirect.

Table 30
Zero-Order Correlation Matrix for Career-Related Variables

Gender Age CE CPLE - I CPLE - P CPSE

 

 

Gender —

Age -.01 _

Career Exposure .0] .14 —

CPLE - Interest -.05 .01 .11 —

CPLE - Participation -.11 -.04 .39“ .50” —

CP Self-Efﬁcacy .06 -.05 .30" .30” .37M —

 

Note. Gender coded 0 = female, 1 = male; CP = Career Preparation; CPLE = Career

Preparation Learning Experience; CPSE = Career Preparation Self-Efﬁcacy
*4! —
p - .01

There were other moderate correlations, all signiﬁcant at p < .001, between the

career-related variables. The correlation between career preparation self-efficacy and

153

interest in career preparation learning experiences was r (139) = 0.3 0. Additionally, the
relationship between participation in learning experiences and total interest score, r (141)
= 0.50, was signiﬁcant, as was the correlation between participation in learning
experiences and career exposure, r (141) = 0.39. None of the career-related variables were
correlated with gender or age. The correlation between interest and exposure was low and

nonsigniﬁcant.

Relationships between Career Preparation Self-Efficacy and Person and Social Context
Variables

No signiﬁcant differences were found with career preparation self-efﬁcacy and the
person and social context variables. One hypothesis (regarding lack of gender differences)
was tested and supported. The results of these analyses are reported below and
summarized in Table 31.

Gender. The hypothesis was advanced that there is no difference in career
preparation self-efﬁcacy scores based on gender. The mean score for girls was 4.25 (SD =
0.49) and the mean score for boys was 4.31 (SD = 0.50). A t test of independent means
was conducted to test for differences based on gender. The difference between boys’ and
girls’ overall CPSE score was not signiﬁcant, t (155) = -0.748, p = .46, therefore this
hypothesis was supported.

To determine whether variations on individual items were masked by the lack of a
gender difference on the overall score, t tests were conducted to test for signiﬁcant
differences between the means. In order to maintain the alpha for samplewise error at .05,

it was determined that the testwise alpha needed was .002. Using this criterion, boys and

154

girls differed signiﬁcantly on only one item: their certainty about whether they could
choose a career in which most of the workers were women. Boys were less certain they
could choose a career in which most of the workers were women, t (157) = 5.288, p <
.001. A comparison of item means by gender for the each item in the CPSE scale is
presented in Table 27.

Age and race/ethnicity. A correlation was computed to test the relationship
between age and CPSE; no age differences were found, r (158) = 0.21, p = .80. Mean
scores for Caucasian, African American, and Hispanic students were 4.22, 4.30, and 4.40,
respectively. AN OVA was used to test for signiﬁcant differences; no signiﬁcant
differences were found, F (2, 152) = 1.136, p = .32.

Social context variables. One-way AN OVAs were used to test for signiﬁcant
differences. By grade, scores ranged from 4.27 for fourth graders to 4.30 for ﬁfth graders.
ANOVA was used to test for differences. There were no signiﬁcant differences by grade
level, F (2, 152) = 0.023, p = .98. Classroom means showed the greatest distribution
compared with that of other variables, ranging from 4.17 to 4.52. There were no
signiﬁcant differences by classroom, F (8, 146) = 0.821, p = .59.

Students whose mothers were college graduates had the lowest scores (M = 4.12).
Those whose mothers had attended college and those who had not completed high school
had the same mean score (M = 4.26). Those whose mothers were high school graduates
had the highest mean score (M = 4.41). One-way AN OVA was used to test for signiﬁcant
differences. There was no signiﬁcant difference for this relationship, F (3, 128) = 1.934,

p=.13.

155

Table 31
Summary of Means, Standard Deviations, and Tests of Significance for Career
Preparation Self-Efﬁcacy and Person and Social Context Variables

 

 

 

 

 

 

 

 

CPSE Score
Variable n M SD Test of Signiﬁcance
Gender
Girls 75 4.25 0.49 t (155 )= -0.748, p = .46
Boys 80 4.31 0.50
Age
M=10.2 r(158)=0.21,p=.80
Race/Ethnicity
African American 65 4.30 0.45 F(2, 152) = 1.136,p = .32
Hispanic 23 4.40 0.47
Caucasian 67 4.22 0.54
Classroom
A 15 4.32 0.48 F(8, 146) = .821,p = .59
B 10 4.35 0.58
C 19 4.21 0.58
D 14 4.52 0.30
E 19 4.17 0.52
F 18 4.19 0.53
G 18 4.20 0.49
H 18 4.30 0.45
I 24 4.35 0.49
Grade
3 44 4.28 0.54 F (2, 152) = 0.023, p = .98
51 4.27 0.49
5 60 4.30 0.49

 

(Table continues)

156

Table 31 (cont’d)

 

 

 

 

CPSE Score
Variable n M SD Test of Signiﬁcance
Mother’s Education
Not completed high 28 4.26 0.47 F (3, 128) = 1.934, p = .13
school
High school graduate 53 4.41 0.44
Attended college 32 4.26 0.49

College graduate or more 19 4.12 0.56

 

Note. CPSE = Career Preparation Self-Efﬁcacy Scale; maximum score is 5.0

Multivariate Analyses
Multivariate analyses were used to address two of the research questions related to
career preparation self-efﬁcacy. Speciﬁcally, the relationships among the three conceptual
dimensions of this outcome were addressed through factor analysis. The unique
contributions that person, social context, and learning experience variables have on aspects

of career preparation self-efﬁcacy were tested with multivariate analysis of covariance

(MANCOVA).

Factor Analysis
Factor analysis was used to examine the structure of the interrelationships among

the 27 variables that comprised the Career Preparation Self-Efﬁcacy Scale (CPSE). This

measure was adapted from existing measures designed for use with older students. Three

157

conceptually related dimensions of career preparation self-efﬁcacy were included: (a)
awareness, (b) exploration, and (c) educational milestones. There were two purposes for
the factor analysis: exploratory and conﬁrmatory. The objectives of the exploratory
analysis were to reduce the amount data and to provide subscale scores to be used in
subsequent multivariate analysis. The analysis would also aid in substantiating the initial

conceptualization of the total scale and the speciﬁc items that comprise it.

Computation of F actor Matrix

After computing the correlation matrix, factor loadings were estimated by principal
axis factoring using orthogonal (V arimax) rotation to improve interpretation (SPSS, 1997).
An orthogonal solution is advised if the subscale scores are to be used in another technique
such as regression since it maintains independence among factors (Hair et al., 1995).
Based on the conceptual design of the measure, the determination was made a priori to
extract three factors. The results of the factor analysis are reported in Table 32. The rotated

factor matrix is reported in Table 33.

 

 

Table 32
Factor Analysis Results
Initial Eigenvalues Extraction Sums of Rotation Sums of Squared
Squared Loadings Loadings
% of Cum. % of Cum. % of

Factor Total Variance % Total Variance % Total Variance Cum. %
1 7.404 27.422 27.422 6.753 25.012 25.012 3.220 11.926 11.926
2 1.801 6.671 34.093 1.148 4.250 29.263 2.854 10.571 22.497
3 1.520 5.630 39.724 0.909 3.366 32.629 2.736 10.132 32.629

 

 

Note. Extraction method: Principal axis factoring. 3 factors extracted a priori.

158

Table 33

 

 

 

Rotated Factor Matrix
Factor
Item 1 2 3

Select job to ﬁt interests .583 .004 .163
Choose, no matter what others say .555 .337 .214
Figure out what I’m interested in .530 .134 .1.03
Decide what is valued most .516 .238 .002
Develop plan of educational goals for 3 years .471 .162 .243
Select one job .464 .002 .191
Figure out best job .442 .113 .254
Stick to choice .431 .366 .269
Describe job skills .428 .362 .178
Figure out what I’m good at .410 .230 .110
Figure out what subjects to take .367 .231 .238
Visit workplace .007 .662 .005
Talk with someone .120 .635 .161
Get job-related experience .003 .468 .176
Choose career with mostly men .275 .465 .103
Find someone to help think about the future .230 .450 .339
Find out salary .193 .358 .307
Go to college or post-high school education .351 .358 .264
Choose career with mostly women .120 .188 .144
Study hard .196 .009 .718
Graduate from high school .202 .224 .596
Get good grades .325 .003 .547
Find information in library . 1 55 . 1 70 .418
Use computer .003 .336 .415
Get a good job .201 .220 .399
Decide how much schooling is needed .347 .365 .393
Take math in high school .242 .225 .345

 

Note. Highest loading for each item is indicated in bold type.

159

Interpretation of Factor Matrix

The interpretation of the factor analysis was based on several criteria: (a) the
variables loaded at least 0.40 on a factor, (b) they did not load closely on two or more
factors, and (0) their inclusion made sense theoretically and conceptually. Based on these
criteria several items were eliminated. There were ﬁve items with loadings below 0.40
(choose career with mostly women, go to college or post high school education, take math
in high school, ﬁgure out what subjects to take, and ﬁnd out salary) and therefore they
were eliminated. One item (choose career with mostly men) loaded high on Factor 2, but
was eliminated because the companion item (belief about the ability to choose a career
with mostly women workers) was eliminated due to low loadings on all three factors. This
decision was in keeping with the empirical ﬁnding that career decision making is a
" gender neutral" process. The remaining items that were eliminated loaded closely on two
or more factors. A total of 17 items remained. The following results are based upon the
full analysis of the 27 items in the CPSE.

Factor 1 consisted of eight items (Items 1-2, 5-9, 22) with factor loadings ranging
from 0.583 (find job to ﬁt interests) to 0.410 (ﬁgure out my skills) and accounted for 11.9
percent of the variance. Analysis of the highest loading items suggested that this factor
tapped self-efficacy tasks that involved making choices that required an awareness of
one’s career interests and goals. This factor was titled Awareness of Interests and Goals
Self-Efﬁcacy (previously called Awareness).

Factor 2 consisted of four items (Items 15-19) with factor loadings ranging from
0.662 (visit a workplace) to 0.450 (ﬁnding someone to help me think about the future) and

accounted for 10.6 percent of the variance. Analysis of the highest loading items suggested

160

that this factor tapped self-efﬁcacy for active participation in work-related activities and
was titled Active Exploration Self-Efﬁcacy (previously called Exploration).

Factor 3 consisted of four items (Items 3-4, 10, 21, 27) with factor loadings
ranging from 0.718 (study hard) to 0.399 (get a good job) and accounted for 10.1 percent
of the variance. Analysis of these items suggested that this factor tapped self-efﬁcacy for
educational tasks and their outcomes and was titled Educational Tasks Self-Efficacy

(previously called Educational Milestones).

