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«L LIBRARY
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dissertation entitled

VARIABLES AFFECTING THE POST HIGH SCHOOL
OUTCOMES OF STUDENTS WITH LEARNING
DISABILITIES

presented by
Carolyn Anne McAllister

has been accepted towards fulfillment
of the requirements for the

PhD. degree in Social Work

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VARIABLES AFFECTING THE POST HIGH SCHOOL OUTCOMES OF
STUDENTS WITH LEARNING DISABILITIES

By

Carolyn Anne McAllister

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
School of Social Work

2008

ABSTRACT

VARIABLES AFFECTING THE POST HIGH SCHOOL OUTCOMES OF
STUDENTS WITH LEARNING DISABILITIES

By
Carolyn Anne McAllister

The focus of this study is to examine the educational and employment related
outcomes of individuals with learning disabilities as they leave high school and make
choices about postsecondary education and employment. First, the paper presents an
overview of the issues and ways that social workers can support adolescents with
learning disabilities as they decide on educational and career goals, and transition from
public school systems. The paper provides a theoretical background on the definition of
learning disabilities and some of the recent changes in this disability category. Next, the
paper reviews research on the individual, familial, and social impacts of a learning
disabilities diagnosis. The paper then provides an overview of researched interventions
for adolescents with learning disabilities.

This study also examined demographic, economic, educational, and other relevant
variables through the National Longitudinal Survey of Youth, 1997 cohort. One way
ANOVAs were completed to determine the ways in which the sample of individuals with
learning disabilities were similar or different from persons with other impairments and
persons with no identified impairment. Findings showed that the educational and income
outcomes seen in previous research after one to three years after leaving high school

continue as students are three to seven years out of high school.

Lent, Hackett, and Brown’s (1994, 1996, 1999) Social Cognitive Career Theory
(SCCT) was then utilized to identify potential factors influencing students with learning
disabilities educational performance. Using the National Longitudinal Survey of Youth,
1997 Cohort (NLSY97), hierarchical regression analyses were completed for individuals
with a diagnosed learning disability, for individuals with other identified learning or
medical impairments, and for individuals with no identified impairments. Support for the
SCCT model was found in all three groups of participants, although the individual
variables influencing each group vary.

Lastly, implications for this research in the areas of social work practice, research
and policy are discussed. Areas for fiiture research are discussed, and the strengths and
limitations of this study are reviewed. The importance of including assessment and
interventions for adolescents and young adults with learning disabilities in all areas of

social work practice is emphasized.

Cepyright by

CAROLYN ANNE MCALLISTER
2008

DEDICATION
This dissertation is dedicated to the memory of two dear friends and colleagues,
Trudy J orgenson and Melissa Valentine Hill Davis. Their passion for work with persons
with disabilities, and their passion for life, even when facing the end of their lives,
continually inspires me. It is also dedicated to the memory of my aunt, Susie Schaaf, who

lived as she wanted to the end.

ACKNOWLEDGEMENTS

Education is always a group effort, and I have been very fortunate to have the
love and support of a number of people. I must start by thanking my mom, Cheryle
Christiansen. She has been my role model for many things, and whenever I think of the
kind of teacher I want to be, I think of her. I also have to give much love and thanks to
my grandparents, Keith and Carolyn Schaaf, step—father Dean Christiansen, sister Katie
Christiansen, and my sister Jennifer McAllister and the rest of her family: Phil, Duncan
and Ian. You all are my foundation, and I could not have done this without your
continued support and encouragement.

Thanks also must go out to those people who I have learned from at MSU. Rena
Harold, my chair and boss, has been an endless advocate, mentor, and supporter for all of
my time in the PhD program. Thank you! Thank you! My committee members, Victor
Whiteman, John Kosciulek, and Kyunghee Lee have also been very helpful in this
process. To my fifth unofficial committee member, Marya Sosulski, for years of reading
my work and sharing your work with me. Special thanks go out to my colleagues: Joan
Ilardo, Nancy Gray, Julie F arman, Brian Ahmedani, and Karen Newman for years of
great teamwork and support.

I want to acknowledge the mutual suffering, I mean process and support, of a
number of doctoral student colleagues. Thank you to my dissertation support group:
Adrienne Adams, Marisa Beeble, and Lauren Lichty. It has been such a help to see that I
am not alone in my frustrations, joys, exhaustion, and accomplishments over the years.
Countless thanks must also be given to my Ph.D. cohort: Francis Akakpo, Muthoni

Imungi, Ric Kobayashi, Sally Pelon, Judi Ravenhorst-Meerman, and Kimberly Steed.

vi

Kim and Rie in particular have been two of the best cheerleaders, friends, and second
family members I could ever hope for. J ayden Steed provided me a reminder that life
goes on, even in a doctoral program. It is friends like these that make finishing my
dissertation a little sad, as it means I am moving on.

A number of friends from California also continue to give me support and
encouragement over the now seven years I have been out of state. Thank you to Chris
Bolandian for the two or three times a week calls just to “say hi”. Thanks also to Carmel
Chan and Nancy Heinschel, two friends who have walked this dissertation path before me
and were always happy to commiserate. Also, thank you to the number of friends that
stayed with me over the years from move to move- Betsy, Brian, J enafir, Denice, Rachel,
and John.

Finally, I need to express my endless thanks to Hiraku Kobayashi. He has been
the kind, cairn, and loving presence I was looking for. Every time I was ready to drop out
of the Ph.D. program and join the circus, he would gently remind me that I loved to study
and that I wanted to finish this. His dedication to his own studies kept me focused on my

own over the past two years. I am so glad to have you!

vii

TABLE OF CONTENTS

LIST OF TABLES .............................................................................................................. xi
LIST OF FIGURES .......................................................................................................... xiii
CHAPTER ONE
INTRODUCTION ............................................................................................................. .1
Chapter Two Overview ............................................................................................ 4
Chapter Three Overview .......................................................................................... 5
Chapter Four Overview ..................................................................... 6
Conclusion ............................................................................................................... 8
References .................................................................................... 9
CHAPTER TWO
WORKING WITH ADOLESCENTS AND YOUNG ADULTS WITH LEARNING
DISABILITIES: A SOCIAL WORK PERSPECTIVE ..................................................... 11
Abstract .................................................................................................................. 1 1
Introduction ............................................................................................................ 1 1
Definition of Learning Disabilities ........................................................................ 14
Aptitude/ Achievement Discrepancy ......................................................... 16
Response to Intervention ........................................................................... 17
Prevalence of Learning Disabilities ........................................................... 18
Impacts of Learning Disabilities ................................................................ 19
Academic and Cognitive Issues ................................................................. 20
Social Skills Issues ..................................................................................... 21
Family Issues ............................................................................................. 23
Psychosocial Issues .................................................................................... 24
Self-concept............ . ........27
Locus of Control ..................................................................................... 27
Self-efficacy ............................................................................................... 27
Self-determination ..................................................................................... 28
Summary of Issues from a Social Work Perspective ................................. 29
Interventions for Adolescents with Leaming Disabilities ..................................... 32
Creating School Enviromnents for Positive Vocational and
Educational Outcomes ............................................................................... 33
Social Skills Training ................................................................................ 34
Self-determination Traimng 36
Conclusion ........................................................................................................... 37
References ............................................................................................................. 40

viii

CHAPTER THREE

UTILIZATION OF THE NATIONAL LONGITUDINAL SURVEY OF
YOUTH TO STUDY POSTSECONDARY OUTCOMES

FOR ADOLESCENTS WITH LEARNING DISABILITIES:

PRELIMINARY FINDINGS ............................................................................................ 48
Abstract .................................................................................................................. 48
Introduction ............................................................................................................ 48

Review of Previous Research Published Since 1990 ................................. 50
Summary of Findings ................................................................................. 53
Research Questions .................................................................................... 55
Method ................................................................................................................... 55
Study Population ........................................................................................ 55
Identification of the Sample ....................................................................... 57
Results .................................................................................................................... 59
Variables ................................................................................................. 59
Demographic Data ..................................................................................... 60
Vocational Data ......................................................................................... 62
Educational Data ........................................................................................ 64
Academic Expectations .............................................................................. 68
Other Variables Identified in Previous Research ....................................... 69
Limitations ............................................................................................................. 72
Discussion .............................................................................................................. 74
References .............................................................................................................. 92
CHAPTER FOUR

APPLICATION OF THE SOCIAL COGNITIVE CAREER THEORY
TO THE EDUCATIONAL OUTCOMES OF ADOLESENTS WITH

LEARNING DISABILITIES ............................................................................................ 97
Abstract .................................................................................................................. 97
Introduction ............................................................................................................ 97

Social Cognitive Career Theory (SCCT) ................................................. 100
SCCT Career/ Academic Choice Model .................................................. 103
Research Questions and Hypotheses ...................................................... 103
Method ................................................................................................................. 106
Study Population ...................................................................................... 106
Identification of the Sample ..................................................................... 107
Identified Variables for Analysis ............................................................. 109
Results .................................................................................................................. 1 l3
Descriptive Statistics ................................................................................ 113
Missing Data ............................................................................................ 114
Correlations .............................................................................................. 1 14
Model Blocks for Hierarchical Regression .............................................. 115
Results of Hierarchical Regression .......................................................... 116

ix

Discussion ............................................................................................................ l 19

Limitations ............................................................................................... 121
Implications for Practice with Adolescents with
Learning Disabilities ................................................................................ 123
Recommendations for Future Research ................................................... 125
References ............................................................................................................ 138
CHAPTER FIVE
CONCLUSION ................................................................................................................ 144
Summary of Manuscripts ..................................................................................... 144
Implications for Social Work Practice ................................................................. 147
Implications for Social Work Education .............................................................. 149
Implications for Future Research ......................................................................... 150
Addendum: Reflections on a Multiple Manuscript Dissertation ......................... 153
References ............................................................................................................ 154
APPENDIX A
CERTIFICATION FOR USE OF AN APPROVED PUBLIC
DATA FILE ..................................................................................................................... 160

LIST OF TABLES

Table 3.1

Sample size, Sex, Race/ Ethnicity, Age and Income of Participants ................................. 77
Table 3.2

Income Earned by Participant in Categories ...................................................................... 78
Table 3.3

Highest Grade Completed and Highest Degree Completed .............................................. 79
Table 3.4

Grade Point Average in High School ................................................................................. 80
Table 3.5

Crosstabulation of Gender by Income Categories ............................................................. 81
Table 3.6

Means and Standard Deviations of Economic and Educational Variables ........................ 82
Table 3.7

Means and Standard Deviations of Participant Interview Measures ................................. 83
Table 3.8

ANOVA Table of Income, Employment Expectations, Highest Grade Completed ......... 84
Table 3.9

ANOVA Table of GPA, ASVAB, and Academic Expectations ....................................... 85
Table 3.10

ANOVA Table of SAT Scores and Mental Health Scale .................................................. 86
Table 3.11

ANOVA Table of School Fairness Scale and People Available for Support .................... 87
Table 3.12

Correlations of Highest Grade Completed, Income with Age ........................................... 88

xi

Table 4.1
Means and Standard Deviations of Dependent Variable,

Self-Efficacy Measure And Demographic Variables ...................................................... 126
Table 4.2

Means and Standard Deviations of Proximal and Distal Contextual Variables .............. 127
Table 4.3

Means and Standard Deviations of Learning Experience Variables .............................. 128
Table 4.4

Hierarchical Regression Model: Individuals with Learning Disabilities (LD) ............... 129
Table 4.5

ANOVA Table of Final Regression Model for

Individuals with Learning Disabilities (LD) .................................................................... 130
Table 4.6

Coefficients for the Final Model for Individuals with Learning Disabilities (LD) ......... 131
Table 4.7

Hierarchical Regression Model: Individuals with No Impairment (N I) .......................... 132
Table 4.8

ANOVA Table of Final Regression Model for Individuals with No Impairment (NI)...133
Table 4.9

Coefficients for the Final Model for Individuals with No Impairment (NI) ................... 134
Table 4.10

Hierarchical Regression Model: Individuals with Other Impairments (OI) .................... 135
Table 4.11

ANOVA Table of Final Regression Model for

Individuals with Other Impairments (OI) ........................................................................ 136
Table 4.12

Coefficients for the Final Model for Individuals with Other Impairments (OI) ............. 137

xii

LIST OF FIGURES

Figure 1
Frequency Distribution of Highest Grade Completed, LD group ..................................... 89
Figure 2
Frequency Distribution of Highest Grade Completed, NI Group ..................................... 90
Figure 3
Histogram of the Highest Grade Completed, 01 Group .................................................... 91

xiii

CHAPTER ONE

INTRODUCTION
Learning disability (or specific learning disability) is one of the eleven categories
of disability to receive services through the Individuals with Disabilities Education Act
(IDEA). Within IDEA, specific learning disability is defined as:
(A) In general.--The term ‘specific learning disability' means a disorder in
1 or more of the basic psychological processes involved in understanding
or in using language, spoken or written, which disorder may manifest
itself in the imperfect ability to listen, think, speak, read, write, spell, or do
mathematical calculations.
(B) Disorders included.--Such term includes such conditions as perceptual
disabilities, brain injury, minimal brain dysftmction, dyslexia, and
developmental aphasia.
(C) Disorders not included.--Such term does not include a learning
problem that is primarily the result of visual, hearing, or motor disabilities,
of mental retardation, of emotional disturbance, or of environmental,
cultural, or economic disadvantage. (IDEA 2004, § 602).
The best estimates that are available for the population of persons with learning
disabilities come from the school systems, as this is not a question asked in the US.
Census (“Disability status”, 2003). In the 1976/1977 school year, 1.8% of the K-12
student body in the United States received special education services based on a

diagnosis of specific learning disability. By the 1980/1981 school year, the proportion

grew to 3.6% percent of the K-12 student body. Since the 1994/1995 school year,
between 5.4% to 6.2% of the student population has had a diagnosis of a specific learning
disability. In the 2006-2007 school year, this equaled approximately 2.7 million children,
and is approximately half of all students receiving special education services through
IDEA (“Children and Youth with Disabilities”, 2008). The National Research Center on
Learning Disabilities estimates that 6% of the population has a learning disability in the
area of reading alone (NRCLD, 2007).