Revised Self-Efficacy Subscale Scores

Subscale scores were computed for use in subsequent multivariate analyses.
Subscale scores were used rather than factors scores. Factor scores maintain independence
among the factors, while in reality the subscales and the items in them are related. Scores
were obtained by summing the items that remained in each subscale after factor analysis
(see Table 34).

The Awareness of Interests and Goals Self-Efﬁcacy subscale consisted of eight
items; the mean score was 4.38 (SD = 0.51). Four items comprised the Active Exploration
Self-Efﬁcacy subscale; the mean score was 4.02 (SD = 0.85). The Educational Tasks Self-
Efﬁcacy subscale consisted of ﬁve items and had a mean score of 4.34 (SD = 0.47). There

were moderate and signiﬁcant correlations between subscales (Table 35).

161

Table 34
Revised Subscales for Career Preparation Self-Efﬁcacy

 

 

Items
Subscales n Range M SD Reliability
Awareness of Interests & Goals 8 2.88 - 5.00 4.38 0.51 0.75
Active Exploration 4 1.00 - 5.00 4.02 0.85 0.68
Educational Tasks 5 2.60 - 5.00 4.53 0.53 0.72
Total CPSE (Revised) 17 2.94 - 5.00 4.34 0.47 0.82

 

CPSE and Subscale Reliability

Internal consistency reliability was computed for the three Self-Efﬁcacy subscales
and the total Revised CPSE. The alpha coefficient for the Revised CPSE (17 items) was
0.82. The item-total correlations were moderate, ranging from 0.32 to 0.58. The alpha
coefﬁcients for the Awareness of Interests and Goals (8 items), Active Exploration (4
items), and Educational Tasks (5 items) Self-Efﬁcacy subscales were 0.75, 0.68, and 0.72,
respectively. Given that the CPSE measures beliefs that are subject to change and may not
be stable, particularly in young children, the reliability of these the overall Revised Career
Preparation Self-Efﬁcacy scale is satisfactory. Subscales, because they contain fewer

items, typically have lower reliability coefﬁcients than the total scale (Gay, 1987).

162

Table 35
Correlation Matrix for Revised Career Preparation Self-Efﬁcacy Subscale Scores

 

Self-Efficacy Subscales

 

Total Aware Explore Educ. Tasks

 

 

Total CPSE (Revised) —

Awareness of Goals and Interests .83** —

Active Exploration .74" .35" —

Educational Tasks .76" .50" .40" —
"p = .01

Although the scales are moderately correlated, they are different enough to be
considered separately. The Awareness of Goals and Interests and Exploration Self-
Efﬁcacy subscale scores will be used as outcomes in subsequent multivariate analyses.
However, the scores on Educational Tasks Self-Efﬁcacy subscale were negatively skewed;
the median (Mdn = 4.80) was higher than the mean score (M = 4.53), and close to the
maximum possible score. Therefore, this Self-Efficacy subscale was dropped from ﬁrrther

analysis.

Multivariate Analysis of Covariance
Multivariate analysis was conducted to examine how the variables of interest may
combine to inﬂuence career preparation self-efficacy. Multivariate analysis of covariance
(MAN COVA) using two outcomes for career preparation self-efﬁcacy (Awareness of
Interests and Goals Self-Efficacy Subscale and Active Exploration Self-Efﬁcacy Subscale)
was conducted. The full model included race/ethnicity, gender, mother’s education, and

grade as categorical variables, and the continuous variables of age, learning experiences,

163

and career exposure. Interactions tested in the full model were race/ethnicity by mother’s
education and race/ethnicity by gender. This model was used to test the hypothesis that
some combination of person and social context variables and learning experiences
uniquely affects two dimensions of career preparation self-efﬁcacy—awareness of interests
and goals and active exploration.

Each variable in the model was examined to see if it made a unique contribution
after all the other variables had been taken into account. An independent test of
signiﬁcance for each variable was accomplished by successively placing each variable last
in the model. After each analysis, the last variable was dropped if it was not signiﬁcant; all
other variables were retained for successive testing. After each test, the model was
reordered and the analysis was repeated until all variables had been tested and only
signiﬁcant variables remained.

Variables were ordered to test complex antecedents ﬁrst (Rencher, 1995).
Therefore, the two interactions (race/ethnicity by mother’s education and race/ethnicity by
gender) were the ﬁrst two variables tested. The two interactions, age, gender,
race/ethnicity, and grade were all found to be nonsigniﬁcant. Mother’s education, career
exposure, and learning experiences were signiﬁcant. The hypothesis that a combination of
variables affects career preparation self-efficacy was supported. Results of the series of

MANCOVA analyses are reported in Table 36.

164

Table 36
Multivariate Analysis of Covariance Results

 

Wilks’ Hypothesis
Effect Lambda F df Error df p

 

Design: LETOTAL + EXPOSTOT + MOTHED4 + RACE + GENDER + GRADE +
AGEYRS + RACE*GENDER + MOTHED4*RACE

Race* mother’s education .890 1 .302 10 21 8 .23

 

Design: LETOTAL + EXPOSTOT + MOTHED4 + RACE + GENDER + GRADE +
AGEYRS + RACE*GENDER

Race*gender .968 0.930 4 228 .45

 

Design: LETOTAL + EXPOSTOT + MOTHED4 + RACE + GENDER + GRADE +
AGEYRS

Age .997 0.188 2 116 .83

 

Design: LETOTAL + EXPOSTOT + MOTHED4 + RACE + GENDER + GRADE
Grade .994 0.185 4 234 .95

 

Design: LETOTAL + EXPOSTOT + MOTHED4 + RACE + GENDER
Gender .981 1.148 2 119 .32

 

Design: LETOTAL + EXPOSTOT + MOTHED4 + RACE
Race/ethnicity .987 0.384 4 240 .82

 

Final Model

Design: LETOTAL + EXPOSTOT + MOTHED4

 

Mother’s education .882 2.646 6 244 .02
Total career exposure .947 3.389 2 122 .04
Total learning experiences .917 5.552 2 122 .005

 

Note. Statistics reported are for the variable when it was the last one in the model.
Outcomes are Awareness of Interests and Goals Self-Efﬁcacy and Active Exploration
Self-Efficacy.

165

Post Hoc Univariate Analyses
Analysis of Variance

Once all the variables were tested, Signiﬁcant ones were retained for further post
hoc analysis. Three variables remained signiﬁcant after the MANCOVAs: Mother’s
Education, Career Exposure, and Leaming Experiences. Having rejected the null
hypothesis for the MANCOVA model (i.e., that the model does not make a unique
contribution), univariate tests are appropriate (Rencher, 1995). Two separate univariate
AN OVAs were conducted with Awareness of Interest and Goals Self-Efﬁcacy and Active
Exploration Self-Efﬁcacy as the outcomes. The results of these analyses are reported in
Tables 37 and 38. Learning Experiences was signiﬁcant in each analysis. Mother’s
Education was a signiﬁcant explanatory variable for Active Exploration Self-Efﬁcacy, F
(3, 124) = 3.578, p = .02, but not for Awareness of Interests and Goals Self-Efficacy, F (3,
123) = 0.997, p = .40. Similarly, Career Exposure was signiﬁcant in the ANOVA for
Active Exploration Self-Efficacy, F (1, 124) = 5.962, p = .02, but was not Signiﬁcant for
Awareness of Interests and Goals Self-Efﬁcacy, F (1.123) = 2.575, p = .11.

To examine what might be contributing to a signiﬁcant F value for Mother’s
Education, a scatterplot was computed. The scatterplot of the variables Mother’s
Education and Active Exploration Self-Efﬁcacy showed the presence of outliers. That is,
there were cases where the student’s Mother’s Education level was high, but their Active
Exploration Self-Efﬁcacy score was very low (see Figure 14). ANOVA is especially
sensitive to outliers; that is, they have a disproportionate impact on the overall results
(Hair et al., 1995). Therefore, Mother’s Education was eliminated as a variable in the next

step in the

166

analysis. Career Exposure was retained, however, to see if it contributed to the model once

Mother’s Education was removed.

 

 

 

 

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Figure 14 Scatterplot Indicating Outliers for Active Exploration Self-Efﬁcacy
and Mother’s Education

167

Table 37

ANO VA for Awareness of Interests and Goals Self-Efficacy

 

 

 

 

 

Sum of Mean

Squares df Square F p B
(Combined) 3 .825 2 1.912 7.979 .001
Career Exposure .617 1 .617 2.575 .111 .007
Learning Experiences 1.562 1 1.562 6.515 .012 .003
Mother’s Education .717 3 .239 .997 .397
Model 4.664 5 .933 3.892 .003
Residual 29.480 123 .240
Total 34.144 128 .267
Table 38
AND VA for Active Exploration Self-Efﬁcacy

Sum of Mean

Squares df Square F p B
(Combined) 13.376 2 6.688 12.437 .000
Career Exposure 3.206 1 3.206 5.962 .016 .160
Learning Experiences 4.202 1 4.202 7.814 .006 .006
Mother’s Education 5.773 3 1.924 3.578 .016
Model 20.511 5 4.102 7.628 .000
Residual 66.683 124 .538
Total 87.194 129 .676

 

168

Multiple Regression Analyses

As a follow-up to the AN OVAs, multiple regression analyses were conducted.
Two separate regression analyses were run—one with Awareness of Interests and Goals
Self-Efﬁcacy as the outcome, and one with Active Exploration Self-Efficacy as the
outcome. Learning Experiences and Career Exposure were the two independent variables.
The overall model for each self-efﬁcacy outcome variable was signiﬁcant. The results Of
these analyses are reported in Table 39 and Table 40.

In the regression model for Awareness of Interests and Goals Self-Efficacy,
Learning Experiences was a Signiﬁcant variable, p = .04, while Career Exposure bordered
on signiﬁcance, p = .06. An examination of the standardized beta coeﬁicients showed that
the impact of these two variables was almost identical (,Bs = 0.174 vs. 0.163,
respectively), therefore Career Exposure is considered practically signiﬁcant in this model.
The multiple correlation coefficient for the model was R = .28. In this model, 8 percent of
the variance in Awareness of Interests and Goals Self-Efﬁcacy was explained by Learning
Experiences and Career Exposure (R2 = .079).

In the regression model for Active Exploration Self-Efﬁcacy, both Learning
Experiences and Career Exposure were signiﬁcant variables, p = .001 and p = .03,
respectively. An examination of the standardized beta coefﬁcients showed that Learning
Experiences had a larger impact than did Career Exposure in explaining Active
Exploration Self-Efﬁcacy scores (ﬁs = 0.269 vs. 0.180, respectively). The multiple
correlation coefﬁcient for the model was R = .38. Therefore, this model containing Learing
Experiences and Career Exposure explained 14 percent of the variance in Awareness of

Interests and Goals Self-Efficacy (R2 = .142).

169

Table 39
Regression Model for Self-Efﬁcacy - Awareness of Interests and Goals

 

Unstandardized Standardized

 

 

 

 

 

 

Coefﬁcients Coefﬁcients
Model B Std. Error Beta t p
(Constant) 3 .976 0.126 31.537 .000
Learning Experiences 0.002 0.013 .174 2.037 .043
Career Exposure 0.007 0.038 .163 1.902 .059
Model Summary: R = .281 R2 = .079 SE = .49
F (2, 148) = 6.348, p = .002
Table 40
Regression Model for Self-Efﬁcacy - Active Exploration
Unstandardized Standardized
Coefﬁcients Coefﬁcients
Model B Std. Error Beta t p
(Constant) 3.053 0.029 14.595 .000
Learning Experiences 0.007 0.022 .269 3.363 .001
Career Exposure 0.138 0.063 .180 2.183 .031

 

Model Summary: R = .377 R2 = .142 SE = .81
F (2, 129) = 12.342, p <.001

170

 

 

 

 

 

 

 

Career Self-Efﬁmcy
Exposure “80> Exploration

 

 

 

Figure 15 Correlations and Partial Correlations for Final Model
Note. Correlations indicated by r, partial correlations in parentheses.

The betas, or partial correlations, from the regression analyses and the correlations
between the dependent and independent variables can be used to understand the
relationships among the variables. The correlations and partial correlations are shown in
Figure 16. The partial correlations take into account that Learning Experiences and Career
Exposure are correlated, and that Awareness of Interests and Goals Self-Efficacy and
Active Exploration Self-Efﬁcacy are correlated. Therefore the partial correlation

represents the unique correlation between two variables.

171

Summary of Multivariate and Post Hoc Analyses

By taking into account the variables in the model in the presence of all the others,
the MAN COVAS were able to identify signiﬁcant variables related to two career
preparation self-efficacy outcomes. Subsequently, these variables were entered into post
hoc univariate analyses (AN OVA and multiple regression). The resulting model takes into
account the effects of Participation in Learning Experiences and Career Exposure on
Awareness of Interests and Goals Self-Efﬁcacy and Active Exploration Self-Efﬁcacy.

Explanation of career preparation self-efﬁcacy was increased by using multiple
outcome variables (Awareness of Interests and Goals and Active Exploration). Because
the Educational Tasks subscale had virtually no variability, inclusion of these items in the
combined scale limited its usefulness. Because the remaining subscales had more
variability, a better understanding of these complex relationships was obtained.

In the ﬁnal models, both career exposure and participation in career preparation
learning experiences contributed to the understanding of career preparation self-efficacy
beliefs of elementary-age students. While both participation and exposure contributed
signiﬁcantly to explaining Awareness of Interests and Goals and Active Exploration self-
efflcacy, participation in learning experiences had more impact than career exposure for
Active Exploration. However, the percent of variance explained by both models is small.
Clearly, other variables would contribute to a more complete understanding of career

preparation self-efﬁcacy beliefs of elementary-age children.

172

Limitations of This Study

While this study may suggest relationships between career preparation self-
efﬁcacy and elementary-age children’s participation in career preparation learning
experiences and amount of career exposure, caution must be used when generalizing the
results beyond this sample. Generalizations from this study are limited because while the
classrooms were randomly selected from eight schools, the students were not. Although
the sample was representative of the population of the sample schools, they may have
differed in some unknown manner. There were missing data on mother’s education for 30
students.

Results are dependent on how accurately the elementary students were able to
report their beliefs. Children in this age range have varying levels of cognitive
development, and their cognitive abilities would affect how well they were able to
understand the questions and answer them accurately.

Another possible limitation is that the instruments adapted and created for this
study may not adequately measure the variables of interest. The checklist of career
preparation learning experiences was developed for this study because no measures of this
kind were located. Participants were asked only to report whether they had done the
activity, not the frequency, intensity, or meaning derived from it. Furthermore, the
assumption was made that having done the activity equated a learning experience; this
may not have been the case. Despite this limitation, the measure appeared able to capture
the concept of career preparation leaming experiences, as it was one of two variables

signiﬁcantly related to career preparation self-efﬁcacy outcomes.

173

The correlational design of the study does not permit establishing cause-effect
relationships. The resulting models accounted for only a small percentage of the variance.
Clearly, there are other variables contributing to career preparation self-efﬁcacy that were
not measured in this study. However, the study did use multivariate analyses, which
provide a conservative test of signiﬁcance. This provides a level of credence to the
ﬁndings that would not have been obtained with less powerful statistical methods.

Despite the problems inherent in the naturalistic setting presented by school
classrooms, some threats to validity and reliability were controlled. One researcher
administered the survey, which provided consistency of administration to the extent
possible. While the researcher attempted to be consistent, confusion resulted when
students did not follow directions. Individual administration, while more time consuming,
would allow for clariﬁcation and follow-up questions. The psychometric properties of the
measures used were adequate. This study makes a contribution because it used a diverse

sample at a younger age group than previously studied.

174

Chapter 5

DISCUSSION

Study Summary

Ecological (Bronfenbrenner, 1979, 1988, 1993; Bronfenbrenner & Morris, 1998),
social cognitive (Bandura, 1986), and self-efﬁcacy (Bandura, 1977, 1995, 1997) theories
provided the theoretical foundations for a study of career preparation self-efﬁcacy. The
career preparation self-efficacy beliefs of 167 third- through ﬁfth-grade students were
studied. These beliefs were examined in relation to person, social context, and career-
related variables. Gender, age, and race/ethnicity comprised the person variables. The
social context variables investigated were mother’s education, classroom, and grade.
Career-related variables were career exposure, interest in career preparation learning
experiences, and participation in career preparation learning experiences, and career
preparation self-efﬁcacy.

Fouad, Smith, & Enochs’ (1997) career decision-making self-efﬁcacy scale for
middle school students, an adaption of the Career Decision-Making Self-Efﬁcacy Scale
(Taylor & Betz, 1983) was modiﬁed for use with elementary-age students. Two
additional measures were developed for this study. An index of career exposure was
created to assess indirect inﬂuences of the social environment on career preparation self-
efﬁcacy. A checklist of 15 activities was used to assess interest and participation in career
preparation learning experiences. These measures were combined into one survey called

Careers and Me.

175

To address the research questions, ﬁve hypotheses were investigated; exploratory
analyses were conducted in cases where no hypotheses were advanced. Descriptive
statistics were used to summarize the variables of interest. Univariate analyses consisted
of t tests, chi-square, AN OVA, and Pearson correlations. The career-related variables
correlated positively and signiﬁcantly with each other and with career preparation self-
efﬁcacy. None of the person or social cognitive variables were signiﬁcantly related to
career preparation self-efficacy. However, several signiﬁcant differences were found for
relationships between career exposure, interest, and learning experience participation and
several person and social context variables. Post hoc multiple comparisons using
Scheffé’s test were used to examine the nature of differences between mean scores.

Factor analysis was used to reduce the data. Interpretation of the rotated factor
matrix yielded three revised self-efﬁcacy subscales for a total of 17 items. Two of the
three subscale scores were used in subsequent multivariate analyses. A series of
MAN COVAs identiﬁed a combination of variables that was signiﬁcantly related to two
outcomes of career preparation self-efficacy (Awareness of Interests and Goals & Active
Exploration). The MANCOVAS were followed with post hoc univariate tests. In the ﬁnal
regression models, only career exposure and participation in career preparation learning
experiences signiﬁcantly contributed to an explanation of career preparation self-efﬁcacy
beliefs. While career exposure had a similar effect for both outcomes, participation in
career learning experiences contributed more in the Active Exploration model. Although
signiﬁcant, neither model accounted for a large percentage of the variance in self-efﬁcacy

SCOTCS.

176

Purposes of This Study

The overall purpose of this study was to gain an understanding of elementary-age

students’ beliefs about their abilities to engage in career preparation activities by

examining the relationships between career preparation self-efficacy and selected person,

social context, and career-related variables. No studies of this kind were located in the

literature, therefore this study represented a beginning effort to describe career

preparation self-efﬁcacy and its correlates. Speciﬁcally, this study was designed to

accomplish the following goals:

1.

Examine children’s exposure to the career environment as well as their
interest and participation in learning experiences that contribute to career
preparation.

Gain an overall understanding of elementary-age children’s career
preparation self-efﬁcacy beliefs.

Understand the relationships among career preparation self-efﬁcacy and
person, social context, and career-related variables.

Adapt existing measures or develop measures that enable the study of
career preparation self-efﬁcacy with elementary-age children.

Provide theoretical and empirical support that will assist with the design of

appropriate career development educational programs for elementary-age

children.

In this chapter, the results of the present study are discussed in relation to these

purposes. The speciﬁc research questions addressed and hypotheses tested will be

summarized. Implications for theory, research, and practice will be considered.

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Discussion of Findings
Children ’s Career Preferences

As part of the Careers and Me survey, children were asked to indicate a job or
career that they wanted to do. This question was included to provide a context for
answering the career exposure questions, but as a descriptive variable, it is interesting in
its own right. An examination of students’ responses revealed "no surprises."
Occupational stereotypes are alive and well in elementary-age students: There was little
overlap of the job preferences stated by girls and boys. Professional sports was the job
most often chosen by boys, and teacher was the job most often chosen by girls. While the
Speciﬁc jobs chosen may be similar or different from other studies of children’s career
preferences, the pattern is the same that has been reported by other researchers (Phipps,
1995; Trice et al., 1995).

Although these descriptive data show that stereotypes are alive and well, they also
indicate that they may be changing somewhat, at least for girls. Consistent with recent
research, girls in this study more often selected careers that required a higher educational
level than those selected by boys (Phipps, 1995; Post et al., 1996; Trice et al., 1995).
Although teacher was the most popular choice with girls, only a few girls selected other
traditionally female occupations—two selected nurse and none selected secretary—choices
that had been prominent in earlier studies. More than two thirds of the girls in the sample
chose careers that could be classiﬁed as professional, technical, or managerial.

Another way to view children’s preferences is to look at what they did not choose.
Very few students, boys or girls, stated a preference for careers in science and

engineering (n = 5) and skilled trades (n = 3). Studies of women who have made these

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nontraditional choices indicate that role models were clearly an important factors in their
decision (Greene & Stitt-Gohdes, 1997). This is congruent with self-efﬁcacy theory, in
that models who are similar (e. g., in terms of characteristics like gender and race) will
likely make a larger impact. Interest in a career is often tied to knowing someone with
that career. Therefore, children’s interests may be expanded or limited by the amount and
type of models with whom they come in contact. This lends support to the
recommendations that other studies have made for exposure to role models in a variety of
careers.

Most of the choices were very general (i.e., teacher, doctor), suggesting a lack of
knowledge about a wide variety of options within a given ﬁeld and within a career
cluster. For example, there are many more careers in the health cluster than just doctor,
dentist, and nurse, and there are many different types of teachers. However, these are the
people that children likely come in contact with in through their everyday experience in
the community. It seems safe to assume that children are not choosing other options
because they do not know about them. Were they aware of other options, they may ﬁnd
that they are of greater interest to them.