The Individuals with Disabilities Education Act requires that students with special
education services receive education in the least restrictive environment, be included in
measures for annual yearly progress if at all possible, and receive yearly transition related
planning to assist the student set and meet goals for postschool life, particularly in areas
such as vocation and education (IDEA 2004). These requirements are intended to prepare
students with learning and other disabilities for work or further education after leaving
the public school system.

Despite the measures of special education departments, postsecondary institutions,
and other professionals working with individuals with learning disabilities to promote
vocational and educational opportunities, students with learning disabilities continue to
have poorer post high school outcomes than students without this diagnosis.
Approximately 25% of students with learning disabilities in public school will leave
school without a high school diploma (Wagner, Newman, Cameto, Garza & Levine,
2005). Approximately one in three students with learning disabilities will go on to receive
some postsecondary education, but only a small proportion of that group will complete

any postsecondary degree (Blackorby & Wagner, 1995; Murray, Goldstein, Nourse &

Edgar, 2000; Rojewski, 1999; Sitlington & Frank, 1990; Wagner et al., 1991; Wagner et
al., 2005). In addition, students with learning disabilities typically attend two-year
colleges or vocational programs rather than four-year institutions (Wagner et al., 2005).
Although individuals with learning disabilities will work at rates similar to the general
population, they will be engaged in lower wage work with fewer benefits; women are
affected, in particular (Murray et al., 2000; Rojewski, 1999; Sitlington & Frank, 1990;
Wagner et al., 2005). However, for those students with learning disabilities who do
graduate from a postsecondary institution, their wages have been reported to be
commensurate to those of students without the diagnosis (Dickinson & Verbeek, 2002;
Murray et al., 2000).

In 2001 , the Cooperative Institutional Research Program, a survey of freshman in
four-year colleges, found that 2.4% of all college freshman identified as having a learning
disability. This is approximately 40% of all individuals with an identified disability
entering four-year college programs (Henderson, 2001). A number of studies have
attempted to monitor and understand the outcomes for students with learning disabilities
as they leave high school, particularly in the areas of postsecondary education, vocational
training, and employment. Although much can be learned from this research, to this point,
the research, primarily, has looked either at particular subgroups of individuals with
learning disabilities or has not followed students with learning disabilities for more than a
few years afier they leave the public school system. A few small studies have looked at
the long-term outcomes for individuals with learning disabilities, but much of that

research has focused on particular student populations.

The main focus of this dissertation is to gain a better understanding of some of the
issues faced by adolescents with learning disabilities and their families as they make
decisions about postsecondary education, and bring a social work perspective to an area
of practice that is well researched in other professions but is not widely explored in social
work. This major focus will be broken down into three areas. First, this study will provide
an overview of the research on individuals with learning disabilities and potential issues
in the transition process out of high school and into various post school outcomes. Next,
this study will provide data regarding the educational and employment outcomes of
individuals with learning disabilities after they leave high school, utilizing individuals
with other identified impairments and individuals with no identified impairments as
comparison groups. Finally, this study will test the applicability of the Social Cognitive
Career Theory in explaining educational level variability for individuals with learning
disabilities. This dissertation is in a multiple manuscript format. Chapter One provides
the introduction to the topics, Chapters Two through Four are individual manuscripts,
which are described below, and Chapter Five provides an overall discussion of the
implications for the research conducted for this dissertation as well as outlining potential
future areas of research, and implications for practice and policy.

Chapter Two Overview

This article provides an overview of the current issues and trends in working with
adolescents and young adults with learning disabilities, and is geared towards a social
work audience. With that perspective in mind, the paper begins by discussing the history
of the diagnosis of learning disability and the changes in definition and assessment over

the years. A review of the literature on the various individual, family, and social impacts

of learning disability are reviewed. Next, it provides an overview of the particular
educational and vocational issues that adolescents with learning disabilities may face as
they transition out of a public school environment. Finally, it provides some suggestions
for theoretical framework and interventions that social workers might employ in a variety
of human service settings who are providing services to this client population.
Chapter Three Overview

Utilizing the National Longitudinal Survey of Youth, 1997 (NLSY 1997) cohort,
this paper explores the similarities and differences between individuals with learning
disabilities, individuals with other identified impairments, and individuals who are not
diagnosed with a leaming disability or other impairment. The NLSY 1997 began with a
sample of 8,894 adolescents aged twelve through sixteen in 1997, and has been following
this sample annually since then (“NLSY User’s Guide”, 2007). The latest data available
are from Round Nine, which are data collected in 2005. Using previous longitudinal
studies of post high school outcomes for individuals with learning disabilities as well as
other research on issues that impact adolescents and young adults with learning
disabilities, a number of variables of interest are compared between the three identified
groups (identified learning disability, other identified impairment affecting emotions or
learning and individuals with no identified impairment). These variables include the
highest grade achieved, sex, race/ethnicity, mental health scale, participant income, high
school Grade Point Average (GPA), measure of perceived school fairness, Scholastic
Achievement Test (SAT) scores, Armed Services Vocational Aptitude Battery (ASVAB)
scores, percent chance the student, when first measured, believed they would be in

college in five years, percent chance the student, when first measured, believed they

would be working at least part-time in five years, and the percent of peers who will go to
college. This paper will also discusses whether or not the age of the participant is related
to the participant’s highest grade achieved in college and their income. The findings of
these analyses, discussion of their importance in research on learning disabilities and
some implications for future research on these topics is discussed.
Chapter Four Overview

The Social Cognitive Career Theory (SCCT) was initially developed by Lent,
Brown and Hackett (1994) to explain the processes that individuals go through to develop
vocational interests, make choices about education and work, act on these choices, and
potentially find success in a career. Like other social cognitive theories, SCCT is based
on the work of Bandura and his theories of social learning, particularly focusing on the
area of self-efficacy (1986, 1997). Unlike previous career development theories, however,
the SCCT model takes into account both constructs such as vocational interests, abilities,
and vocational goals, and processes such as career choice and vocational change during a
career (Lent, Hackett & Brown, 1996). This is a move away from the historical
vocational trait theories that previously dominated career development theory (Lent,
Brown & Hackett, 1994). This model also uses Bandura’s triadic reciprocal model of
causality, where person specific attributes, external environmental factors and overt
behaviors are interactive variables, rather than the previous models of causation that did
not take into account the changes that overt behaviors can make on attributes and the
external environment (Bandura, 1986, Lent et al., 1996).

SCCT posits that there are three interacting variables; sclf- efficacy, outcome

expectations, and interest/ goal formation, that work together to affect the process of

career development (Lent et al., 1994; Lent et al., 1996; Swanson & Fouad, 1999). Self
efficacy, which is described as the most important of the three variables, is defined by
Bandura as “people’s judgments of their capabilities to organize and execute courses of
action required to attain designated types of performances” (Bandura, 1986, p. 391) and
is a “dynamic set of beliefs that are specific to particular performance domains and
interact complexly with other person, behavior and contextual factors” (Lent et al., 1996,
p. 83). Outcome expectations are the outcomes the individual believes will occur if a
particular behavior is performed (Lent et al., 1996). Finally, interest and goal formation is
the actual process of making individual vocational plans (Lent et al., 1994).

The variables of self-efficacy, outcome expectations, and goals are further
influenced by individual variables such as gender, ethnic background, socioeconomic
status, disability status, which are termed “person inputs” (Lent, Hackett & Brown, 1999).
These variables are seen to alter the individual’s opportunity structures. As these
variables change the environment around the individual, the individual will then alter
their self-efficacy beliefs, outcome expectations, and goals. For example, in this theory,
women may not enter the field of math and science because social pressures to stay out of
the field may create environmental hardships, thereby creating a situation where a
person’s self-efficacy beliefs change, leading to a move away from math and science as a
career (Lent ct al., 1994).

This paper examines the utility of the Social Cognitive Career Theory in
explaining the factors that might impact students with learning disabilities’ decisions to
attain a high school diploma and/or a postsecondary degree. Variables corresponding to

the SCCT were examined in the National Longitudinal Survey of Youth, 1997 cohort.

These include the highest grade achieved, sex, race/ethnicity, mental health rating,
educational level of biological mother and father, amount of government assistance
received, participant income, high school GPA, measures of the school environment,
percent chance the student, when first measured, believed they would be in college in five
years, and the percent of peers who will go to college. Hierarchical regression was
utilized to test models for the group of individuals with learning disabilities, the group of
individuals with other types of disabilities, and then for individuals with no diagnosed
learning disability. The results of models are compared, and the results are discussed in
terms of previous research. Implications for the use of SCCT to direct interventions for
adolescents and young adults with learning disabilities are discussed.
Conclusion

The conclusion of this dissertation includes a chapter that provides an overall
discussion of the three papers and how they relate to each other. Implications for future
research in this content area as well as a discussion on how this information might be

used in social work practice and policy are addressed.

References

Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive
Theory. Englewood Cliffs, NJ: Prentice-Hall.

Bandura, A. (1997). Self-Eflicacy: The Exercise of Control. New York: W.H. Freeman
and Company.

Blackorby, J ., & Wagner, M. (1996). Longitudinal postschool outcomes of youth with
disabilities: Findings from the National Longitudinal Transition Study.
Exceptional Children, 62(5), 399.

Center for Human Resource Research. (2007). NLSY97 user's guide: A guide to the
rounds [-9 data. Washington, D.C.:U.S. Department of Labor. Retrieved 5/11/08
from http://www.nlsinfo.org/nlsy97/docs/97HTML00/97guide/toe.htrn.

Dickinson, D. L., & Verbeek, R. L. (2002). Wage differentials between college graduates
with and without disabilities. Journal of Learning Disabilities, 35(2), 175-184.

Henderson, C. (2001). College freshmen with disabilities: A biennial statistical profile.
American Council on Education. Washington, DC: American Council on
Education.

Individuals with Disabilities Education Act of 2004, Pub. L. No 108-446.

Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying Social Cognitive
theory of career and academic interest, choice, and performance. Journal of
Vocational Behavior, 45, 79-122.

Lent, R. W., Hackett, G., & Brown, S. D. (1996). A Social Cognitive framework for
studying career choice and transition to work. Journal of Vocational Education
Research, 21(4), 3-31.

Lent, R. W., Hackett, G., & Brown, S. D. (1999). A social cognitive view of school-to-
work transition. The Career Development Quarterly, 47(4), 297.

Murray, C., Goldstein, D. E., Nourse, S., & Edgar, E. (2000). The postsecondary school
attendance and completion rates of high school graduates with learning
disabilities. Learning disabilities research and practice, 15(3), 119-127.

National Center for Education Statistics (2008). Children and Youth with Disabilities in
Public Schools. Retrieved June 1, 2008 fi'om
http://nccs.ed.gov/programs/coe/2008/sectionl/indicator08.asp.

National Research Center on Learning Disabilities (2007). SLD Identification Overview:
General information and tools to get started. Lawrence, KS: National Research
Center on Learning Disabilities. Retrieved May 11, 2008 from www.ncrld.org.

Rojewski, J. W. (1999). Occupational and educational aspirations and attainment of
young adults with and without LD 2 years after high school completion. Journal
of Learning Disabilities, 32(6), 533-552.

Sitlington, P. L., & Frank, A. R. (1990). Are adolescents with learning disabilities

successfully crossing the bridge into adult life? Learning Disability Quarterly,
13(2), 97-111.

Swanson, J. L., & Fouad, N. A. (1999). Career theory and practice: Learning through
case studies. Thousand Oaks, CA.: Sage Publications.

US. Census Bureau. (2003). Disability Status: 2000. Washington, DC: US. Department
of Commerce. Retrieved May 11, 2008 from
http://www.ccnsus.gov/population/www/cen2000/briefs.htrnl.

Wagner, M., Newman, L., Cameto, R., Garza, N., & Levine, P. (2005, April). After high
school: A first look at the postschool experiences of youth with disabilities: A
report from the national longitudinal transition study-2 (N LTS2). Ofiice of Special
Education Programs US. Department of Education.

Wagner, M., Newman, L., D'Amico, R., Jay, E. D., Butler-Nalin, P., Marder, C., et al.
(1991). Youth with disabilities: How are they doing? The first comprehensive
report from the National Longitudinal Transition Study of special education
students. Menlo Park, CA: SRI International.

10

CHAPTER TWO

Working with Adolescents and Young Adults with Learning Disabilities:

A Social Work Perspective

Abstract

This paper presents an overview of the issues and possible ways that social
workers can support adolescents with learning disabilities as they decide on educational
and career goals, leave the public school system, and enter postsecondary life. The paper
begins by giving a background on the definition of learning disabilities and some of the
recent changes in this disability category. Next, the paper reviews research on the
individual, familial, and social impacts of a diagnosis of learning disabilities. The paper
then provides an overview regarding the areas of researched interventions for adolescents
with learning disabilities as they transition out of public school. Finally, implications for
social work practice and research with this population are addressed.

Introduction

Social workers, based on our many roles in human service systems, work with
individuals with a wide variety of disabilities on a daily basis. This may occur by
mandate, such as in school districts or mental health settings, where issues related
directly to the disability may be the focus of treatment, or in agencies where services may
be indirectly related to or independent of the individual’s particular disability
(Beaulaurier & Taylor, 2001; J onson-Reid, Kontak, Citerman, Essma & Fezzi, 2004;
Quinn, 1995). Within all of these settings, social workers will work with individuals with

learning disabilities, one of the largest sub-groups of individuals with disabilities. In

11

order to empower individuals with learning disabilities to reach their educational,
vocational, and other goals, it is important that social workers gain a stronger
understanding of this diagnosis, and particularly how it impacts individuals in
adolescence and young adulthood in relation to educational and career-related goals.

According to the US. Census, approximately 18% of Americans have at least one
impairment legally considered to be a disability (“Disability Status: 2000”, 2003). Within
the public education system, that number decreases slightly to 13.8 percent of all children
enrolled (“Children and Youth with Disabilities”, 2008). One group of individuals with
disabilities that social workers may work with in all areas of practice, not only within a
school setting, is persons with learning disabilities.

Individuals with learning disabilities (also called specific learning disabilities)
comprise one of the largest groups of individuals with disabilities. The US. Department
of Education estimates that approximately half of all children with disabilities in public
school (approximately 2.7 million in the 2006-2007 school year) have a learning
disability (“Children and Youth with Disabilities”, 2008). Although there are no statistics
on the numbers of adults with learning disabilities, it is clear fi'om research that this is
diagnosis with lifelong implications, so it is likely the prevalence of the diagnosis
remains fairly stable (Goldberg, Higgins, Raskind & Herman, 2003; Hollenbeck, 2007;
NRCLD, 2007). Boys tend to be diagnosed with learning disabilities more often and
earlier in their academic careers (Bloom & Day, 2006; “Children with and Youth
Disabilities”, 2007; Wagner, Newman & Cameto, 2004). Individuals with a diagnosis of
learning disabilities, by definition, have near typical to above typical intelligence, but

have difficulties with one or more areas of academics (IDEA, 2004).

12

This diagnosis, although primarily focused on the acquisition and performance of
academic tasks, has a number of other potential implications for the individual. Children
and adults with learning disabilities may struggle with: (a) social skills issues (e.g.,
Bauninger Edelstein, & Morash, 2005; Cartledge, 2005; Elias, 2004; Goldberg ct al.,
2003; Kavale & Fomess, 1996; Weiner, 2003); (b) with self-concept and self-esteem (c.g.,
Bryan, 2005; Cosden & Eliott, 1999; Elias, 2004; Weiner, 2003); (c) with mental health
and substance abuse issues (e.g., Beichtman, Wilson, Douglas & Adlaf; 2001; Bryan,
2005; Cosden, 2001; Morrison & Cosden, 1997; Hoy et al., 1997); and (d) with self-
cfficacy and self-determination (e.g., Brinkerhoff, 1996; Field, 1996; Field, Hoffman &
Posch, 1997; Lackeye, Margalit, Ziv & Ziman, 2006). Beyond this set of psychosocial
issues, there are possible impacts regarding completing high school, going on to
postsecondary education or training, and working. Approximately 25% of students with
learning disabilities in public school will leave school without a high school diploma
(Wagner, Newman, Cameto, Garza & Levine, 2005). Approximately one in three
students with learning disabilities will go on to receive some postsecondary education,
but only a small proportion of that group will complete any postsecondary degree
(Blackorby and Wagner, 1995; McAllister, 2008; Murray, Goldstein, Nourse & Edgar,
2000 Sitlington & Frank, 1990; Rojewski, 1999; Wagner et al., 1991; Wagner et al.,
2005). Although individuals with learning disabilities will work at rates similar to the
general population, often they are engaged in lower wage work with less benefits; this is
especially true for women (Murray et al., 2000; Rojewski, 1999; Sitlington & Frank,

1990; Wagner et al., 2005).

13

This paper presents an overview of the issues and possible ways that social workers can
support adolescents with learning disabilities as they decide on educational and career
goals, leave the public school system, and enter postsecondary life. The paper begins by
giving a background on the definition on learning disabilities and some of the recent
changes in this disability category. Next, the paper reviews research on the individual,
familial, and social impacts of a diagnosis of learning disabilities. The paper then
provides an overview of researched interventions for adolescents with learning
disabilities as they transition out of public school. Finally, implications for social work
practice with this population are addressed.
Definition of Learning Disabilities

The definition of learning disabilities emerged from years of research done by
neurologists, psychologists, and educators on individuals experiencing particular
academic deficits, most commonly reading based deficits, but who had average or above
average intellectual functioning. The diagnosis was first given the label “word-blindness”
in the early 20th Century as a way to describe the symptoms of these individuals
(Hallahan & Mercer, 2001). It has since become apparent that people could experience
academically related deficits in other areas than reading such as math, writing, and
language (Hallahan & Mercer, 2001; Kavale & Fomess, 2000). Over time, learning
disability has been defined as disorders specific to the areas of spoken language,
academic, or thinking disorders, and is separated from other kinds of global learning
delays or disorders such as mental retardation (Kavale & Fomess, 2000; NRCLD, 2007).

Another key feature of this diagnosis is that it is based on psychological process deficits,

14

rather than other potential etiologies for learning deficits such as autism, emotional
disorders, sensory disorders, environmental or cultural factors (NRCLD, 2007).
The definition has not undergone radical change since Samuel Kirk first described

learning disabilities in the book Educating Exceptional Children (1962) as:
...a retardation, disorder, or delayed development in one or more of the
processes of speech, language, reading, writing, arithmetic, or other school
subject resulting from a psychological handicap caused by a possible
cerebral dysfirnction and/ or emotional or behavioral disturbances. It is not
the result of mental retardation, sensory deprivation, or cultural and
instructional factors. (p. 263)

The current definition of learning disabilities in the Individuals with Disabilities

Education Act, 2004, is the following:
(A) In general.--The term ‘specific learning disability' means a disorder in
1 or more of the basic psychological processes involved in understanding
or in using language, spoken or written, which disorder may manifest
itself in the imperfect ability to listen, think, speak, read, write, spell, or do
mathematical calculations.
(B) Disorders included.--Such term includes such conditions as perceptual
disabilities, brain injury, minimal brain dysfimction, dyslexia, and
developmental aphasia.
(C) Disorders not included.--Such term does not include a learning

problem that is primarily the result of visual, hearing, or motor disabilities,

15

of mental retardation, of emotional disturbance, or of environmental,
cultural, or economic disadvantage. (IDEA 2004, § 602).

Beyond updating the language, these two definitions have minor, although long
discussed and debated, differences (Hallahan & Mercer, 2001; Kavale & Fomess, 2000).
The biggest change that should be noted is that emotional and behavioral disturbances
cannot be the root cause of the learning disability in the current definition (Kirk, 1962;
IDEA, 2004). Although the definition of learning disabilities has remained fairly
consistent, particularly since it was included within the Education for all Handicapped
Children Act (PL 94-142, 1977), many other aspects of the diagnosis have changed over
time. The two most pertinent are: (a) how learning disabilities are diagnosed; and (b) the
prevalence of learning disabilities.

The manner of diagnosis of learning disabilities has varied over time and is still
an issue of heated debate within the learning disability research community. Although the
US. Department of Education has made statements supporting various diagnostic models
over time, there has never been an agreed upon set of diagnostic criteria (IDEA, 2004).
Each state, and in many cases, each school district, determines how they will identify and
diagnose learning disabilities (“SLD Determination”, n.d.). There are currently two major
models used to conceptualize the diagnosis of learning disabilities.

Aptitude/ Achievement Discrepancy

The aptitude/ achievement discrepancy model has a long history, going back to
early testing procedures done to identify individuals with “word blindness” beginning in
the 1920’s (Hallahan & Mercer, 2001). In this model, tests of aptitude, namely tests of

intelligence, are compared to achievement tests in various academic subjects, and

16

significant discrepancies between what would be expected based on the intelligence test
and how the student actually performs are examined as possible indicators of a learning
disability. When PL 94-142 was enacted, the US. Office of Education came out in
support of this model (Hallahan & Mercer, 2001). This model became an established
method of learning disability diagnosis, and remains a major model in many school
districts, and in many research studies done on learning disabilities (Fletcher Denton, &
Francis, 2005; Hollenbeck, 2007).

A number of arguments have been made against sole use of an aptitude/
achievement discrepancy model to identify learning disabilities. First, this method means
that children often are not formally diagnosed with learning disabilities until later in
elementary school through high school (Fuchs & Mellard, 2007 ; Hollenbeck, 2007).
There are a number of potential psychometric problems with use of the aptitude/
achievement model, including the potential for disproportionate identification of
individuals based on minority status or the highly documented sociocultural biases in IQ
tests (Fletcher et al., 2005; NRCLD, 2007; KewalRamani et al., 2007; Reis & Colbert,
2004).

Response to Intervention

The Response to Intervention models of learning disability diagnosis have been
designed to address many of the concerns regarding the use of the aptitude/ achievement
model. The major elements of the Response to Intervention models include the following:
(a) providing high quality teaching practices and screening to determine instructional
needs of all children entering the school system, (b) modeling and assessing of individual

student progress, and increasingly intensive educational interventions for students who

17

are not advancing at typical rates, and then (c) if the student does make sufficient
progress with the second level of intervention, she or he will be assessed for special
education services (Hollenbeck, 2007). This model hinges on educators having access to
best practice educational interventions that are effective to a wide variety of student
learning needs (Fuchs & Mellard, 2007; Hollenbeck, 2007; “SLD Determination”, 2007).
The hope with this model is that students who are experiencing delays in academic
achievement are getting their needs addressed in the least restrictive setting, per the IDEA
mandates, and before they display failure in academic subjects (Fuchs & Mellard, 2007;
Hollenbeck, 2007; “SLD Determination”, 2007).

Current opinion held by most members of learning disabilities organizations is
that aptitude/ achievement discrepancies are neither necessary nor sufficient for a
diagnosis of learning disabilities. A minority opinion within members of learning
disabilities organizations is that the aptitude/ achievement markers are appropriate but not
sufficient for a diagnosis of a learning disability (NRCLD, 2007). Most professionals,
along with the most current revision of IDEA, support a Response to Intervention
approach that includes achievement assessment for all students (Fletcher et al., 2005;
Fletcher, Monis, Morris & Lyon, 2005; Francis, Shaywitz, Stuebing, Shaywitz &
Fletcher, 1996; Hollenbeck, 2007 ; IDEA, 2004; NRCLD, 2007).
Prevalence of Learning Disabilities

Another area of concern for many professionals is the rapid increase in numbers
of persons being diagnosed with learning disabilities. For example, in the 1976/1977
school year, 1.8% of the K—12 student body in the United States received special

education services based on a diagnosis of specific learning disability. By the 1980/ 1981

18

school year, the proportion grew to 3.6% percent of the K-12 student body. Since the
1994/ 1995 school year, between 5.4% to 6.2% of the student population had a diagnosis
of a specific learning disability. This is approximately half of all students receiving
special education services through IDEA (“Children and Youth with Disabilities”, 2008).
The National Research Center on Learning Disabilities estimates that 6% of the
population has a learning disability in the area of reading alone (2007). It is difficult to
identify all of the reasons that the proportion of individuals receiving a diagnosis of
learning disabilities has increased so quickly. Persons with diagnoses of mental
retardation or overall low academic achievement may be misdiagnosed as a learning
disability, which is partially supported by the subsequent lower rates of diagnosis of
mental retardation when rates of learning disability increased (“Children and Youth with
Disabilities”, 2008; Fletcher, Benton and Francis, 2005; Kavale & Fomess, 1998;
NRCLD, 2007). Also, concern about the potential overuse of a diagnosis of learning
disability to receive accommodations, particularly at the college level, has been discussed
(Kavale & Fomess, 1998; Stanovich, 1999).
Impacts of Learning Disabilities

Like any other disability, a diagnosis of a learning disability affects each
individual in a unique way, based on a variety of person and environmental factors.
However, there are a ntunber of features of learning disability that affect a significant
proportion of the people with this diagnosis. The first is academic and cognitive deficits.
Social skills issues also impact the way that learning disabilities are manifested in school
and family relationships. The impact of family on the adolescent with a learning

disability may also be different than for individuals without a disability. The social

19

stigma of disability may impact the goals and expectations that the individual with a
learning disability, family, educational and other professionals have for the student.
Mental health and substance abuse, struggles with self-concept, and self-efficacy and
self-determination skills are potential additional issues for individuals with learning
disabilities.
Academic and Cognitive Issues

By definition, a learning disability “. . .may manifest itself in the imperfect ability
to listen, think, speak, read, write, spell, or do mathematical calculations” (IDEA 2004, §
602). One likely consequence of these symptoms is challenges in the areas of academic
skills. Historically, a student with a learning disability is often initially identified based
on academic struggles and low grades (Fuchs & Mellard, 2007; Hollenbeck, 2007). This
is also verified in many longitudinal studies looking at high school and post high school
outcomes. Within the sample of students with disabilities identified in the National
Educational Longitudinal Survey, individuals with learning disabilities had the lowest
math and reading proficiencies of all other groups of students with disabilities who were
sampled (Rossi, Herting, & Wohnan, 1997). The National Longitudinal Survey of Youth-
2 found that students with a diagnosed learning disability, on average, had math and
reading achievement levels a little over three years lower than would be expected for
their age and grade level (Wagner et al., 2003). Additionally, students with an identified
learning disability had significantly lower grade point averages than students with no
diagnosed learning disability within the National Longitudinal Survey of Youth, 1997
cohort (McAllister, 2008). All of these results point to the ongoing academic struggles for

individuals with learning disabilities as they are in the K-12 school system.