The high level of boys who indicated an interest in professional sports careers
bears mentioning. If having the goal of a career in professional sports motivates students
to do well in school and persist at demanding tasks, then it has served an important
function. After all, professional basketball players once sat in elementary school
classrooms, long before their potential for professional-level status was evident. There is
nothing to say that a future star is not among these students as well. It is highly unlikely,

though, that almost half of the 87 boys who participated in this study will go on to have

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professional sports careers. If they have foreclosed prematurely on that choice (or any
other choice, for that matter), it may mean that they do not explore other options. The
choices they make now as a result of this expressed preference may serve to limit or
expand their opportunities.

The fact that girls are stating a preference for a professional sports career (n = 6)
can be viewed somewhat differently, as it represents an expansion of options previously
unavailable to them. This is indicative of a change at the societal (i.e., macrosystem)
level. Since the passage of Title IX just over 25 years ago, girls’ participation in sports
has increased dramatically. With the formation of the Women’s National Basketball
Association and the success of the US. women athletes in ﬁgure skating, hockey, and
soccer at the recent Olympic games, girls now have access to more role models portrayed
through the media. It has taken many years to see this effect of the Title IX legislation.
Perhaps it will be many years before the beneﬁts stimulated by the passage of the School-
to-Work Opportunities Act of 1994 are evident.

Although not addressed in the present study, a study of career choice content-
related self-efﬁcacy beliefs would complement a study of the process dimensions studied
here. It seems likely that individuals will more readily engage in career preparation tasks
related to those careers for which they have a high level of interest and efficacy. This
could be tested in a study using multiple self-efﬁcacy measures.

Although Trice (1991) found some stability over time (i.e., a school year) in
elementary children’s choices, what they indicate now as a preference does not dictate
their fate and their future choices. However, given that children’s stated preferences

closely matched the occupational gender stereotypes in the adult world, we should pay

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attention to what children are saying even in their childhood years. Additional

implications of these ﬁndings will be discussed further later in the chapter.

The Career Environment

Career Exposure

Children were asked to provide information about sources of career exposure that
they encountered in relation to their stated career preference. This exposure came from
four possible sources: (a) family; (b) school, neighborhood, or community; (c) reading;
and (d) television. Students reported an average of two sources of career exposure. It is
interesting to note that the sources that would provide direct contact (family and
community) were mentioned less often (38.9 percent and 48.7 percent respectively) than
were media sources (52.3 percent for reading and 62.7 percent for televison). Because
students indicated these sources in relation to their stated career preference, these results
suggest that students may not have career role models immediately available to them. It is
not surprising that the media, television in particular, represented such a large percentage
of career exposure; its presence in our culture is pervasive. Parents and educators should
be encouraged that half of the students reported reading as a source of career exposure. It
means, however, that it is important to pay attention to the number and type of
occupational images portrayed in the media. If "what you see" in the career environment
is "what you ge " in terms of choices, these portrayals could either reinforce or challenge
stereotypical images and serve to expand or restrict the variety of options considered.

There was strong support in the literature for including these four sources of

career exposure. However, it does not preclude that there are other important sources of

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career exposure that were not documented in the present study. A limitation of the Career
Exposure Index used to measure this variable is that students were asked about sources of
exposure as a simple yes or no question. Furthermore, the meaning that children attach to
these sources is not known. An alternate approach to studying career exposure would be
to ask children themselves for this information in an open-ended format rather than have
them respond to predetermined categories. A study of this nature suggests a more
qualitative approach using semi-structured interviews. This sort of information would
facilitate the further reﬁnement of the measure, similar to the process used by Young and
his colleagues in their studies of parents’ role in relation to adolescent career
development (Young & Friesen, 1992; Young et al., 1988). Using a qualitative procedure
called the critical incident technique, they were able to elicit rich description about
parents’ intentional actions that, from their perspective, they undertook to foster their
child’s career development. These actions could then be grouped into related categories.
Subsequently, Downing and D’Andrea (1994) used these identiﬁed categories to design a
survey that they were able to use with a larger sample. This type of iterative process
would serve to increase the measure’s "ecological validity" (Bronferbrenner, 1979),
making it more theoretically sound and practically meaningful.

Another conceptual consideration is that the measure really only assessed whether
the environment afforded the potential for an inﬂuence. Furthermore, the assumption has
been made that the inﬂuence is a positive one. Children’s exposure to the career
environment may also be a negative inﬂuence, particularly if children see stereotypical
portrayals in the media or experience negative attitudes toward careers. However,

students were asked to indicate this exposure in relation to a job or career for which they

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expressed interest. It is doubtful that children would have indicated an interest in a job

about which they had negative associations.

Interest and Participation in Career Preparation Learning Experiences

Overall, elementary-age children in this study had a high degree of both
participation and interest in career preparation learning experiences. Out of a possible
score of 15, the mean participation score for the career preparation learning experiences
was 9.5 and the mean interest score was 11.25. Interest and participation in career
preparation learning experiences were moderately and signiﬁcantly correlated (r = .50).
The ﬁnding that elementary-age students had a high interest in career preparation learning
experiences was not surprising. Previous evaluation reports of career-related programs
with this age group indicated that children enjoyed the activities and would like to do
more of them (Ferrari & Farrell, 1997; Human Service Research, 1996). Of concern,
therefore, would be students who indicated low participation, low interest, or both.
Reasons for this could be probed in studies using qualitative or action research methods.
Findings from such studies could improve how and with whom these learning
experiences are designed and implemented.

While there is considerable overlap of students’ interest in activities and their
participation, the discrepancy between the mean scores for participation and interest
indicated that there were also students who have not had an opportunity to participate in
activities in which they have an interest. Failure to capitalize on these interests represents
a missed opportunity.

It was hypothesized that those students whose mothers had completed a higher

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level of education would have participated in more career preparation learning
experiences. There was no signiﬁcant difference in this relationship so this hypothesis
was not supported. However, what is noteworthy is that although the results were not
signiﬁcant, they were in the opposite direction than expected. That is, the mean scores for
those students from homes where mothers had lower educational levels were higher, (by
approximately one learning experience) than those children from homes where mothers
had completed more years of schooling. It could be that mothers with a lower education
level are aware of the barriers their children will face in preparing for and entering the
workforce because they have experienced them ﬁrsthand. Perhaps this motivates them to
emphasize these experiences for their children. Another explanation is that schools and
other youth organizations (e. g., 4-H) may be making more attempts to provide these
career-related experiences through special programs, particularly to students who are
considered "at risk." This is, in fact, a speciﬁc goal of the collaborative efforts occurring
in the schools in the sample (Keith et al., 1998). This study provides support for the
continuation of such efforts. The resulting creation of career-rich learning environments
should serve to enhance development.

It is important to examine the signiﬁcant differences that were found between
career-related and person and social context variables. Speciﬁcally, there were signiﬁcant
differences found between level of participation and race and between level of interest by
race, as well as for grade and for classroom. While statistics reﬂecting the social context
of education often put minority students at a disadvantage, this study does not support
that notion. Perhaps this reﬂects the particular social context of the schools involved in

the study. Similar to studies of resilient children (for example, Werner & Smith, 1992),

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there is a notion of what might be called collective resilience. That is, there is evidence to
suggest that despite being in economically poor communities, there are schools where
students are doing exceptionally well (Waxman, Huang, Anderson, & Weinstein, 1997).
In a review of factors that inﬂuence student learning, it was shown that individual student
or classroom processes are much more likely to affect student learning than are school-
level or district-level factors (Wang, Haertel, & Walberg, 1990). Students’ perceptions of
the learning environment, as well as speciﬁc characteristics of the environment, were not
assessed in the current study. However this information would provide a valuable
perspective, and future studies could examine this aspect.

Furthermore, the Signiﬁcant differences by classroom, subsequently captured as a
grade-level difference for two of the three career-related variables, support the idea of
looking more closely at the microsystem environment of the classroom. Ecological theory
would contend that is the processes that occur within these systems that are important
(Bronfenbrenner & Morris, 1998). Because data were not collected regarding classroom
practices, it is impossible to know what might have contributed to these differences.
Additionally, some of the career experiences likely occurred outside the classroom
setting, suggesting the need to consider other microsystems and to consider mesosystem
linkages. Therefore, there are several possibilities for fruitful inquiry. Documentation of
the range of experiences and actual time spent in career preparation learning experiences
would prove useful, whether by direct observation or by student and teacher self-report.
Beyond the experiences themselves, the affective climate of the classroom may prove to
be a distinguishing factor. The amount and quality of teacher and student interaction are

two of the most important educational variables that promote student outcomes (Wang,

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Haertel, & Walberg, 1994), and these factors have been shown to differentiate effective
from ineffective schools (Waxman et al., 1997). Another idea is to examine whether
teacher training makes a difference in the amount and type of learning experiences
provided.

Along this line, the concept of self-efﬁcacy could be extended to include teacher
self-efﬁcacy. As Bandura (1995) stated,

the task of creating a learning environment conducive to learning rests heavily on

the talents and self-efficacy of teachers. Evidence indicates that classroom

atmospheres are partly determined by teachers’ beliefs in their instructional
efﬁcacy. Teachers who strongly believe in their instructional efﬁcacy create
mastery experiences for students. Those who have low assurance in their
instructional efﬁcacy generate negative classroom environments that are likely to

undermine students’ sense of efﬁcacy and cognitive development. (pp. 19-20)
Differences in teachers’ self-efficacy for conducting career preparation learning
experiences, and the value they place on these experiences, likely inﬂuence the classroom
climate. The present study supports an effort to more closely examine a multifaceted
concept of classroom environment.

A methodological limitation of the present study is the measure used to assess
participation in career preparation learning experiences. Students were asked to indicate
only whether or not they had participated in the activity, not the frequency, duration, or
the intensity of their participation. Adding these dimensions would improve the measure
conceptually as well as theoretically. According to one of the propositions of ecological
theory, to be effective as a source of development, activities must take place "on a fairly
regular basis over an extended period of time" (Bronfenbrenner & Morris, 1998, p. 996).

Clearly, this measure falls short on that account. However, in spite of its relative

simplicity, it was able to provide explanatory information regarding career preparation

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self-efﬁcacy.

Another limitation of the ﬁndings is that the data were not gathered regarding
student’s perception of the usefulness of these experiences as preparation for their future
careers in the world of work. An activity can afford this potential only if it is perceived as
such (V ondracek et al., 1986). Simply having an experience does not mean that it has
been useful. For example, the connection between doing chores at home and developing
useful skills for the workforce may not be apparent, therefore the value of this activity as
a career affordance may be limited. Furthermore, negative experiences may serve the
opposite function than what is intended. However, the moderate but signiﬁcant
correlation between career preparation self-efﬁcacy scores and participation (r = .37, p <
.01) suggests that there is some perception of this relationship. Qualitative studies to
probe this aspect of the career environment would deepen our understanding about how

this perception affects children’s beliefs and actions.