20

Social Skills Issues

Social skills deficits is an important focus of study within the study of learning
disability, with research in this area going back almost as long as the diagnosis has been
in existence, although much of this research began in the 1980’s (Bender, 2004; Hallahan
& Mercer, 2001; Vaughn & Haager, 1994). One of the discussions among researchers is
whether or not social skills deficits should be included as part of the diagnosis: whether
or not the symptoms of learning disabilities potentially include social skills deficits, or if
the psychosocial experience of having a learning disability potentially leads to social
skills deficits (Bender, 2004; Cartledge, 2005; Kavale & Mostert, 2004; NRCLD, 2007).
The area of social skills encompasses a number of more specific potential areas of
concern, which include: verbal and nonverbal social communication, social acceptance,
classroom behaviors, and social relationships.

Verbal and Nonverbal Social Communication

Communication skills are an important part of building and sustaining
relationships. A number of studies have looked at the ways that individuals with learning
disabilities may struggle with verbal and nonverbal communication skills. Some of the
conununication challenges researched include choosing a topic, taking turns in speaking,
requesting for something to be clarified, and being able to think about and discuss
multiple points of view when talking with others (Bauminger & Edelstein, 2005; Bryan &
Burstein, 2004; Bryan, 2005). Nonverbal communication skills may also be a challenge.
Students with learning disabilities consistently perform poorer than comparison groups
on being able to identify feelings, particularly complex emotions in others (Bauninger et

al., 2005; Bender, 2004; Bryan & Burstein, 2004; Bryan, 2005; Cartledge, 2005; Elias,

21

2004). They may also struggle to understand the social cues of others (Bryan, 2005;
Kavale and Mostert, 2004; Weiner, 2004).

Classroom Behaviors

Students with learning disabilities are consistently seen as more off task, more
disruptive to the classroom, less able to engage in classroom discussions, more
aggressive, seeking more attention and more emotionally immature (Bender, 2004; Bryan
& Burstein, 2004; Vaughn & Haager, 1994). This may lead to lowered academic
expectations for student achievement and issues with social relationships with peers.

Social Acceptance

Typically, students with learning disabilities are less likely to receive social
acceptance from their peers (Bauninger et al., 2005; Bryan, 2005; Weiner, 2004). This
appears to begin even in kindergarten (Bryan & Burstein, 2004). In a review of past
studies on this issue, Vaughn and Haager found that peer ratings of students with learning
disabilities consistently are lower than for students who do not have a learning disability
(1994). It seems that students with learning disabilities are somehow identified and are
more likely to be rejected by their peers even before academic issues are raised.

Social Relationships

Studies that look broadly at social relationships for students with learning
disabilities find that these students have fewer relationships or fiiendships with their
peers (Berger, 2004; Bryan & Burstein, 2004; Geisthardt & Munsch, 1996; Kavale &
Mostert, 2004; Vaughn & Haager, 1994; Weiner, 2004). In many ways, this is a
measurement of the global impact of the various social skill issues that students with

learning disabilities may face. Struggles with understanding what other people mean and

22

feel, conversation deficits, negative classroom behaviors and lower acceptance by peers
leads to fewer friendships. This may also lead to academic impacts, as “peer relationships
impact academic plans as peer attachment becomes more critical to the self-irnage of
vocational and educational outcomes” (O’Koon, 1997, p. 473).
Family Issues

Disability does not just impact the individual with a diagnosis, it impacts the
entire family. Concurrently, the way that families deal with or adjust to having a member
with a disability can affect the success of that member with a disability. There have been
a number of studies looking the impact of a child with a learning disability on the family,
and a number of issues have been raised. First, parents’ levels of stress are higher when
they have a child with a learning disability, although this does not have a resultant higher
level of family dysfunction (Dyson, 1996; Morrison & Cosden, 1997). Siblings of
children with disabilities report having more rigidly defined roles, but do not report lower
self-concept or self esteem (Dyson, 1996). Dyson also found that families focused more
on the personal growth of the child with a learning disability (1996). Therefore, although
there may be differences in family roles and stress level, research has not shown these
things to correlate with family problems. Finally, initial research has shown that there a
strong family association of reading disabilities within siblings, and fi'om parents to
siblings, which means that there is a significant chance that there are multiple family
members with learning disabilities (Del-Homme, Kim, Low, Yang & Smalley, 2007).

The family’s impact on the child with a learning disability, however, may be
stronger. Research finds that parents have lower academic and career expectations of

children with learning disabilities (Bender, 2004; Newman, 2005). Parents may also

23

have lowered expectations of these children’s behaviors (Bender, 2004). Parents also
tend to be more directive with children with learning disabilities (Bender, 2004). The
adolescent may take longer to establish an “adult relationship” with his or her parents
(Cosden, 2001; Yehahey & Mestanova, n.d.). Finally, parents may be more involved in
making decisions for the child at school and may not give the adolescent with a learning
disability as much responsibility for his or her own learning (Brinkerhoff, 1996; Field &
Hoffman, 1997; Field & Sarver, 2003; Yehahey & Mestanova, n.d.).
Psychosocial Issues

Adjustment to a Disability

In addition to some of the specific issues students with learning disabilities may
face, there are issues that all persons with disabilities may encounter. First, most
individuals with a disability go through stages, or a process of coming to terms with,
adjusting to, or accepting the disability. Livneh describes these stages as initial impact,
defense mobilization, initialization, realization, and integration (Stewart, 1999). Another
model, more specific to individuals with learning disabilities, includes awareness of
“difference”, the labeling event, understanding and negotiating the label,
compartmentalization, and transformation (Higgins & Raskind, 2002). In both of these
models, it is not until the later stages of acceptance/ adjustment that the individual with a
disability may feel comfortable with taking “ownership” of his or her diagnosis,
recognize the individual strengths he or she has, and find the positive aspects of living
with a disability (Higgins & Raskind, 2002; Stewart, 1999). A number of studies have
shown that students who have a better understanding of their learning disability have a

more positive outlook on their lives, can compartmentalize the effects of their disability

24

rather than perceiving it globally, and have higher success in school and later careers
(Cosden & Eliott, 1999; Field, 1996; Goldberg et al., 2003; Skinner & Lindstrom, 2003).

However, it appears that many adolescents and young adults with learning
disabilities have a minimal understanding of their disability. Although IDEA mandates
student involvement in the Individualized Education Plan (IEP), which is the contract for
educational supports and services between the student and the school district, and the
transition planning process, many adolescents with learning disabilities are either
minimally involved or not present at their IEP or transition-related meetings (Gri gal,
Test, Beattie & Wood, 1997; Hitchings et al., 2001; Mason & Field 2004). In a
nationwide study of students who had left or finished high school over the previous two
years, had an IEP in high school, and were now in college, 52.4% of them stated they did
not even see themselves as having a disability (Newman, 2005).

Secondary Mood and Substance Use Disorders

Studies have also looked at the incidence of mood disorders, particularly
depression and anxiety disorders. Several studies found a higher incidence of feelings of
depression and anxiety amongst students with learning disabilities (Bender, 2004; Bryan
et al., 2004; Dyson, 1996; Larkin & Ellis, 1998; Morrison & Cosden, 1997). Past
research has also found a higher incidence of substance abuse disorders, although the
rates of substance use are not higher (Beitchman et al., 2001; Bender, 2004; Cosden,
2001; Weinberg, 2001). Most research ties the increase in mood, anxiety and substance
use disorders to issues such as potential co-occurring disorders, social skills issues,
increased psychosocial stressors, and social stigma of having a learning disability, rather

than being primary characteristics of learning disabilities (Weinberg, 2001).

25

Social Stigma

Another issue adolescents with disabilities may face is the stigma and negative
stereotypes of being an individual with a disability. Individuals with disabilities
experience stigma and stereotypes from the media, from popular culture, fiom teachers,
family, and fiiends (Hahn, 1999). Even though a learning disability may be considered
an “invisible” disability, persons with this diagnosis still will hear and experience
prejudice and stereotypes about learning disability (McDonald, Keys, & Balcazar, 2007).
Teachers and families may have lower expectations of them because of their diagnosis
(Newman, 2003). They may experience taunting, bullying and ridicule at school
(Baumeister, Storch & Gefflten, 2008; Hahn, 1999; Higgins & Raskind, 2002, Vash &
Crewe, 2004). Instructors may be unwilling to provide accommodations (Beilke & Yssel,
1999; Frymier & Wanzer, 2003). This pressure may lead to the individual “internalizing”
these negative attitudes, believing that the negative things that are said and the lowered
expectations are all that he or she deserves.

The other outcome from the social stigma may lead to adolescents to attempt to
hide their learning disability, particularly after they leave high school. For example, the
majority of individuals with learning disabilities that received special education services
and supports in high school will not use academic supports in college (Hartman-Hall &
Haaga, 2002; Wagner ct al., 2005). Adults with learning disabilities may decide not to
disclose their disability status for fear of losing their job or being treated negatively

(Greenbaum, Graham & Scales, 1996).

26

Self-concept

A number of studies have looked at overall self-concept, which is “the view one
has of oneself, either overall or in relation to a particular situation or setting” (Bender,
2004, p. 110), and it can be looked at from a general self-concept or from a specific issue.
Typically, students with learning disabilities may not express a lowered general self-
concept, but they do express a lower self-concept in the areas of academics and other
school specific behaviors (Bender, 2004; Bryan cta1., 2004; Cosden & McNamara, 1997;
Ferri, Gregg, & Heggoby, 1997; Geisthardt & Munsch, 1996; Hargman-Hall & Haaga,
2002; Raines, 2006).
Locus of Control

Many studies have looked at the issue of locus of control, or “one’s perception of
control over onc’s environment” (Bender, 2004, p. 111). Persons with an internal locus
of control have a high sense of being able to change their environment, while persons
with an external locus of control tend to believe that the have little determination over
their fate. Researchers looking at locus of control find that students with learning
disabilities tend to express an external locus of control (Bender, 2004; Bryan et al., 2004;
Kavale & Mostert, 2004). This may lead to students being less likely to advocate for
themselves, or to experience feelings of helplessness or incompetence.
Self-efficacy
Self-efficacy, according to Bandura, is composed of“. . .people’s judgment about their
capabilities to organize and execute courses of action required to attain designated types
of performances.” (1986, p.391.) Self-efficacy beliefs link strongly to a number of

academic and vocational outcomes (N auta, Kahn, Angell & Cantarelli, 2002). Some work

27

has found that, in the areas of academic and social self-efficacy measures, students with
learning disabilities rate their self-efficacy lower (Lackeye, Margalit, Ziv & Ziman,
2006). In his review of self-efficacy studies and individuals with learning disabilities,
Klassen found that students with learning disabilities may or may not, depending on the
particular area studied, have a different level of self-efficacy than individuals without a
learning disability. What the studies found, however, was that students with learning
disabilities may overestimate or miscalibrate their abilities, particularly in the areas of
reading and writing (2002). This may be due to their maturity levels, or because they
have not been given enough information to be able to make a more accurate statement of
their achievement abilities (Klassen, 2002).
Self-determination

Self determination is a key skill that incorporates self-awareness, ability to make
decisions about the future, setting and reaching goals, and evaluating their actions and
adjusting plans if needed (Algozzine, Browder, Karvonen, Test, & Wood, 2001; Field,
1996; Wehmeyer & Field 2004). Field discusses a number of barriers that adolescents
and young adults with learning disabilities have to self-determination, which include: (a)
a lack of self-awareness of or a disavowal of their disability will make it hard to have a
sense of possibilities (b) a history of having other people make decisions for the
individual may lead to learned helplessness (Seligman, 1975); (c) cognitive challenges in
the areas of making plans and initiating behaviors may make it difficult to carry out self-
determination (1996).

The need for self-determination skills is very clear in the process of transitioning

to college for all adolescents, and in particular for adolescents with disabilities. In the

28

postsecondary environment, a student with a disability must be able to identify
themselves as a person with a disability, provide any required documentation to verify
that disability, know enough about his or her particular learning needs to ask for the right
accommodations, advocate for the right accommodations, and be able to request these
accommodations from each instructor and possibly from peers (Babbit & White, 2002;
Brinkerhoff, 1996; Gil, 2007; Skinner & Lindstrom, 2003). This is a big jump for an
adolescent who, in the secondary setting, may have only minimally participated in his or
her IEP and may never have had a discussion about his or her disability. Family may
have done all of the advocating for him or her over the years. Now, the college
environment wants to talk directly to the student, not the parent (J ani ga & Costenbader,
2002).

Summary of Issues fiom a Social Work Perspective

Students with learning disabilities, by definition, will struggle with some aspect of
knowledge acquisition. A number of other potential affects have also been discussed in
the literature, but it is unclear what the origins of these other potential secondary
characteristics of learning disability are. More specifically, how do the specific
neurological impairments an individual with learning disability has interact with that
individual’s ecosystems?

A number of researchers would argue that many of the secondary symptoms of
learning disability result the primary neurological language and cognitive impairments of
the individual. Many researchers in the field of learning disability discuss how
neurological impairments may affect social skills (e.g., Bauninger et a1, 2005.; Bryan,

2005; Court & Giron, 2003; Elias, 2004). In these models, then, these primary symptoms

29

of learning disability will then lead to various other issues such as difficulties with peer
relationships, self-esteem issues, and other mood issues (Weiner, 2004).

As the model of disability theory has moved from a medical, or deficit based,
model of disability, to a model that looks at the ways that disability has been socially
constructed, so has the area of learning disability begun to broaden the focus from the
ways that the neurological impairments lead an individual to have a disability, to how the
social system constructs an environment where the individual becomes “disabled”. Reid
and Valle, for example, argue that the concept of learning disabilities has social and
cultural underpinnings, and that it is a socially designed concept that pathologizes
students who are struggling in socially constructed school system (2004). They also argue
that individualizing these issues takes away from the systemic issues that isolate and
underpriviledge a number of student groups, including ethnic and racial minorities, as
well as individual who learn in ways that are not “normative” (Reid & Valle, 2004). The
impacts of being labeled with a disability may lead to individuals, their families and their
support systems to have lowered expectations on a variety of levels, which may impact
self-efficacy and self-determination, and finally their goals and outcomes (Dudley-
Marling, 2004; Hahn, 1999; McDonald, Keys & Balcazar, 2007; Reid & Valle, 2004;
Vash & Crewe, 2004). From this perspective, therefore, many of the secondary effects of
learning disabilities are based on the impacts of being labeled as having a disability
within the school system and within the individuals’ environment.

Another area of current research regarding the secondary impacts of learning
disability that extends the social constructionist approach to disability comes fi'om risk

and resilience research (Margalit, 2004; Weinberg, 2001; Weiner, 2003). The basis of this

30

research is that the secondary characteristics of learning disabilities, such as social skills
deficits, mood and substance disorders, are not due to the individual’s impairment, but
that having a learning disability may lead to increased risk for these other disorders. As
Weiner states, “Risk and resilience models highlight the fact that merely examining
factors intrinsic to the individual with LD provides an incomplete understanding of their
functioning” (p. 78). Some of the potential risk factors identified are cultural attitudes
towards disability, various secondary diagnoses, particularly behavioral disorders such as
Attention Deficit Hyperactivity Disorder and Conduct Disorder, levels of stress and
tension, and a lack of understanding of disability (Kalyanpur & Harry, 2004; Morrison &
Cosden, 1997; Weinberg, 2001; Weiner, 2003). Some protective factors or areas that
might affect resilience include positive classroom environments, parental adjustment and
acceptance of disability, self-awareness and understanding of the disability, and
engagement in academics (Margalit, 2004; Morrison & Cosden, 1997).

From a social work perspective, there is something to be learned from all of these
arguments. It is possible to see these perspectives as interactive, rather than at diarnetric
poles. It appears that there are sets of neurological deficits that lead to language and
learning deficits, which, like all other human characteristics, fall on a continuum. The
particular point by which these learning differences lead to a diagnosis of a disability is
based on social and political constructions of disability, as well as current research in the
field of learning disabilities. Since the inception of the diagnosis, there has been
increasing consensus regarding the symptoms of and assessment procedures for learning
disabilities. Research on learning disabilities identifies a number of primary and

secondary issues that affect a large proportion of people with a learning disability

31

diagnosis. Each individual with a learning disability will have some or all of these
primary and secondary issues based on the particular biopsychosocial factors influencing
that individual (Raines, 1989). Social work interventions, therefore, should focus on all
aspects of the client system, rather than just on the individual with a learning disability,
and should focus on systems change, empowerment, and strengths based approaches, as
is the focus of social work practice with other marginalized populations (Beaulaurier &
Taylor, 2001; Gilson & DePoy, 2002; Mackelprang & Salsgiver, 1996, 1999).
Interventions for Adolescents with Learning Disabilities

Again, the impact of the learning disability differs for each person, but the overall
impact of these issues becomes evident when looking at postsecondary educational and
vocational outcomes, with lower enrollment and completion of postsecondary education
and taking on lower wage jobs with less benefits as discussed previously. Many of the
primary and secondary symptoms or issues related to learning disabilities may impact
postsecondary educational and vocational outcomes. The impacts of lowered or
miscalibrated self-efficacy, and lower academic self-concept, may lead to students either
believing that they will not be able to manage the acaderrrics of postsecondary education,
or to aim for academic programs they may be inadequately prepared for. Students without
strong self-determination skills and may not be prepared for the skills that are expected in
postsecondary environments: high levels of autonomy in determining class enrolhnent
and class work completion; seeking out, advocating for, and requesting supports and
services for specific learning needs; making strong social support networks as an
independent adult; organizing their schedule and managing time commitments

(Brinkerhoff, 1996; Field & Hoffinan, 2003). Social skills deficits may also impact the

32

transition to independent, mature relationships in postsecondary education and vocational
environments.

However, a number of types of interventions within the school system are
discussed in the literature as having promise for success. The major interventions focus
on (a) creating educational environments to support positive post high school educational
and vocational outcomes, (b) providing social skills training, (0) providing training in
increasing self-determination practices and ((1) providing appropriate transition services.
These will be reviewed briefly, with a focus on how social workers can incorporate these
practices in their own work with adolescents with learning disabilities.

Creating School Environments for Positive Vocational and Educational Outcomes

Students with learning disabilities are less likely then the general population to
complete high school, and significantly less likely to go on to post high school
educational or vocational programs (Blackorby and Wagner, 1995; McAllister, 2008;
Murray, Goldstein, Nourse & Edgar, 2000 Sitlington & Frank, 1990; Rojewski, 1999;
Wagner et al., 1991; Wagner et al., 2005). One way to advocate for students with learning
disabilities is to support practices that lead to the inclusion of students with learning
disabilities in the general education program, particularly programs that are preparing
students for college. These experiences give students with learning disabilities more time
with peers and to work on some of the academic and social issues the student will face in
a postsecondary environment within the relative safety of secondary school. Students
with learning disabilities are increasingly being supported and included in general
education (McLeskey, Henry, & Axelrod, 1999; Wagner et al., 2003; Wehmeyer & Field,

2004). IDEA 1997 and 2004 both have reiterated the commitment to the least restrictive

33

environment (LRE) and the idea that students with disabilities should be in the general
education curriculum as much as possible (IDEA, 2004). Social workers in the school
setting should work to advocate for their students to remain in the general education
setting as much as possible by providing support and suggestions to general education
teachers as needed, working on behavior plans for students who are struggling to behave
in the general education classroom, and advocating in IEP meetings for LRE.

Students with learning disabilities are as likely to work after leaving high school
as are students without a diagnosed disability, although they are working in lower paying
jobs (Murray et al., 2000; Rojewski, 1999; Sitlington & Frank, 1990; Wagner et al.,
2005). Another way to support students with learning disabilities discussed in the
literature is to advocate for vocational oriented high school programs, particularly those
with the opportunity for paid work experiences (Benz, Lindstrom, & Yovanoff, 2000;
Evers, 1996; Griffith & Wade, 2001; Wagner, Newman & Cameto, 2005). These
programs, while providing important vocational training and preparation, have not been
shown to negatively impact students’ ability to enter and succeed in postsecondary
education (Benz, Lindsrom & Yovanoff, 2000; Griffith & Wade, 2001). Although social
workers may not be directly involved in vocational training, they should be aware of the
importance of vocationally oriented education, and be ready to advocate for vocational
experiences for adolescents with learning disabilities.

Social Skills Training

The largest area of treatment explored for students with learning disabilities is in

the area of social skills. There are a large number of social skills training programs that

have been developed for use inside and outside of the classroom. The skills taught may

34

include goal setting, working cooperatively, asking for help, and decision making
(Bender, 2004; Bryan et al., 2004; Cartledge, 2005; Elias, 2004; Kavale & Mostert, 2004).

Evaluation on these methods has shown only mild to moderate improvements in
social skills (Kavale & Mostert, 2004). Studies report that there are higher benefits to
doing this kind of training within the classroom environment, by the teacher, on an
ongoing basis, rather than having students have a separate social skills group, and should
be individualized to the particular environment (Bryan et al., 2004; Cartledge, 2005;
Elias, 2004). Because many of these training methods are relatively new, it may still be
several years before any long-term benefit can be seen. However, social skills and other
types of self-help groups are common types of interventions for many student concerns,
and are an area of practice that is common for school social workers (Garrett, 2005;
Mishna & Muskat, 2004 Raines, 2006), so it may be a natural extension of this type of
work for social workers to work with adolescents with learning disabilities on social
skills within group formats.

Social workers should also be prepared to assess adolescents with learning
disabilities in the area of social skills. In his overview of evidence-based practices for
individuals with learning disabilities, Raines reports that the only screening measure that
has shown promise for adequately being able to measure social skills deficits is the Social
Skills Rating System (SSRS), which is geared towards individuals in grades 3-12 (2006).
He recommends that workers who do not have this measurement available ask the student
sets of scaled questions regarding various areas of potential social skills issues, or to rank
order various social skills in terms of how easy or difficult they are for the student

(Raines, 2006). He also recommends gathering data from the student’s teachers, family,

35

and peers in addition to assessing the student with a learning disability, as each group will
have a different perspective on the social skills strengths and areas of concern for the
student (Raines, 2006).
Self-Determination Training

Another area of significant intervention is in the area of self-determination and
self-advocacy. This is a natural extension of social work’s core values (NASW, 1999). A
number of self—determination programs and projects have taken place focusing on
children and adolescents with learning disabilities. Many of these intervention strategies
involve providing training to the individual about their learning disability, to identify
their goals, and to set action plans towards these goals (Field, 1996). Most of these self-
determination strategies have focused on assisting students to become active participants
in their IEP process (Field, 1996; Field, Sarver & Shaw, 2003). What has been found is
that higher levels of participation in IEP’s, particularly when students are able to set goals
and plan for achievement of goals, leads to higher rates of high school completion and
postsecondary educational participation (Field et al., 2003; Griffith & Wade, 2001;
Mason, Field, & Sawilowsky, 2004; Weymeyer & Field, 2004). This is particularly
important during transition planning, which begins at the age of 14 and continues as long
as the individual with a disability is within the public school system. Adolescents with
learning disabilities, if possible, should take over the responsibility of making academic
decisions, running IEP meetings, and settings goals and plans during secondary school
(Babbit & White, 2002; Brinkerhoff, Sarver & Shaw, 2002; Mahoney-Kuba, n.d.;
Skinner & Lindstrom, 2003). By begimring the process of taking responsibility in the

secondary environment, the IEP team is giving the adolescent an environment to try out

36

the behaviors he or she will need to know to be successful in a postsecondary
environment.

Social workers can be an important component of both teaching self-
determination strategies. Similarly to social skills groups, a number of training programs
designed for adolescents with learning disabilities that have shown effectiveness in
increasing self-determination are available. The Steps to Self-Determination curriculum
by Field and Hoffman, the Choicemaker Self-Determination Transition Curriculum, and
the IPLAN Curriculum have all been shown in previous research to make positive
changes in student’s ability to engage in self-determination practices (Field, 1996). Field
also recommends role modeling, providing opportunities for students to make decisions
for themselves, attribution retraining and cognitive behavioral strategies to improve
students’ self-determination skills. Social workers should also for advocating for self-
determination practices within the educational system, particularly in the area of IEP and
transition planning participation (Beaulaurier & Taylor, 2001; Gilson & DePoy, 2002).

Conclusion

Although it is clear that many of these techniques can be and already are utilized
by school social workers, social workers in other areas of practice with adolescents
should also be prepared to support positive transitions out of the school environment. One
part of that preparation is being aware if adolescents that the social worker is currently
engaged with have learning disabilities. Adolescents receiving mental health, child
welfare, substance abuse, forensic, medical or other types of social work interventions
should be assessed for the presence of impairments such as learning disabilities. This may

be as simple as asking the client and his/her family if he/she has ever been diagnosed

37

with a learning disability. Even if the social worker is not familiar with the particular
assessment techniques for learning disability, he/she can assess for the potential impacts
the adolescent identified with a learning disability might be experiencing. The social
worker may also determine that goals such as increased social skills or self-determination
skills are appropriate to focus on in their work with adolescents with learning disabilities
and their families, depending on the nature and scope of the relationship with the client.

It is also important that social workers in all settings be prepared to assess the
potential psychosocial impacts of having a learning disability and work with the client to
understand the impact of disability discrimination and acceptance of disability on the
individual and his/ her systems (Beaulaurier & Taylor, 2001; Gilson & DePoy, 2002;
Mackelprang & Salsgiver, 1996, 1999). Social workers should be prepared to explore
with the adolescent and his or her family their understanding of learning disabilities in
general, and the way that having a learning disability has impacted him or her. It is also
important to enable the adolescent and his or her family to view and enhance the
strengths and skills the adolescent possesses. Referrals to agencies such as Centers for
Independent Living or learning disability advocacy groups may be appropriate for
individuals wanting to connect with other individuals with disabilities. A number of
learning disability advocacy resources are now available on-line (Murray, Goldstein,
Nourse, & Edgar, 2000).

Finally, social work educators should become more aware of the ways that
college students with learning disabilities can be supported within the classroom and field
placements. Building strong relationships with the college or university’s resource center

or advocate for persons with disabilities is important to support the enrolhnent and

38

success of college students with learning disabilities in Bachelor’s, Master’s, and Ph.D.
programs in social work. Social work faculty should also seek out education on the
various accommodation strategies that are often requested and used by individuals with
learning disabilities. Integrating content about practice with individuals with disabilities
can be done in almost all areas of social work class content. A number of lesson
suggestions and plans, course syllabi for required and elective courses with disability
content, and textbooks on social work practice with persons with disabilities are available
(Gilson & DePoy, 2002; Gilson, MacDuffie & Meyershon, 2002; Liese, Clevenger, &

Hanley, 1999; Mackelprang & Salsgiver, 1996, 1999; Rothman, 2003).

39

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Weinberg, N. Z. (2001). Risk factors for adolescent substance abuse. Journal of Learning
Disabilities, 34(4), 343-351.

Wiener, J. (2003). Resilience and multiple risks: A response to Bernice Wong. Learning
Disabilities Research & Practice, 18(2), 77-81.

Wiener, J. (2004). Do peer relationships foster behavioral adjustment in children with
learning disabilities? Learning Disability Quarterly, 27(1), 21-30.

Yehehey, M., & Mestanova, S. (n.d.). The role of families of students with disabilities in

postsecondary education brief. Retrieved May 11, 2008 from
http://www.rrtc.hawaii.edu/products/phases/phase3.asp.