Career Preparation Self-Efﬁcacy

Students’ scores on the Career Preparation Self-Efﬁcacy Scale (CPSE) indicated
that they have a high level of self-efﬁcacy for career preparation tasks at this age (M =
4.28; maximum score = 5.00). The ﬁnding of this relatively high level of career
preparation could mean (at least) two things. First, it could be that, in general, children of
the elementary grades can be expected to have an overall high level of efﬁcacy no matter
what tasks are studied. Studies have found that these self-appraisals may start out
unrealistically positive and change gradually as children have more experiences and time

to evaluate their abilities (Bjorklund, 1995). Developmentally, this high level of efficacy

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may serve an adaptive function if it facilitates initiation of activities (Bjorklund, 1995).
The ﬁnding of a high level of interest in career preparation learning experiences also
suggests that children will be likely to initiate and persist in these activities. This is
important because those who avoid activities will not be afforded the opportunity to
develop the skills and beliefs fostered through them. Furthermore, this avoidance could
eventually contribute to the development of a helpless versus a mastery orientation
(Eccles, Wigﬁeld, & Schiefele, 1998). In these instances, children do not attempt
activities because they believe they will not be successful.

Another possible explanation for the high self-efﬁcacy scores is that the
instrumentation needs further reﬁnement. The explanatory power is increased with the
speciﬁcity of the measure (Bandura, 1986; Pajares, 1996). The career preparation self-
efﬁcacy tasks may need to be more speciﬁcally deﬁned. High mean scores and low
variability on the Educational Milestones Self-Efﬁcacy subscale (and on the revised
Educational Tasks subscale) likely contributed to the lack of signiﬁcant differences with
overall Career Preparation Self-Efﬁcacy and person and social context variables.
Likewise, other subscales could beneﬁt from increased speciﬁcity of the career
preparation tasks.

Although students had relatively high levels of efﬁcacy, it does not mean that they
were uniform. It was interesting to note the tasks about which they felt less certain; the
activities that children were least certain about their ability to do were in the Exploration
subscale. This is conﬁrmed by the mean subscale scores. Of the three revised career
preparations self-efﬁcacy subscales, students had the highest mean scores for Educational

Tasks (M = 4.53) compared with Active Exploration (M = 4.02) In fact, the median score

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for Educational Tasks was 4.80, which was close to the maximum possible score of 5.00,
compared to Active Exploration, which had a median of 4.00. Shavelson (1988)
recommends that the median may be a more useful measure of central tendency for
skewed distributions, such as that of Educational Tasks, because of its sensitivity to
extreme scores.

The hypothesis that there would be no gender differences in career preparation
self-efficacy scores was supported. It is not common practice to state a null hypothesis as
the expected outcome; generally, one is looking for differences that distinguish groups in
some way. However, this hypothesis was advanced based on ﬁndings from studies of
older students (middle school through college undergraduates). Like the present study,
these previous studies did not ﬁnd gender differences related to career process outcomes
(e.g., career decision making self-efﬁcacy; Bergeron & Romano, 1994; Fouad & Smith,
1996; Taylor & Betz, I983). The idea that career-related tasks are viewed as gender
neutral (Lent & Hackett, 1987) is supported by this research.

However, when items were analyzed individually, one signiﬁcant gender
difference did surface. Not surprisingly, boys were less certain of their ability to choose a
career in which most of the workers were women. The reverse situation, that is, girls’
uncertainty about their ability to work in a ﬁeld with mostly men, was not the case.
Children’s career preferences, discussed earlier in the chapter, illuminate this distinction
more clearly. Past research has shown that boys tend to have less positive attitudes about
and less interest in careers that are nontraditional for their gender (McKenna & Ferrero,
1991; Tremaine & Schau, 1979), and that they are more restrictive in the occupations

they think are appropriate for women (Hageman & Galdding, 1983). This pattern may

189

exist as a reﬂection of the existing occupational structure, where there is relative
clustering of low-paying jobs that are traditional for women. On the other hand, with the
loosening of gender stereotypes, it may now be more acceptable for girls to make
nontraditional choices than it is for boys.

No other relationships between career preparation self-efﬁcacy and person and
social context variables were hypothesized, nor were there any signiﬁcant differences
found. Therefore, the data from this study do not support the advancement of hypotheses
for signiﬁcant differences in career preparation self-efﬁcacy related to age, racial or
ethnic background, mother’s education, grade, or classroom. This is actually the result
that one would hope to ﬁnd. Differences in children’s level of participation in experiences
is something that is easier to address than the beliefs that they develop about their
abilities. Although they are amenable to change, the development of self-efﬁcacy beliefs

is a complex cognitive process (Bandura, 1986).

Multivariate Analysis: Examining a System of Variables

Up to this point, career preparation self-efficacy has been discussed as a one-
dimensional concept. However, during the development of the self-efficacy measure,
three dimensions—Awareness, Exploration, and Educational Milestones—were used to
conceptualize career preparation. Analysis of a set of variables that includes more than
one outcome variable can be accomplished with multivariate analysis.

First, data reduction was accomplished with principal axis factor analysis. This
technique was employed to reduce the three subscales to items that were more highly

correlated with an underlying dimension, or factor. Orthogonal rotation was used to aid

190

interpretation of factors. Fairly conservative criteria were used to interpret the rotated
factor matrix. The resulting subscales contained fewer items, but they were more
representative of the self-efﬁcacy concepts. The subscales were renamed to better reﬂect
the nature of the remaining items. Two of the three career preparation self-efﬁcacy
subscales, Awareness of Interests and Goals and Active Exploration, were retained for
subsequent multivariate analyses. The Educational Tasks subscale was eliminated as an
outcome variable due to negatively skewed scores and a restricted range of scores.

As a result of the series of MANCOVAS and post hoc univariate analyses
(ANOVA and multiple regression), the hypothesis that a combination of variables better
explains career preparation self-efficacy beliefs was supported. Although signiﬁcant in
both ﬁnal regression models, participation in career preparation learning experiences
made a larger contribution to explaining Active Exploration Self-Efficacy than it did for
Awareness of Interests and Goals Self-Efﬁcacy. While the impact of learning experiences
participation and career exposure were approximately equal for Awareness of Interests
and Goals Self-Efﬁcacy (pr = .174 vs. .163, respectively), participation contributed
approximately one and a half times more to the Active Exploration Self-Efficacy score
than did learning experiences (,6 = .269 vs. .180, respectively).

This ﬁnding makes sense, both conceptually and theoretically, because the items
on the Career Exposure Index assessed indirect sources of inﬂuence, while the Career
Preparation Learning Experiences Checklist asked about actual participation. Self-
efﬁcacy theory contends that enactive mastery experiences are more inﬂuential sources of
self-efﬁcacy beliefs (Bandura, 1997). This indirect versus direct relationship was an

important distinction. For example, while it is possible for parents to intend to inﬂuence

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their children indirectly, as shown in the work of Young and his colleagues (Young &

F riesen, 1992; Young et al., 1988), whether or not the child attends to the information
provided in this manner may depend on a variety of situational factors and cognitive
processes, including self-efﬁcacy. It also lends support to Bronfenbrenner’s
(Bronfenbrenner, 1993, Bronfenbrenner & Morris, 1998) proposition that microsystems
exert inﬂuence because they contain "particular physical, social, and symbolic features
that invite, permit, or inhibit, engagement in sustained, progressively more complex
interaction with, and activity in, the immediate environment" (Bronfenbrenner & Morris,
1998, p. 1013). An important dimension of microsystems is that they focus on
relationships versus tasks.

In summary, this research extended the ﬁndings of previous studies on the process
dimensions of career self-efficacy to elementary-age children. In the end, career-related
variables (career exposure and participation in learning experiences) were the only ones
that remained signiﬁcant in explaining career preparation self-efficacy. A
multidimensional conceptualization of career-preparation self-efficacy also proved to be
useful. Because the ﬁnal model, although signiﬁcant, only explained a small portion of

the variance in self-efﬁcacy scores, additional research is needed.

Implications

Three types of implications are discussed in the following section: (a)

implications for theory, (b) implications for research, and (c) implications for practice.

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Implicationsfor Theory

This research began with the idea that ecological (Bronfenbrenner, 1979, 1988,
1993; Bronfenbrenner & Morris,1998), social cognitive (Bandura, 1986), and self-
efﬁcacy (Bandura, 1977, 1995, 1997) theories were compatible and that their combined
consideration would inform a study of the career preparation beliefs of elementary-age
children more completely. An extensive review of the theories’ key concepts, strengths,
and limitations in Chapter 1 ultimately demonstrated their compatibility. The resulting
ﬁndings from this study based upon this conceptual foundation support the continued use

of this combination of theories in future research.

Implications Related to Ecological Theory

The ﬁnding that only the career-related variables remained in the ﬁnal statistical
models and their differential effects in explaining career preparation self-efﬁcacy can be
linked back to Bronfenbrenner’s (1979) concept of the microsystem, particularly in its
most current formulation (Bronfenbrenner & Morris, 1998). He proposes that human
development takes place "through processes of progressively more complex reciprocal
interaction between an active, evolving biopsychological human organism and the
persons, objects, and symbols in its immediate external environment" (Bronfenbrenner &
Morris, 1998, p. 996). The concepts of "activity," "time," and "increasingly more
complex" (as opposed to repetitive) activity are what distinguish these "proximal
processes . . . as the primary engines of development" (p. 996). Examples of proximal
processes include activities that a child may do alone (e.g., read a book) or with others

(e.g., activities with peers or adults such as athletics, games, or learning a skill). The key

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feature is the active participation involved in processes such as making plans, acquiring
new knowledge, and learning new skills. These features are the very "stuff" of the career
preparation learning experiences examined in this study.

Another theoretical consideration is that the ﬁndings related to career preparation
should be interpreted in light of a life span (i.e, chronosystem) approach to development.
When studying children at a particular point in time, it is important to know what came
before, what they brought to the present situation, and where they are headed next. While
the urgency of career preparation tasks may not be evident in elementary school, the
situation will change before too long. Once those students enter junior high school they
will make decisions, such as what courses to take and what activities to participate in, that
will have consequences for their academic and career futures. Research has shown that
although the transition to junior high school is a normative developmental change, it is
marked by signiﬁcant declines in students’ achievement, esteem, and sense of efficacy
(Midgley, Feldlaufer, & Eccles, 1989). Therefore, students who make this transition in
possession of positive self-efﬁcacy related to career preparation tasks will be well served.
This demonstrates the usefulness of ecological theory for conceptualizing and stimulating

research, designing studies, deﬁning variables of interest, and interpreting the results.