47

CHAPTER THREE

Utilization of the National Longitudinal Survey of Youth to Study Postsecondary

Outcomes for Adolescents with Learning Disabilities: Preliminary Findings

Abstract

This study examined demographic, economic, educational, and other relevant
variables from the National Longitudinal Survey of Youth, 1997 cohort. Tests were also
performed to determine the ways in which the sample of individuals with learning
disabilities were similar or different from persons with other impairments and persons
with no identified impairment. Findings showed that the educational and income
outcomes that have been seen in previous research after one to three years of leaving high
school continue as students are three to seven years post high school. Implications and
directions for future research are discussed.

Introduction

Individuals with learning disabilities (also called specific learning disabilities)
comprise one of the largest groups of individuals with disabilities. The US. Department
of Education estimates that approximately half of all children with disabilities in public
school have a learning disability (“Children with Disabilities”, 2007). Although there are
no statistics on the numbers of adults with learning disabilities, it is clear from research
that this is a diagnosis with lifelong implications, so it is likely that the prevalence of the

diagnosis remains fairly stable (Goldberg, Higgins, Raskind & Herman, 2003;

48

Hollenbeck, 2007; NRCLD, 2007). Boys tend to be diagnosed with learning disabilities
more often and earlier in their academic careers (Bloom & Day, 2006; “Children with
Disabilities”, 2007; Wagner, Newman & Cameto, 2004). Individuals with diagnoses of
learning disabilities, by definition, have near typical to above typical intelligence, but
have difficulties with one or more areas of academics (IDEA, 2004).

This diagnosis, although primarily focused on the acquisition and performance of
academic tasks, has a number of other potential implications for the individual. Children
and adults with learning disabilities may struggle with: (a) social skills issues (e.g.,
Bauninger Edelstein, & Morash, 2005; Cartledge, 2005; Elias, 2004; Goldberg etal.,
2003; Kavale 8L Fomess, 1996; Weiner, 2003); (b) with self-concept and self-esteem (e.g.,
Bryan, 2005; Cosden & Eliott 1999; Elias, 2004; Weiner, 2003); (c) with mental health
and substance abuse issues (e.g., Beichtrnan, Wilson, Douglas & Adlaf; 2001; Bryan,
2005; Cosden, 2001; Morrison 8c Cosden, 1997; Hoy et al., 1997); and (d) with self-
efficacy and self-determination (e.g., Brinkerhoff, 1996; Field, 1996; Field & Hoffinan,
1997; Lackeye, Margalit, Ziv & Ziman, 2006). Beyond this set of psychosocial issues,
there are possible impacts regarding completing high school, going on to postsecondary
education or training, and working.

In 2001, the Cooperative Institutional Research Program, a survey of freshman in
four-year colleges, found that 2.4% of college fi'eshman identified as having a learning
disability. This is approximately 40% of all students with disabilities entering four-year
programs, and up significantly from the 1.0% of students who identified as having a
learning disability in 1988 (Henderson, 2001). This is significantly lower, however, then

the approximately 6% of students identified as having a learning disability in the public

49

school system (“Children with Disabilities”, 2007). A number of studies have attempted
to monitor and understand the outcomes for students with learning disabilities as they
leave high school, particularly in the areas of postsecondary education, vocational
training, and employment. Although much can be learned from prior work, to this point,
the research, by and large, has looked either at particular subgroups of individuals with
learning disabilities or has not followed students with learning disabilities for more than a
few years after they leave the public school system. Therefore, we have some estimates,
for example, of the types of exits students with learning disabilities take from the public
school system, how many students with learning disabilities who enter a postsecondary
institution will finish that program, and how many students with learning disabilities will
begin postsecondary education within the first year or two of leaving the public school
system. A few small studies have looked at the long-term outcomes for individuals with
learning disabilities (e. g. Goldberg, Higgins, Raskind & Herman, 2003; Wong, 2003) but
much of that research has focused on particular student populations.
Review of Previous Published Research Since 1990

National Longitudinal Transition Studies

The first major study looking at educational and vocational outcomes for students
with disabilities published since the early 1990’s is the National Longitudinal Transition
Study. Beginning in 1987, and ending in 1993, this study looked at a nationwide sample
of over 8,000 students with disabilities. Starting with a sample of students between the
ages of 13 and 18, the study followed these individuals through annual interviews with
the student and/or the parent. Areas covered included experiences in secondary school,

graduation rates, independent living measures, risk behaviors, employment and

50

postsecondary education (“NLTS Sample”, n.d.). In a paper published in 1996, Blackorby
and Wagner reported the educational and vocational outcomes for students with
disabilities in general. In terms of individuals with learning disabilities, they found that
this group was employed at rates comparable to the general population of persons without
disabilities, but were entering and completing postsecondary education at much lower
rates. Specifically, two years after high school, 13.9% of students with learning
disabilities had entered postsecondary education, which increased to 30.5% after three to
five years out. In comparison, 53% of the general population had entered postsecondary
education two years out of high school, increasing to 68% after three to five years out
(Blackorby & Wagner, 1996).

The follow up study to this one, the NLTS-2, began with a sample of 12,000
students with disabilities ages 13-16 in 2000, and plans to follow these students for 10
years (“NLTS-2 FAQ”, n.d.). The first report regarding postsecondary experiences,
published in 2005, found that, for students with learning disabilities, 75% of the sample
left high school with a diploma. Approximately one third of students with learning
disabilities had taken some postsecondary education after leaving high school, with
21.5% going to community college, 9.7% going to a four-year school, and 5% going to a
vocational/ technical school. Almost 75% of this group was working at least part time
(Wagner, Newman, Cameto, Garza & Levine, 2005).

Educational outcomes of students with learning disabilities from the state of Iowa

In 1990, Sitlington and Frank sampled 911 individuals with learning disabilities in
the state of Iowa one year after they graduated from high school. Although 77% of the

sample was working, almost two thirds of those working were in low-status jobs, and

51

men were making over one dollar per hour more than women. In addition, of the almost
30% who had begun a postsecondary program, only 6% were still in that program one
year after high school graduation. The majority of students who had any postsecondary
education were at a two year college (Sitlington & Frank, 1990).

National Educational Longitudinal Study

Roj ewski utilized the National Educational Longitudinal Study to look at the
educational and vocational outcomes and aspirations of students with learning disabilities
two years after leaving high school. He found that graduation rates and rates of
employment were significantly lower for females than males with learning disabilities.
Men and women with learning disabilities were more likely to be working only and much
less likely to be in postsecondary education than individuals without a learning disability.
Both men and women with learning disabilities were aspiring to lower prestige careers
than their counterparts without a learning disability (Rojewski, 1999).

Outcomes of individuals who are grfied and have learning disabilities

Holliday, Koller and Thomas (1999) looked at 80 individuals with learning
disabilities who had a Full-Scale IQ score of over 120 and were receiving Vocational
Rehabilitation services. They found that only 21% of the sample had completed four or
more semesters of college, but almost half of the sample was interested in a college
degree. Their findings suggested that these individuals were accomplishing educational
goals at a similar rate to other individuals with leaming disabilities (Holliday, Koller &

Thomas, 1999).

52

Outcomes of high school graduates in 1985 and 1990 with learning disabilities

Murray, Goldstein, Nourse 8:. Edgar (2000) studied the educational status of 1985
and 1990 high school graduates from three high schools between 1991 and 1996 on an
annual basis. They found that, overall, students with learning disabilities were much less
likely to enroll in or graduate from any postsecondary program, and the rates of
postsecondary enrollment for individuals with learning disabilities did not increase
significantly over time. The postsecondary graduation rates for individuals with learning
disabilities were approximately 19% at five years and 43% at ten years, compared to 35%
at five years and 55% at ten years for individuals without a learning disability. The most
striking difference is in graduation rates from four-year colleges. At ten years, 2.4% of
individuals with learning disabilities had graduated from a four-year college compared to
45.5% of individuals without a learning disability (Murray, Goldstein, Nourse 8: Edgar,
2000).
Summary of Findings

Overall, the studies looking at post high school outcomes for individuals with
learning disabilities have similar findings. Most have found employment rates that are
similar to individuals without a learning disability, and that women are working less and
for less pay than men. Although students with learning disabilities can be found at all
levels of education, they are typically less likely to graduate from high school, more
likely to enter vocational or two-year college programs, far less likely to enter a four-year
college program, and less likely to complete postsecondary education then their peers
who do not have learning disabilities. Almost all studies found that about one third of

individuals with learning disabilities graduating from high school will enter

53

postsecondary education, but the graduation rates of individuals with learning disabilities
are significantly lower than that, particularly at four-year institutions.

Only two of the previous studies followed students for longer than two years after
leaving high school. The only published study that looked at graduates over five years
after completion of high school looked at high school graduates from 1985. Since that
time, a number of significant changes have been made in Special Education services,
most specifically, a significant focus on transition planning and preparation. Since 1997,
all students receiving Special Education services are required to have an annual
Individual Transition Plan beginning at age 16, with the intent of having a team of
individuals to work with the individual with a disability to ensure that their post high
school goals can be met (IDEA, 2004). In addition, there has been a movement towards
integration of all individuals with disabilities into the Least Restrictive Environment and
into more academically challenging coursework (IDEA, 2004; McLeskey, Henry, &
Axelrod, 1999; Wagner et al., 2003; Wehmeyer & Field, 2004). Theoretically, then,
students with learning disabilities should be much more prepared for postsecondary
education and training programs. The transition plan should provide the student and his/
her support network with the opportunity to make plans towards postsecondary education
and/ or vocational goals. Inclusion in general education classes may provide the academic
challenge and rigor that is more similar to that of a postsecondary educational
environment, and thus better prepare students and increase their chances for success

(Brinkerhoff, 1996; Wehmeyer & Field, 2004).

54

Research Questions

This study, although with a still relatively young population of individuals with
learning disabilities, will begin to look at some of the more long-term outcomes for this
population. Utilizing the National Longitudinal Survey of Youth, 1997 cohort, the
following questions will be explored:

1. What are the demographic, educational, vocational, and mental health status data for
individuals with learning disabilities (LD), individuals with another identified
impairment (OI), and individuals without an identified impairment (NI)?

2. Are these identified variables similar or different between these three groups?

3. Are the educational achievement levels and income for these three groups a function

of the participant age?

Method

Study Population

In order to answer these questions using a large, representative sample of
adolescents leaving the K-12 system and entering into various post high school outcomes,
one of the samples of the National Longitudinal Surveys (NLS) was chosen. The NLS are
a series of longitudinal panel surveys sponsored by the Bureau of Labor Statistics to
“gather information at multiple points in time on the labor market experiences of six
groups of men and women” (2003, p. 1). These surveys began in 1966, and were intended
to be representative of all persons living in the US. at the time the particular sample was
drawn. These surveys have included studies of older men, mature women, young men,

young women, young mothers (NLSY79), youth (NLSY79 children and NLSY79 young

55

adults), and most recently adolescents and young adults transitioning out of the K-12
system (NLSY 97) (Bureau of Labor Statistics, 2003).

The National Longitudinal Survey of Youth, 1997 (NLSY97) was chosen because
of the relative recency of the survey, the sample size, the ability to gather a sample of
adolescents with learning issues, and the types of questions asked of the participants.
NLSY97 is a longitudinal panel study of young people with beginning ages of 12-16 as
of 12/31/96 (born between 1980 and 1984). The researchers gathered the initial sample
by screening 75,291 houses within 147 nonoverlapping primary sampling units
(“NLSY97 User’s Guide”, 2007). Interviewers requested that, within the identified
homes, all household residents born between 1980 and 1984 participate in the sample.
This led to 8,984 participants within 6,819 households (“NLSY 97 Users Guide”, 2003).
Of this sample, there is both a cross-sectional sample of the US population (6,748 initial
participants) as well as an oversarnple of Hispanic and black individuals (2,236 initial
participants) (Center for Human Resource Research, 2003). Data have been collected on
the survey participants on an annual basis. Currently, the retention rate for NLSY 97 is
81.7 for Round Nine (“NLSY97 User’s Guide”, 2007).

The NLSY 97 survey began with an interview of the primary guardian of the
participant and a separate interview with the participant. After the initial year, the
participant was the only person interviewed. In addition, a number of other data sources
were used. Students were given the Armed Services Vocational Aptitude Battery
(ASVAB) test and career inventories and the Peabody Individual Achievement Test Math

Assessment. Finally, participant’s schools were asked for final high school transcripts.

56

The content of the survey can be broken down into ten sections. The majority of
these data were gathered by participant interview, although some data were gathered by
the aforementioned methods as well. The first content area is about employment to learn
what types of jobs, how much money and how often the participants are working. Second
is a section on education, which includes enrollment status at the K-12 or postsecondary
grade level, number and types of courses taken, GPA, sources and types of financial aid,
status, and emphasis in school. Third, there is a section regarding participation in training
programs. Fourth, there is a section on the income of the participant and the household,
assets, and participation in any government programs. The fifth section is about family
formation, including relationships, partnerships/ marriages, children, and child care. Sixth,
there is a section about the background of the participant’s parents, including family
system, work history, income, level of education, health status, relationship status, and
other demographic information. Seventh, the expectations for the participant and goals
for the participant were assessed. Eighth, participants were asked about their attitudes,
including relationships with parents, relationships with peers, peer activity, religion,
perceptions of school environment, risk behaviors, and time management. Ninth,
participants were asked about their health status. Tenth, participants were asked about
environmental variables, such as the urbanicity of the residence or the unemployment rate
of the local labor market (“NLSY97 User’s Guide”, 2007).

Identification of the Sample

In order to analyze the data, three subsets of the sample were created in order to

be able to compare the participants with an identified learning disability with persons

with another identified physical, behavioral or emotional impairment and to persons with

57

no identified impairments. The first subset, which is the sample of participants labeled as
having a learning disability (LD), was identified with a question asked of the participant’s
parent or guardian at the time of the first interview. Specifically, the participant’s parent
or guardian was asked if the participant had a learning disability such as dyslexia or
attention disorder, alter stating “yes” to a question of whether or not the student had a
learning or emotional problem that limits the participant’s school or work performance
(“NLSY Parent Questionnaire”, 1997). This particular question was used because there
was no other question asked regarding learning disability asked of the entire sample at
any other time. This led to a sample of 623 individuals, which is 6.9% of the total sample.
Limitations of this identification method are discussed in the limitations section of the
paper.

The second subset consists of participants who were identified as having any
other physical, behavioral or emotional impairment and are labeled as Other Impairment
(01). This was calculated by combining the yes responses to three impairment related
questions that were asked of the participant’s parent or guardian in the first interview
(“NLSY Parent Questionnaire”, 1997). This led to a sample of 1,974 individuals, which
is 22.0% of the total sample. It is important to clarify that some of the individuals in the
LD group may have other physical, emotional, or behavioral co-occurring impairments,
but the 01 group does not have any individuals identified by the parent or guardian as
having a learning disability such as dyslexia or attention disorder. The third subset of the
sample, identified as No Impairment (NI) consists all of the other participants of the
study, and is the group of individuals who did not have a parent or guardian identify that

the participant had any type of physical, behavioral, or emotional impairment.

58

Learning disability identification is a problematic issue for all studies looking at
this client population. As an “invisible” disability (McDonald, Keys & Balcazar, 2007),
accurately identifying members of this population can be challenging. Studies may utilize
self-report, parental report, teacher report, students identified through the participant’s
school district, review testing that was done of the participant to identify him or her with
a learning disability, or finally to give tests for learning disabilities to all study
participants. Each of these sampling methods has its own strengths and limitations. This
issue is compounded by advancements in learning disability identification. Over the past
decade, a move away from aptitude/ achievement measures being the primary method of
identification to a Response to Intervention method (Hallahan & Mercer, 2001; NCRLD,
2007) firrther complicates the accurate identification of individuals with a learning
disability.

Results

Variables

In order to answer the research questions, a number of demographic and outcome
variables are examined, along with potential variables that have been identified as
theoretically relevant to the population of individuals with learning disabilities. SPSS
Version 15.0 for Windows (2006) was used to perform all statistical tests. First,
descriptive statistics will be presented these variables. Next, information regarding the
correlation of the income and educational levels of the participants in relation to the age
of the participant will be provided to answer the second research question. The Type I
error rate for rejection of these correlations of .05 was set. Finally, one-way ANOVAs

will be run on the identified variables to determine if they are different between the LD

59

group, the NI group, and the 01 group. Post-hoe tests (Tukey’s test) on the ANOVAs
were run, with a significance level of .02 to reduce the chance of Type I errors based on
the multiple tests run (Howell, 2002). Tables 3.1 through 3.5 provide information
regarding the demographic, educational, and employment related variables, Tables 3.6
and 3.7 provides the means and standard deviations of all of the variables examined,
Tables 3.8 through 3.11 provides information regarding the one-way AN OVA’s
performed, and Table 3.12 provides information regarding the correlations between age
and income and highest grade completed. Figures 1 through 3 provide information
regarding the frequencies of the highest grade completed for each group.
Demographic Data

Sex

Table 3.1 illustrates that the sample consists of approximately equal numbers of
males and females in the NI and 01 groups. In the LD sample, however, approximately
68% of the sample is male and 32% is female. This gender disparity is a typical finding in
this population. For example, the Child Trends Data Bank reports that, in 2004, 10% of
boys and approximately 6% of girls had a learning disability (“Learning Disabilities”,
n.d.). Other researchers note that boys tend to be diagnosed earlier and more often than
girls (Bloom & Day, 2006; “Children with Disabilities”, 2007; Wagner, Newman &

Cameto, 2004).

60

Racial/Ethnic Background

Because the NLSY 97 was designed to oversample for African American/ Black
and Hispanic/ Latino populations, the proportions of individuals should not be compared
to the general population without weighting the sample. NLSY97 created four racial/
ethnic categories for the study. These include Black/ Afiican American, Hispanic/ Latino,
Mixed Race/ Non Hispanic, and Non Hispanic/ Non Black, which includes all other
racial/ ethnic groups in the study sample (“NLSY97 Codebook”, 2007). The unweighted
proportions of individuals, in each racial/ ethnic category identified by NLSY as shown
in Table 3.1, can be compared between the three subsamples, however. The entire sample
has a racial/ethnic breakdown that NLSY reports as the following: 51.9% Non Hispanic/
Non Black, 26.0% Black, 21.2% Hispanic, and 0.9% Mixed Race/ Non Hispanic. With
that in mind, along with a comparison to the NI subsample, it appears that, for the LD
subsarnple (and, to a lesser extent, the 01 subsample), there is a disproportionately higher
number of Non/Hispanic/ Non Black individuals, approximately proportionately the same
numbers of Black and Mixed Race/ Non Hispanic individuals, and a disproportionately
lower proportion of Hispanic individuals.

These findings go against the data issued by the US. Department of Education
regarding racial/ ethnic status and Special Education status based on a learning disability.
In 2004, Black/ African American students and Hispanic/ Latino students were more
likely than Whites/ Caucasians to receive special education services based on a learning
disability (KewelRamini, Gilbertson, Fox & Provasnik, 2007). However, other studies
have found that Hispanic/ Latino communities are less likely to be identified with a

learning disability, particularly when it is the families who are making the identification

61

(Cosden & Eliott, 1999; Kalyanpur & Harry, 2004). It is impossible to determine the
reasons for this disproportionate finding in this sample. There may be cultural
considerations regarding disability identification. In cultures that focus on the family as
the primary unit of society, identifying a child as having a disability may reflect poorly
on the family. It is possible that Hispanic/ Latino families are less open to identifying
their children as having a disability based on the potentially negative impacts it will have
on the family (Kalyanpour and Harry, 2004). Another consideration is the increased
complexity of determining disability when there are both language and learning issues, as
this sample is more likely to be engaged in English as a Second Language cuniculum in
school settings. A parent or guardian may identify their child’s learning issues as being
related to second language learning rather than a disability.

Age of Sample

The NLSY 97 was designed to sample equal proportions of adolescents ages 12
through 16 as of December 31, 1996. As is shown in Table 3.1, it appears that all three
groups have fairly equal proportions of adolescents ages 12 through 16, although the
group of individuals with learning disabilities appears to have slightly more younger and
fewer older participants.
Vocational Data

Income earned by participant

Tables 3.2 and 3.5 provide information regarding income levels of participants.
NLSY 97 surveys all of its participants annually regarding their salary for the previous
year in a variety of categories. This data was combined to create a composite variable of

all earned income. The most recent year there is a complete set of earned income is for

62

earnings in the year 2003. Data for this were collected in both 2004 and 2005 . The data
are presented in the categories designated by NLSY in Table 3.2. The categories for
income are as follows: l= $1-,5000; 2= $5,001-10,000; 3=$10,001-25,000; 4=$25,001-
50,000; 5=$50,001-100,000; 6=$100,001-250,000; 7=$250,000 + (“NLSY97 Codebook”,
2007). The ANOVA statistic (Table 3.8) for this was significant (F [2,7608]=7.33,
p=.001). Post-hoe tests show that the N1 and 01 groups have similar means, and the LD
group has a significantly lower mean.

The biggest difference in income ranges appears to be in the area of no earned
income. Referring to Table 3.2, 27.7% of the LD sample had not earned any income in
2003, compared to 19.7% of the 01 sample and 18.6% of the NI sample. This does not
appear to match well with previous research, which found that individuals with learning
disabilities appear to be working at similar rates to the general population (Blackorby &
Wagner, 1996; Murray et al., 2000; Rojewski, 1999; Sitlington & Frank, 1990; Wagner et
al., 2005)

In order to understand this finding a little more, crosstabulations were completed
for each group. Some previous findings have shown that men with learning disabilities
work at equal proportions to the general population, but that women are likely to work
less. Please see Table 3.5 for these findings. From this table, it shows that both men and
women in the LD reported earning income comparatively less than the other two groups.
In particular, over 40% of the women in the LD group did not report earning any income
in 2003, compared to approximately 20% of the NI group and 23% of the OI group. The
gap for men is smaller, with approximately 21% of the LD group, 17% of the NI group,

and 16% of the 01 group reporting no earned income. Again, it is not possible to

63

determine what has led the LD women to work at such lower rates than all of the other
groups in this study. Previous studies have hypothesized that women who have been
identified as LD typically have more significant learning impairments than the males do,
potentially due to the way the LD is expressed in the classroom (Levine & Nourse, 1998;
Rojewski, 1999; Sitlington & Frank, 1990). It would also be important to distinguish
between women who are not earning income based on family or other responsibilities
inside the home and women who are not earning income for other reasons.

Employment Expectations

There were very few questions asked of NLSY97 participants regarding their
employment expectations. In the 2000 interviews, students were asked two questions
regarding employment. First, they were asked how likely it was that, if they were going
to school, that they would also be working. If they stated they did not expect to be in
school five years from that date, they were asked on a scale of 0-100 to estimate how
likely they would be working at least part-time. The mean and standard deviations for
this question is in Table 3.5. The mean score for the LD group was 88.9%, for the NI was
93.5%, and for the 01 group was 93.6%. The ANOVA test statistic, which can also be
found in Table 3.8, for this was significant (F [2,7610]=l7.1, p<.0005). Post-hoe tests
found that the N1 and 01 groups had similar means, and the LD group had a significantly
lower mean.
Educational Data

Highest grade completed by participant

For the last demographic, the highest grade completed by each participant, by the

2005 interview, is analyzed. For this data, NLSY staff compiled the highest grade that

64

each participant reported having completed throughout the years of data collection. For
this reason, there may be some individuals that have achieved higher educational levels
but have not been interviewed in the past few years.

For all three groups, the most common (modal) highest grade completed is the
12th grade, or completion of a high school diploma. What is clear from the information
from Table 3.2 and from Figures 1 through 3, however, is that the LD group has a very
different fiequency distribution to that of the N1 and 01 groups. For the LD group, ahnost
40 percent of the sample does not have a high school diploma, another almost 40 percent
has a high school diploma only, and only a little over 20 percent has any college
attendance (Figure 1). For the NT and 01 groups, a little over 25 percent have no high
school diploma, a little over 25 percent has a high school diploma only, and ahnost 50
percent have at least some college (Figure 2 and 3). The ANOVA for this test , presented
on Table 3.8, was significant (F [2,8892]=66.8, p<.0005). The post-hoe tests for this
statistic found that the N1 and 01 groups had similar means, and the LD group had a
significantly lower mean. This finding is consistent with all other educational outcomes
findings in previous longitudinal research on adolescents and young adults with learning
disabilities (Blackorby & Wagner, 1996; Murray et al., 2000; Rojewski, 1999; Sitlington
& Frank, 1990; Wagner et al., 2005), showing that students with learning disabilities are
more likely to leave high school without a diploma and less likely to enter postsecondary
education.

Grade Point Average

Table 3.4 contains data regarding the means and standard deviation of the Grade

Point Average (GPA) for each group. To calculate GPA for this study, NLSY 97

65

interviewers gathered high school transcripts for all individuals who had left high school
on two separate occasions. This led to transcript data being available for approximately
two-thirds (66.8%) of the participants. The NLSY 97 staff converted all grades on
transcripts to a standardized GPA based on the Carnegie Credit system (“NLSY97
Codebook Supplement”, 2007).

For this study, the overall GPA will be reviewed. For visual representation, the
exact GPA was placed into categories in Table 3.4, but for analysis, the exact GPA is
used, which is given to the hundredths place. Table 3.6 provides the mean and standard
deviation for GPA for the three groups. Both the overall GPA mean and the spread of
GPA is different for the LD group in comparison to the other two groups. Students in the
LD group had a mean GPA of 2.53. One in five students had a GPA of 2.0 or lower, and
only 23.0% had a GPA of 3.0 or higher. By comparison, the mean for the NT group was
2.84, 8.7% of students had a GPA of 2.0 or lower and 41.1% had a GPA of 3.0 or higher.
In the OI group, the average GPA was 2.84, 9.7% of the group had a GPA of 2.0 or lower,
and 42.2% had a GPA of 3.0 or higher. The ANOVA test, which is presented in Table 3.9
was significant (F[2,6001]=45.0, p<.0005). The post-hoe test showed that the 01 and NI
groups had similar means, and the LD group had a significantly lower mean.

These grades are not just implications of the relative difficulties students
identified as having a learning disability in this sample have in high school, but also has
strong implications for college admissions. In an environment where the majority of
college admission boards do not take into account a potential learning disability in their

decision, the lower GPA of students with learning disabilities likely makes gaining

66

admission to competitive or prestigious colleges challenging (Mull & Sitlington, 2001;
Vogel & Adehnan, 1992; Vogel & Leonard, 1998).

AS VAB Scores

Another measure of educational achievement is the Armed Services Vocational
Aptitude Battery tests, which were taken by 7,093 of the participants (79.0%). In order to
provide a comparable score, NLSY97 staff created age based customized sampling
weights for the tests of Mathematical Knowledge, Arithmetic Reasoning, Word
Knowledge and Paragraph Comprehension, and created a percentile score between zero
and 99 for each participant taking the exam (“NLSY97 Codebook Supplement”, 2007).
The means for each group can be found in Table 3.6. ANOVA for ASVAB scores, which
is presented in Table 3.9, was significant (F[2,7809]=7.64, p<.0005). Post-hoe tests,
however, did not differentiate groups. The 01 group had the highest mean, and the NT and
LD Groups appeared to have similar means.