Implications Related to Self-Efﬁcacy Theory

Self-efﬁcacy theory provided useful guidance for constructing the measures
adapted and developed for this study. Likewise, the theory aided in interpreting the
results. The ﬁndings of signiﬁcant differences by classroom may be explained in part by

self-efﬁcacy theory, particularly in terms of teacher self-efficacy, discussed earlier in this

194

chapter. In addition, Bandura (1995, 1997) has recently put forward the concept of
collective efficacy. That is, as a social system the entire staff of a school may collectively
judge themselves to be powerful or powerless in terms of making a difference with
students. The levels of individual, teacher, school, and collective efﬁcacy are relatively
parallel with Bronfenbrenner’s (1979) levels of successively embedded ecosystems,
providing another substantive link between the two theories.

Another implication concerns the use of the theory to extend the results of the
research. Combined with the new knowledge gained from this study regarding
elementary-age children’s career self-efficacy, knowledge of the sources of self-efficacy
beliefs makes it possible to develop educational interventions that foster children’s career
development process.

In conclusion, the theoretical model used in this study was informed by
ecological, social cognitive, and self-efﬁcacy theories. The contributions of each
strengthened the research, and in turn, the ﬁndings can be related back to these theories.
The theories provide explanations of the variables under investigation, thereby aiding our
understanding of what is a complex process. Their usefulness in the present study

provides support for the continued use of this combination of theories.

Implications for Research
The ﬁndings of this study have several implications for future research. Some
recommendations for instrument reﬁnement and additional topics for study have already
been addressed earlier in this chapter. Additional methodological, conceptual, and topical

suggestions are made in this section.

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One very general implication is that the conceptualization and design of a study
are strengthened when it is guided by theoretical considerations. For example, the
recommendation that self-efﬁcacy should be measured in a task-speciﬁc rather than
global manner provided needed guidance for adapting existing measures (Bandura, 1986;
Pajares, 1996). At a more global level, the theoretical underpinnings of this study pointed
out potential research questions, suggested study designs and important variables, aided

in interpreting results, and suggested potential courses of action.

Measurement Issues

With regard to the measures used, high mean scores and a restricted range on the
revised Educational Tasks subscale likely inﬂuenced overall career preparation self-
efﬁcacy scores. This situation also precluded its use as an outcome variable in the
multivariate analyses. Researchers should give consideration to how this dimension is
conceptualized in future studies. Results from other studies Of young children suggest that
perhaps they are giving socially desirable responses to this type of question. That is, they
have been told (sometimes repeatedly) by signiﬁcant adults in their life that they need to
stay in school, study hard, and get good grades. Then they will graduate from high
school, go to college, and get a good job. Consequently, they indicate positive beliefs
about their abilities in these tasks without a real understanding of what is involved. More
speciﬁc measures may more accurately capture self-efﬁcacy beliefs in the area of
educational tasks.

That is not to say that some measure of educational efficacy would not be useful.

If it is "normal" (in the sense of average) to score high on such a measure, those students

196

who indicate low self-efﬁcacy for educational tasks should command our immediate
attention. As discussed earlier in the chapter, consideration should be given to whether
the items in the scale represent the educational tasks speciﬁcally enough to constitute a
useful self-efﬁcacy measure.

Because they were measures designed speciﬁcally for this study, further testing
and reﬁnement of the Career Preparation Learning Experiences Checklist and the Career
Exposure Index needs to occur. For both measures, there may be other experiences and
sources of exposure that need to be on the list. The practice and research dimensions
could be combined by conducting semi-structured interviews or focus groups with youth,
parents, and teachers. Shared agreement between parents and teachers about desired
learning experiences would strengthen mesosystem linkages and increase the

intentionality behind career preparation learning experiences.

Additional Studies

An obvious recommendation is the need to replicate this study with both similar
and different samples. Several suggestions for follow-up studies that would extend the
ﬁndings of the present research have been made throughout the chapter. Both quantitative
and qualitative methodology should be considered, depending on the goals of the
research; a multimethod approach would be useful. For example, qualitative studies could
explore children’s perceptions of the useﬁrlness of career-related learning experiences and
the processes by which exposure and learning experiences affect career preparation self-
efﬁcacy. It also would be helpful to quantify the frequency, duration, and intensity of

career-related exposure and learning experiences. The effects of teacher training,

197

including teacher self-efﬁcacy, could be examined from both quantiative and qualitative
perspectives.

Another line of inquiry would be to assess students self-efficacy beliefs for self-
regulated learning, that is, processes that students use to initiate and direct their efforts to
acquire knowledge and skills (Zimmerman, 1989). According to Zimmerman (1989) self-
regulated learning strategies "are actions and processes directed to acquiring information
or skills that involve agency, purpose, and instrumentality perceptions by learners. They
include such methods as organizing and transforming information, self-consequating,
seeking information, and using memory aids" (p. 329). There is growing evidence of the
importance that these metacognitive processes have for children’s academic performance.
Like career preparation, self-regulated learning reﬂects a process dimension. A likely
hypothesis is that career preparation self-efficacy and efficacy for self-regulated learning
would be positively related. Furthermore, unlike career self-efﬁcacy studies, those for
self-regulated learning have been conducted with elementary school students. It is an
appropriate time in the life span to study these processes, because metacognitive thought
develops during middle childhood (Bjorklund, 1995; Fisher & Bullock, 1984). Therefore,
studies of this nature may also be informed by a cognitive information processing
approach to career development (Peterson, Sampson, & Reardon, 1991; Peterson,
Sampson, Reardon, & Lenz, 1996).

Longitudinal studies are also needed, particularly because self-efﬁcacy represents
self-judgments that are changeable, not ﬁxed traits. This would enable assessment of
possible developmental and as well as situational changes. At a very minimum, speciﬁc

self-efficacy measures can be designed and administered before and after career-related

198

 

programs (Speight, Rosenthal, Jones, & Gastenveld, 1995). It has been suggested that as
the decision-making tasks become more complex, efficacy for these tasks may change.
Research has found that those students who "are less conﬁdent in their ability to complete
the tasks and behaviors required for effective decision making are more likely to report
being vocationally undecided" (Bergeron & Romano, 1994, p. 23).

In conclusion, the results of this study, and theories upon which it was based,
provide a perspective from which to suggest reﬁnements and additional research. Efforts

should continue to examine career-related topics with elementary-age children.

Implications for Practice

The need to start career exploration at the elementary level was a recommendation
echoed throughout various educational reports, studies, and reviews related to school-
to-work and career self-efficacy (AFT, 1997; Hackett, 1995; Trice et al., 1995;
Vondracek & Fouad, 1994). This recommendation needs to become a reality. Therefore a
major program implication of the present research centers around creating
developmentally appropriate educational programs to enhance elementary-age children’s
career development in formal and nonformal settings.

The good news is that self-efficacy beliefs can be changed, and the theory
provides a blueprint for how to do it. The sources of self-efficacy beliefs (e.g., mastery
experiences, role models, verbal encouragement) can be included by design when
planning educational and career interventions. Ecological theory makes the point that
"once is not enough" when it comes to activities that will enhance development.

Processes that encourage development should occur over time and become progressively

199

more complex. It is clear from the present study that the most important thing to do is to
provide children with a career-rich environment at home, at school, and in the
community—in short, wherever they happen to be.

The recommendation to focus on children when they are in elementary school is
particularly important in light of the ﬁndings of signiﬁcant classroom and grade
differences in relation to the number of career preparation learning experiences.
Educators should pay particular attention to the both the content and process of what goes
on in the classroom and other out-of-school learning environments. The educational
system has taken much criticism because of the perception that when they leave, students
are not prepared for life beyond the school walls. That real world consists of complex
tasks requiring a combination of skills, not at test at the end of the chapter for which there
is only one right answer. In order to successfully navigate the school-to-work transition,
students will need to be equipped with equal parts of skills and self-efficacy to face the
challenges that lie ahead.

The literature contains suggestions for doing elementary-level career programs.
Many are good ideas, some are research based, but few are grounded in empirical studies.
These programs will beneﬁt from systematic application of research ﬁndings, as will
research be informed by the implementation and evaluation of these programs. Indeed,
that was the case in the present research. Perhaps it is time to recognize that the research-

practice dichotomy represents an artiﬁcial line in the sand.

200

Using Theory and Research to Guide Program Development

One very general implication is that the conceptualization, design, and delivery of
educational programs are strengthened when they are guided by theoretical considerations
(Hughes, 1994). An example of how one can attend to the theoretical basis for program
development follows.

Design of learning experiences. A career preparation learning experience that has
become popular with the current emphasis on the school to work transition is job
shadowing (i.e., spending time with an employee at the workplace). A connection to self-
efﬁcacy theory, however, would show that job shadowing "wor " because it includes all
the sources of self-efficacy beliefs. The best way to develop efficacy is to directly engage
in experiences which promote mastery, which job shadowing does by going directly to
the workplace. It "works" even better when students have an opportunity to engage in
tasks that let them experience what it is like to have that job. In addition, job shadowing
can broaden horizons by providing exposure to role models; particular attention can be
made to target nontraditional and under-represented careers (e.g., skilled trades). Both
organizers and hosts can provide verbal encouragement at various points in the process. A
key organizational issue for organizers is to make sure they build in advance preparation
and follow-up reﬂections. Such a positive experience would serve to reduce the anxiety
associated with aspects of the world of work, and reﬂection afterwards would allow for a
time to reinforce key components of the experience. Furthermore, while it is possible that
students ﬁnd out that a particular job is not the "right one" for them, the experience is still
valuable because it engages them in the process of ﬁnding out information, a process they

can repeat for other jobs, and in other areas of life. Furthermore, it may contribute to

201

improved self-efﬁcacy beliefs, not only for the speciﬁc job under scrutiny, but for the
tasks that are part of the preparing for every career.

In relation to the present study, a job shadowing experience can deal directly with
many of the tasks for which students indicated the most uncertainty, such as talking with
someone about a job I might like to do, ﬁnd out the salary for a job, and choosing a career
with mostly women. This information can be shared with job shadowing organizers and
hosts so they can incorporate these topics into their discussions with students.

Furthermore, self-efﬁcacy theory suggests a way to measure the impact of the job
shadowing experience. The administration of efﬁcacy measures as a pre- and posttest has
the potential to show how a particular career learning experience has impacted students
by increasing their self-efﬁcacy (Fouad, 1995; Fouad et al., 1997; Speight et al., 1995).
Educational programs may be most effective when they enhance self-efﬁcacy beliefs with
respect to speciﬁc situations, tasks, activities, or processes. Compatibility between the
goals and content of learning experiences and the companion measures would therefore
be essential.

Evaluation. Evaluation of learning experiences is always a concern. Self-efﬁcacy
measurements tied directly to career learning experiences provide a potentially useful
dimension for evaluation. It would seem necessary to use these measures as pre- and
posttests. A challenge with evaluating career preparation learning experiences is the
possibility that they may have a "sleeper" effect. That is, the salience of these experiences
may not be apparent until many years from now. Therefore it is important to
conceptualize both proximal and more distal outcomes of educational programs.

Combining short-term evaluation with longitudinal designs would increase the

202

meaningfulness of evaluation results.

Timing. The ﬁndings from this study and the theories that guided its development
provide guidance about when to intervene in a system that is constantly changing.
Intervention in this context refers to activities and programs that can be thought of as
promoting optimal development or as preventive in nature, not necessarily to correct an
undesirable situation that has already occurred. To be effective, "techniques of
intervention could be chosen or developed that are most responsive to the particular
points of development at which they are to be applied" (V ondracek et al., 1986, p. 157).
F urtherrnore, a developmental intervention "means that one intervenes not to inﬂuence a
static point in life; rather one intervenes to affect a change trajectory—one potentially
encompassing the individual’s entire life course" (p. 158). The contextual human
development approach outlined by Vondracek and his colleagues directs us to look at
earlier periods in the life span than have typically been the target of intervention efforts
and provides a framework from which to approach programs for students in the
elementary grades. ’

Elementary level programming offers a great opportunity to expand children’s
awareness, to stimulate interest in the many options and opportunities available, and to
relate current interests to the future. Before most formal career development programs
even begin, students have expressed career preferences. If school-to-work efforts continue
to be directed to youth at the high school level, many students will have already
eliminated certain occupational clusters from consideration. Perhaps much of the focus
has been on high school because there the transition from school to work is imminent.

However, as a report by the American Federation of Teachers (1997) has noted, high

203

schools cannot be expected to "magically" provide a solution to this situation. Those who
are "serious about preparing youngsters for work and citizenship when they graduate
from high school must make sure changes extend all the way down to the elementary
grades" (p. 6).

Role of adults. Adults play an important role in promoting career development
efforts, by making sure that the opportunities for children to engage in career exploration
are available, and furthermore, that children’s needs and interests inform these programs.
This study documented that third- through ﬁfth-grade students had a high level of interest
in career preparation learning experiences. They favored those that involved active
exploration, yet that is the area that they feel least certain about their ability to do. As
Bergeron and Romano (1994) suggested, those who work with students regarding career
issues must not only look at their interests and abilities, but the sense of efficacy they
bring to the decision making process itself, and to the consideration of a wider range of
alternatives.

While children in the school-age years have given thought to their future, this is
not the time to make push them into decisions that restrict their future career exploration
and attainment. While it is important to assist children in gaining information about jobs
that interest them, the focus should be on developing attitudes and skills that will be
important for their success in whatever path they choose.

Furthermore, adults need to be the ones who build mesosystems that link
important contexts of development—families and schools, schools and workplaces,
families and communities. This Shared importance placed on career preparation

experiences would strengthen the mesosystem for children, thereby fostering their

204

development.

Summary of Program Development Implications

This research has pointed out several important implications for those who work

with elementary-age children. These recommendations are applicable in formal settings

(e.g., school classroom), nonformal settings (e.g., Extension 4-H and after-school

programs), and informal settings (e.g., families).

1.

Capitalize on the high level of interest that children exhibit for career-
related experiences in their elementary school years. This interest indicates
that children are ready for career development experiences, provided these
experiences take their developmental characteristics and needs into
account. Children have not had the opportunity to do all of the activities in
which they are interested: This represents a missed opportunity. At this
age, children still have an enthusiasm for participating in a wide variety of
activities. This may not be the case when they reach adolescence.

Focus on activities that involve active exploration. This accomplishes two
goals: (a) active learning experiences are developmentally appropriate, and
(b) children indicated the lowest levels of self-efﬁcacy for active
exploration tasks. Thus, actually engaging in these tasks (such as visiting
worksites and talking to workers) should serve to increase their self-
efﬁcacy.

Provide exposure to career role models. While this strategy is one that is

often suggested for girls, boys should not be left out of this experience.

205

Both boys and girls have knowledge of a fairly narrow range of career
possibilities. In this study boys were unsure of their abilities to select a
career in which most of the workers were women. There are many careers
that both boys and girls were not choosing (e.g., science and engineering
and skilled trades). Therefore, they should be exposed to a wide variety of
role models, this exposure should happen more than once (especially
because their interests may change), and it should happen in more than one
way (e.g., through the media, in the classroom, at the worksite).
Emphasize community service and volunteer experiences. While they
cannot join the regular workforce, these represent real ways that children
can get work experience. There are short-term and long-term beneﬁts.
Children do not have to wait until they are older (like they are told they
must for so many other things) for these experiences to be meaningful and
useful for their development. Many children in elementary years have not
had the opportunity to participate in community service or volunteer
experiences. However, they require adults to provide some structure and
supervision in order to be meaningful learning experiences. Community
service and volunteer experiences have the concomitant beneﬁt of
preparing children to be good citizens.

Capitalize on children’s exposure to the media. Children indicated that
most of their exposure to their career preference came ﬁom the media.
This presents opportunities to use the media as a teaching tool, for

example, as one way to provide exposure to role models.

206

6. Increase connections between the school, community, employers, youth
organizations, and families. Each part of the system has a role to play, and
this can be strengthened when they work together. In order to provide a
career-rich environment, experiences need to happen with some frequency,
duration, and intensity; there are more than enough program development
opportunities to go around. This can ensure that experiences happen by

design rather than by default.

Contributions of This Study

This study made several theoretical and practical contributions by contributing to
the body of knowledge related to career development and self-efﬁcacy. First, the study
was informed by two strands of theories, ecological and social cognitive/self-efﬁcacy.
Together, they provided a strong theory base from which to design and carry out an
investigation related to career preparation of elementary-age children. Furthermore, this
age group was younger than previously studied in relation to process dimensions of
career self-efficacy. This study extended the usefulness of career self-efﬁcacy to this
younger age group.

The ﬁndings of this study lend to support to aspects of ecological, social
cognitive, and self-efficacy theories. Classroom differences that were signiﬁcant in
univariate, but not multivariate analyses, can be cast in terms of a microsystem inﬂuence.
The potential difference in these environments needs to be examined. Although the data
from this study do not provide an explanation for these differences, ecological and social

cognitive/self-efficacy theories provide the means to conceptualize and investigate them

207

in future studies.

Two measures were created for this study. Because it was the variables created
from these measures, career exposure and participation in career learning experiences,
that produced signiﬁcant relationships with career preparation self-efﬁcacy, studies using
these measures, or reﬁnements of them, should be undertaken. The need to include a
measure of learning experiences is supported by both ecological and self-efficacy
theories.

The theories guiding this study provided an organizing framework from which to
think about linking theory, research, and practice. It must be remembered that any
research is a product of its social and cultural context. This effort to examine career
preparation self-efﬁcacy comes at a time when successfully making the school-to-work
transition has become a concern of educators, parents, employers, and youth themselves.
While the model school-to-work system begins when school begins, younger children are
left out of the picture when emphasis is placed only on older students. Reality has not yet
caught up to this ideal. It is hoped that the results of this research will inform future
career development programs.

Perhaps the most signiﬁcant ﬁnding is that the variables that were signiﬁcant in
explaining career preparation self-efﬁcacy are those about which something can be done.
Educators, in both formal and nonformal settings, should make it a priority to concentrate
their efforts on creating career-rich environments for children and to begin doing it at an

early age.

208

 

APPENDIXES

Appendix A

Approval Letter from Lansing School District

LANSJN_G_
S C 11 0g.
_DISTRICI_
Cornmlrred to Quality
March 17, 1998

Joanne Keith, Professor
203 Human Ecology
Michigan State University
East Lansing, MI 4882A

. Dear Dr. Keith: '

In regard to the proposed study, “An Exploratory Study of Elementary-age Children’s
Career Self-Efﬁcacy”, the request to conduct the study in the Lansing School District has
been approved by the District’s Research Review Committee. The review process and
approval also included the principals from the Young Spartan Schools who are
participating in the study. The following comments apply to the study:

The study is viewed as an wrgolng part of curriculum development and educational
activities cornucted in the classroom. Slaﬂ and student participalton tn the study are
strictly voluntary. Passive consent letters will be sent by the principal of the
pariclpatln'g .schools to students, parents or guardians. Parents or guru-(lions who do not
want their child to.participale will notify the school In writing. Please contact me for

teclmtcol assistance regarding student demographic data and the administration of the
my.

if you have any questions or need additional information, please give me a call (325-

Marian Phillips

-- Supervisor

c: Research Review Committee
Principals, Young Spartan Schools ‘

Research. Evaluation & Pupil Accounting
500 W. Lenawee' St.
Lansing. Michigan 48933
An Equal Opportunity District

209

Appendix B

Human Subjects Approval Letter

MICHIGAN STATE

UNIVERSITY

March 19, 1998

TO: Joann. Knith
2 Paolucci Building

23: IRE : 95-192
TITEB: DBSIQI AND EVALUATION 01’ CAR!“ DEVELOPMENT
CURRICUDUH FOR ELEMENTARY SCKOOL-AOB CHILDREN

(K-Sl
micron mam: oz 09/93
arm nan: o: ia/sa

The university Committee on Research Involving Human Bub ecta'(UCRIEa)
review or this roject is complete. I am pleased to adv no that the
rights and wel are of the human subjects appear to be adequately
rotected and'methods to obtain informed consent are appropriate.
Egeretore, the UCRIHS approved this project and any revisions listed
O'VO. .
UNDER FEDERAL REGULATIONS PROTECTING HUMAN SUBJECTS OF RESEARCH,
UEﬁIHS MRI HOT APPROVE "PASSIVE" CONSENT PROCESSES. INSTEAD IT IS
APPROVING THIS PROJECT DUE TO ITS UNDERSTANDING FROM DR. KB THAT
THE LANSING SCHOOL DISTRICT, THROUGH HS. MARION PHILLIPS, IS PﬁOVIDING
CONSENT FOR THE STUDENTS TO PARTICIPATE.

illulhht UCRIHB approval is valid for one calendar year, beginning with
the approval date shown above. Investigators plann to
continue a proje t be and one year must use the reen renewal
form (encloaed w th e original a roval letter or when a
project in renewed) to seek u date certification. There in a
maximum of four ouch expedite renewals enable. Investigate
via to continue a reject beyond the time need to '
again or complete rev aw.

REVISIONS: UCRIHS must review an changes in procedures involving human
eubje to, rior to ' tiation at t e e. If this is done
the t me o renewal, please use the gieen renewal torn. To
revise an approved protocol at an c or time during the year‘
send your.written re eat to the CRIBS Chair, requesting rev:
approval and referonc pg the project'a IRE # and title. Inclt
in ur roqueat a den r ption of the change and any revised
ino rumenta. consent arms or advertisements that are applical

 

PROBLIIS/
Clhlulsr Should either of the follo arise during the course of the
work, invooti atora must noti YCRIHS promptly: (1) rohlem:
(unexpected e do effects. comp a nta, e c.) involving uman
subjects or (2) changes in the r search environment or new
information indicating greater r ax to the human nub ects they
existed when the protocol wee previously reviewed an approve:
I! we can he of any future help. lea-e do not hesitate to contact us
at (517)355-2180 or Eﬂx (517ld 2- 171.
Sincerely.
mounaunnMnn
248 Miami!!! mm W
anummumuwm
48324-1oa6

mnnsnw
FAX’ 517M324 171

 

cc: Patricia Farrell
Theresa Ferrari

Marion Phillips
runnmmaununmu

umanauouMwny
Ere-Ibo: mm.

nautmaMnmean
inlaunnommwm

210

Appendix C

Sample Follow-Up Letter to Teachers

[Date]
Dear [Teacher’s Name]:

I am writing to follow up with our telephone discussion about the Careers and Me
survey. I really appreciate your willingness to participate in this project.

This is to conﬁrm that we’ve agreed to [date] at [time] for me to come to your class to do
the survey.

Here’s a review of the steps involved:

Before the scheduled date:
—Distribute consent letters to the children in your class.
-Collect any responses and put them in the mailbox in your school, where
I will pick them up.
-Tell the class that I am coming. Be sure to stress that this is not a test, it
is to ﬁnd out what they think. What is most important is that they are
attentive and answer honestly.

When I come to your class:
—I will come to the class and provide copies of the survey.
-1 will read the survey to the children.
-I would like to allow an hour to do the survey (so I can introduce myself
and the survey and they can take a break in the middle if needed).
—Because you know the children, it will be helpful for you to be in the
class when I do the survey.

I will be compiling the survey data from the participating classrooms. I will the share the
results of the survey with the [schools] and use the data to write my dissertation. Thanks
very much for your help with this survey. Please do not hesitate to contact be with any
thoughts or questions. I can be reached at [(phone)], [(fax)], or [(e-mail)].

211

Appendix D

Consent Letter

[Date]

Dear Parent or Guardian:

I am writing to let you know about a survey we plan to conduct in your child’s classroom.
The survey, called Careers and Me, will help us to learn more about what children do and
what they think about as they prepare for their future in the world of work.

The [school district] is working with the [program] on this project and has approved the
use of this survey. Your child will do the survey in his or her regular classroom. The
survey will be read to the class and your child’s teacher will be in the classroom. The

survey takes about 45 minutes to do.

A copy of the survey is on ﬁle in the school ofﬁce if you would like to review it. If you
have any questions please feel free to contact me.

If you do not want your child to participate in this survey we need to hear from you.
Please return the attached sheet by Idate |. While it would be helpful for you to let us
know either way, if you do not return this sheet, we will assume that it is OK for your
child to do the survey.

Thank you very much.

Sincerely,

School Principal

212

 

Careers and Me Survey

OK to participate Not OK to participate

Child’s Name

 

Class

 

Parent or Guardian’s Signature

 

Date

 

Please return to your child’s teacher by ldatel.

213

Appendix E
Instructions for Careers and Me

Introduction

This survey is about jobs and careers and what you think about them. There are no right
or wrong answers to these questions. It is just about what you think, so maybe you will
answer differently than other students in the class. That’s OK. You don’t have to have the
same answers.

I will read each of the question for you. After each question, there is a set of responses.
When I read the question, you will think about which of these answers is the one for you.

Part 1 (Learning Experiences) Instructions (to be read to student):

For this set of questions I am going to ask you about what you have done and what you
would might like to do in the area of jobs and careers. After I read each activity, you will
answer two questions. First, you will tell me if you have done the activity or not by
circling the word "yes" or "no." The second question is whether you would like to do the
activity or not. I want you to circle either the word "like" or "not like." So you will have
two circles for each activity listed.

Let’s do a sample question. The ﬁrst question says: "Go to a Spartan basketball game."

Is this something you have done? If you have, circle the word "yes," or if this is
something that you have not done, circle the word "no." Is going to a Spartan basketball
game something you like to do circle "like." If it is something that you do not like to do,
then circle "not like." Even if you haven’t done it, you can answer whether you would like
or would not like to do it. So it doesn’t matter whether you answered yes or no, you can
still answer the second question.

Does anybody have any questions? There are 15 questions in the section. Each one of
them is in the same format as the sample question we just did.

Part 2 (Self-Efﬁcacy) Instructions (to be read to students):

The ﬁrst set of questions asked you to think about what you did, and about your opinion
(whether or not you would like to do something). This next set of questions is different.
These question ask you to think about how sure you are that you can do something. Some
things you may have done, but other questions ask you to think about things you have not
done yet. So think about how sure you are that you could do it.

214

 

Does everybody understand? Let’s do a practice question to be sure. The ﬁrst question
says: "How sure are you that you can ride a bike?"

If you are sure that you can ride a bike, mark the square.

If you are pretty sure that you can ride a bike, mark the circle.
If you think that maybe you can ride a bike, mark the star.

If you don ’t think you can ride a bike, mark the flower.

If you are sure you can ’t ride a bike, mark the diamond.

Remember that you will make only one mark for each question. Let’s try one more. The
next question says: "How sure are you that you can drive a car?" This is something you
have done yet. So tell me if it is something you think you could do.

If you are sure that you can drive a boat, mark the square.

If you are pretty sure that you can do this, mark the circle.

If you think that maybe you can do this, mark the star.

If you are pretty sure you can ’t drive a boat, mark the ﬂower.
If you are sure you can ’t drive a boat, mark the diamond.

Does anybody have any questions? There are 27 questions in this section. Let’s begin.
[Responses will be read after each question as necessary]

215

10.

11.

12.

Appendix F

Careers and Me

 

 

My name is
Iam a Cl Girl Cl Boy
I am year old.
School
_[1] _[5]
_[2] _[6]
_[3] _ _[7]
_[4] _[81
Grade
3m 4m 5m

 

I know what someone does who has this job. LI Yes

I know the skills I will need to do this job. J Yes

Someone in my family has this job/career. J Yes

I know someone in my school, neighborhood,
or community who has this job/career. J Yes

I know someone on TV who has this job/career. J Yes

Someone that I read about has this job/career. J Yes

216

is a job or career I think I might like to do.

D No D Not sure

C] No C] Not sure

DNo

DNo

CINo

CINo

 

Careers and Me

 

Is this something
you have done?

Is this something you
would like to do?

 

1. Do a job (chore) at
home.

 

2. Do a job in your
classroom or school.

  

 

3. Do community
service or a
volunteer job in
your community.

 

 

4. Make a list of things

\ . you are good at
1‘ and like to do.
1 -~

 

 

5. Make a list of things you
would like to learn or

improve.
I l

 

6. Talk to a parent or

teacher about a job you . ‘7

think you'd like to do.

  
 

 

 

7. Hear a guest
speaker or talk
with someone
about their job.

 

 

YES

YES

YES

YES

YES

YES

YES

NO

NO

NO

NO

NO

NO

NO

 

Like

Like

Like

Like

Like

Like

Like

Not like

Not like

Not like

Not like

Not like

Not like

Not like

 

217

 

 

Is this something
you have done?

Is this something you
would like to do?

 

8. Go on a ﬁeld trip
(to study about jobs)
or visit a workplace
(to see someone
work at his or her job).

a»

 

9. Go to the library
to ﬁnd out
information about a

 

 

job or career.

 

 

10. Read a book
about what people do
for their job.

 

 

11. Find out about
what you would have

to do (or the skills you 1 1‘

would need) to get a
job that interests you.

 

12. Learn about
subjects you would have
to study in school
for a job that you
are interested in.

  

 

13. Do a report or
project to learn more
about a job that you
might like to do.

  

 

14. Go to an after-
school activity that
helped you learn
about jobs or job
skills.

 

 

15. Visit a college
campus.

 

 

YES

YES

YES

YES

YES

YES

YES

YES

NO

NO

NO

NO

NO

NO

NO

NO

 

Like

Like

Like

Like

Like

Like

Like

Like

Not like

Not like

Not like

Not like

Not like

Not like

Not like

Not like

 

218

 

Careers and Me

 

 

 

 

 

How sure are you that you can do the following . . .

1. Can you ﬁgure out what you are good at doing?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

2. Can you figure out what things you are interested in?

Cl O 72: ﬁ 9

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

3. Can you study hard for your classes?

Cl C) a“: 6% 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

219

4. Can you get good grades in school?

B O a“: Q Q

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

5. Can you make a plan of your educational goals for the next
three years?

Cl C) a“: Q 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

6. Can you decide what you value most in a job or career?

Cl O 72: Q Q

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

7. Can you figure out what job or career would be best for

you?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

8. Can you choose a job or a career that will fit your

interests?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

9. Can you select one job from a list of possible careers you
are considering?

Cl 0 i‘r 6% Q

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

220

 

10. Can you find information in the library about a job or
career you are interested in?

Cl O 71‘: Q 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

11. Can you use the computer to find out information about a
job or career you are interested in?

D O a“: Q 9

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

12. Can you find out what subjects you would have to take?

Cl 0 a“: 1% Q

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

13. Can you describe the job skills of a career you might like

 

to enter?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

14. Can you find out how much money people make in a job or

career?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

15. Can you decide what kind of schooling you will need to
achieve your career goal?

Cl O 72: ﬁ 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

221

 

16. Can you talk with a person who already works in the type
of job or career you are interested in?

Cl C) i‘r Q 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure 1 can’t

17. Can you visit a workplace of a person who has a job or
career that interests you?

Cl 0 32: Q 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

18. Can you get some real experience, like volunteering or
community service, that will help you get some skills or
decide if you like a particular kind of job?

Cl 0 a“: 6% Q

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

19. Can you ﬁnd someone who can help you think about what
you want to do with your future?

Cl 0 if: Q1 9

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

20. Can you take math in high school?

Cl 0 a“: ﬁ 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

21. Can you graduate from high school?

Cl 0 71‘: Q Q

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

222

22. Can you choose a career that you think is right for you no
matter what others say?

Cl 0 a“: €69 9

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

23. Can you stick to a career choice even if others did not
approve of it?

C] O 71} ﬂ 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

24. Can you choose a career in which most of the workers are

men?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

25. Can you choose a career in which most of the workers are

 

women?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

26. Can you go to college or other education beyond high

 

school?
I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

27. Can you find a good job?

Cl O 72: $3? 0

I’m sure I can I’m pretty sure I can Maybe I can I’m pretty sure I can’t I’m sure I can’t

 

223

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