SAT Scores

Part of the NLSY97 transcript analysis was to gather the SAT Verbal and Math
scores for all people reporting them to the school. This test provides a marker of students
who are planning on applying to four-year college institutions, as they are generally not
required for community or jtmior college admissions. The means and standard deviations
for each group can be found in Table 3.6. The ANOVA tests, presented in Table 3.10, for
both the SAT Verbal (F [2,2421]=2.76, p=.06) and Math (F[2,2460]=1.33, p=.27) scores
were not significant. In this case, the means for the LD group were slightly higher on
both tests. The proportion of students reporting scores in all three groups was

approximately 25%.

67

Academic Expectations

There are very few measures of academic expectations in the NLSY dataset. In
previous research, it was found that, although students with learning disabilities were
entering higher education at significantly lower rates than the general population, the
majority of students polled say they have the goal to enter postsecondary education
(Wagner et al., 2005). In 2000, NLSY97 investigators asked participants to rate, fi'om 0-
100% certainty, the chance they would be in school in five years (“NLSY97 User’s
Guide”, 2007). See Table 3.7 for the mean and standard deviations of this data. The
ANOVA (Table 3.9) for this test was significant (F[2,8022]=10.2, p<.0005). Post-hoe
tests show that the N1 and 01 groups had similar means, and the LD group had a
significantly lower mean. This result cannot be compared to the question asked in the
National Longitudinal Transition Study 2 (Wagner et al., 2005). A question asking about
the percent chance a student will be in school in five years may be related to a student’s
goals, but also may be related to the student’s academic self-efficacy (Bandura, 1986). A
student may have the desire to continue on in school, but believe that he or she is not
capable of continuing in school. Likewise, a student may also believe that he or she is
capable of continuing in school, but may have goals other than remaining in school. This
question is also confounded by the age it was asked. It was asked of all participants in the
2000 survey year. At this time, students would have been between the ages of
approximately 15 and 19. For some of the older students, they may have already been in
school and expected to be done by 2005. A correlation run between age of participant and
educational expectations for 2005 found a significant negative correlation, verifying this

hypothesis (r=-.287, p<.0005).

68

Other Variables Identified in Previous Research

Mental Health

Issues related to mental health and self-image have been identified in the past for
individuals with learning disabilities. Specifically, a few studies have found higher levels
of depression and anxiety reported for individuals with learning disabilities (Beichtrnan et
al., 2001; McNamara, Willoughby & Chalmers 2005; Morrison & Cosden, 1997). In
2000, all participants were asked a series of questions regarding their emotional state
during the month previous to the survey, that were answered “All of the time”, “Most of
the time”, “Some of the time”, and “None of the time”. These questions were turned into
a scale, which was entitled the “Mental Health Scale”, with scores that could range from
5-20. Higher scores denote more positive mental health, and lower scores might indicate
emotional concerns. Cronbach’s Alpha of this scale was estimated at 0.77 for internal
consistency, which is above the cutoff of 0.7, which may be considered the low cut point
for a scale (Garson, n.d.; “NLSY97 Codebook”, 2007). The means and standard
deviations for this scale is found in Table 3.7. The ANOVA (Table 3.10) was significant
(F [2, 8021]=9.28, p<.0005). Post-hoe tests show that the LD group has a significantly
lower mean than the NI group, but the 01 group is not significantly different from either
other group.

School Characteristics

A few studies have looked at the frustrations that students with learning
disabilities might have with the school system, particularly the secondary school system.
Students have listed difficulties with teacher expectations, boredom with special

education classes, and some alienation in the school setting (Bear, Kortering, & Braziel,

69

2006; Brown, Higgins, Pierce, Hong, & Thoma, 2003; Kortering & Braziel, 2002). The
NLSY97 asked several questions regarding the relative fairness of the school on a scale
of one to four. These items were combined to create a composite score for this analysis.
The Cronbach’s Alpha of this scale is low (0.59), meaning that the items have low
internal reliability (Garson, n.d.). Means and standard deviations for this measure can be
found in Table 3.7. ANOVA (Table 3.11) for this was not significant (F [2,8980]=.963,
p=.383).

Educational and Vocational Support

Social skills and social support are an area of significant focus within the context
of learning disabilities. Although the NLSY97 did not directly ask about social skills, it
did ask the participants to state how many people they could ask for assistance or
guidance in making vocational or educational experiences, and then asked the
relationship they had to the person they would ask for assistance from first. All three
groups reported that they would ask their biological mother for advice the most often, and
then their biological father. Please see Table 3.7 for the means and standard deviations of
this measure. ANOVA (Table 3.11) for this variable was not significant (F [2,7245]=.209,
p=.811), as all three groups had approximately the same number of people they would
request help from on educational and employment decisions.
Relationship between Education, Income and Age

Because this is a sample that includes individuals who began the survey between
the ages of 12 and 16, the last interview in 2005 would contain individuals between the
ages of about 21 to 25. One of the expectations, therefore, is that older participants may

be earning more income as they gain more work experience, and that older participants

70

will be more likely to have entered or moved firrther on in postsecondary education. This
assumption for persons with learning disabilities has been hypothesized, as many of these
longitudinal studies have only looked at students less than five years after high school
completion (Blackorby & Wagner, 1996). It has been hypothesized in the past that
students with learning disabilities may need some additional years of maturation before
going back to college, and that, over time, the numbers of people with learning
disabilities who enter college will increase (Blackorby & Wagner, 1996; Wagner et al.,
1991). The only study to look at graduates more than five years out of college, however,
found that the additional years out of school did not seem to make a large impact on
college acquisition in the population of individuals with learning disabilities (Murray et
al., 2000).

In order to determine if increasing age of participants has an impact on the highest
grade completed or income earned, correlations were run to compare both variables with
the age of the participants at first survey. Please see Table 3.12 for the results of these
correlations. For the N1 and 01 group, there is a significantly positive relationship
between education and age (01: r=.064, p=.005; NI: r=.089, p<.0005), and employment
and age (01: r=.243, p<.0005; NI: r=.240, p<.0005). For the LD group, there is a
significant positive relationship between employment and age (r=.197, p<.0005), but not
for education and age (r= .050, p=.212). This means that, for employment, all groups are
seeing a significant increase in income as they get older, but that only the 01 and NI
groups are seeing an increase in their educational level. The LD group is not showing the
same increase in educational level over time, which matches the finding of the Murray et

al. study (2000).

71

Limitations
The largest limitation to this study is the measure of learning disability within

NLSY97. The only measure within the instrument is a question asked of the participant’s
guardian in the first round of surveys. Specifically, the parent or guardian was asked if
the participant has a learning or emotional problem that impacts his or her education.
This question leaves a lot unanswered. Does the participant have a diagnosed learning
disability? Does the participant receive special education services? This limitation is
similar to limitations of other large representative data sets, such as the National
Educational Longitudinal Study (1988). A review of the disability indicators in NELS
found that parental identification of disability was potentially more accurate than other
indicators such as teacher identification (Rossi, Herting & Wolman, 1997). This indicator
also makes it possible to include students with learning difficulties who have not been
identified by the school system. However, the use of this measure only leads to difficulty
in interpretation of the results to students with a diagnosed learning disability.

Secondly, the sample of individuals identified as having a learning disability is
mixed in with individuals who are identified by their parents as having Attention Deficit
Disorder (ADD). Previous research has found a significant co-occurrence of these
diagnoses. For example, between 20-40% of individuals with Attention Deficit
Hyperactivity Disorder (ADHD) also have a learning disability (Del’Homme, Kim, Low,
Yang & Smalley, 2007). This is a significant challenge to these data, as the two
populations, although overlapping, do have different issues. For example, many of the
potential social and behavioral concerns discussed in the literature on learning disabilities
is hypothesized to happen more frequently in individuals with ADD or ADHD

(McNamara et al., 2005; Weiner, 2004). Based on how the question was asked to the

72

parent or guardian, there is no way to separate out the two groups in this study. Therefore,
all findings must be tempered with the fact that there is an unknown proportion of this
sample that may not identify as having a learning disability, but rather an attention related
disorder, or identify as having both diagnoses.

Third, having only one measure of learning disability leaves the chance for
individuals to either receive a later diagnosis of a learning disability, or to have it
determined later that the individual does not have a learning disability. A number of
individuals will be diagnosed with a learning disability in high school or later, and
individuals with higher than typical intelligence scores are also likely to be diagnosed
with a learning disability later in their academic career (F erri, Gregg & Heggoby, 1997;
Holliday & Koller, 1999; Mellard & Byme, 1993) Again, this is a limitation with no
solution in this study, unless that type of information was gathered along with the
transcript analysis and not provided at this time.

A final limitation comes from larger issues about the definition of learning
disabilities that is outside of the purview of this dissertation. The definition of learning
disability is a topic of much debate. Because IDEA leaves the specifics of determining a
learning disability to the school districts, each state or school district may come up with a
different set of standards to determine learning disability (Gordon & Lewandowsky,
2002; Hallahan & Mercer, 2001; IDEA, 2004; “SLD Determination”, n.d.). The learning
disability community has also been examining and has come to consensus that alternative
measurements for learning disability that follow a Response to Intervention, rather than
an Aptitude/ Achievement discrepancy approach, should be utilized (N CRLD, 2007).

Lastly, there is a wide range of types of learning disabilities, with varying impacts. And

73

thus the specific impacts of this diagnosis on the individual may be very difficult to
measure. However, the information gathered from this study, with the aforementioned
limitations, still has relevance for current practice and policy issues.

Discussion

These findings, although largely descriptive in nature, provide a picture of
adolescents and young adults with identified learning disabilities further out from the
completion of high school than most other longitudinal studies of this nature. The
findings are comparable in many aspects to prior studies, but can begin to extend the
thinking about the impacts of learning disability diagnoses into the longer term. The
biggest finding, which replicates all prior research, is that individuals with learning
disabilities have significantly lower educational outcomes, and that this is not changing
as students get older. Similar to the findings by Murray et al. (2000), and to Blackorby
and Wagner (1996), individuals with learning disabilities were seen to enter
postsecondary education at lower rates and are less likely to remain in postsecondary
education than the general population. The other finding that replicates previous research
is that individuals with learning disabilities are earning less in income than an individual
in the general population.

The more surprising finding in this data is that individuals who are classified as
having another health, emotional, or learning impairment other than a learning disability
or Attention Deficit Disorder, are performing at similar levels to the general population
regarding the highest grade completed, income, employment expectations, GPA, and
academic expectations. Possible explanations for this finding is that this group is

inclusive of almost any impairment, from very minor to significant. This may alter the

74

outcomes in some way. What it does support, at any rate, is the need to do separate
research on individual disability categories, rather than completing blanket studies
comparing individuals with disabilities to individuals without a diagnosed disability. It
also points to the need for professionals working with individuals with disabilities to have
an understanding of the impacts of specific disabilities to insure that interventions and
support are appropriate for their client populations.

Within the group of individuals with learning disabilities, there are additional
findings that should continue to be explored. As in previous research, women with a
diagnosed learning disability are less likely to be working, and will receive lower
earnings, than men with a learning disability. It is also important to continue to explore
the cultural and socioeconomic differences in learning disability identification. In this
study, persons identifying as Hispanic were far less likely to be identified with a learning
disability. It would also be important in future research to gather a sample that identifies
groups such as Native Americans, a group that has been overrepresented among those
diagnosed with learning disabilities, and Asian Americans, a group that has been
underrepresented in the school systems in terms of learning disability diagnoses.

Finally, it is important to look at the academic findings other than GPA and
highest grade completed, particularly when making recommendations and advocating for
individuals with learning disabilities to go into postsecondary education. In this study,
although not significant, students in the LD group actually had higher ASVAB scores and
SAT scores than the NI groups, although similar proportions of each group completed
each test. It is not possible to determine the reasons why this finding occurred, but should

be explored in future research.

75

As the NLSY97 continues to complete interviews of their participants, so should
investigation into these variables, particularly the educational and employment related
variables, continue. With the annual data, trend analysis and other longitudinal measures
could be used to gain a deeper understanding of the various trajectories that adolescents
and young adults follow in their education and employment. Relationships of variables to
educational and employment outcomes is another important area of research. Finally, this
research should allow for the participants to be followed for at least a decade, with the
benefit of a relatively large sample size, which, to this point, has not been published in
the literature. The only longitudinal studies following individuals with learning
disabilities for a decade or longer have been small samples that have been largely studied
in a qualitative fashion (Goldberg et al., 2003; Wong, 2003) The findings from continued
research on the NLSY97 will allow researchers to test some of the findings of those

studies.

76

Table 3.1

Sample size, Sex, Race/ Ethnicity, and Age of Participants

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LD N1 01
Sample Size n= 623 n= 6387 n=1974
(6.9%) (71.1%) (22.0%)
Sex
Male 426 (68.4%) 3167 (49.6%) 1006 (51.0%)
Female 197 (31.6%) 3220 (50.4%) 968 (49%)
Race/ Ethnicity
Non Black/ Non 386 (62.0%) 3137 (49.1%) 1142 (57.9%)
Hispanic
Black 152 (24.4%) 1715 (26.9%) 468 (23.7%)
Hispanic 79 (12.7%) 1481 (23.2%) 341 (17.3%)
Mixed Race/ 6 (1.0%) 54 (0.8%) 23 (1.2%)
Non Hispanic
Age 12/31/96
12 years old 135 (21.7%) 1253 (19.6%) 383 (19.4%)
13 years old 142 (22.8%) 1254 (19.6%) 411 (20.8%)
14 years old 121 (19.4%) 1348 (21.1%) 372 (18.8%)
15 years old 118 (18.9%) 1345 (21.1%) 411 (20.8%)
16 years old 107 (17.2%) 1187 (18.6%) 397 (20.1%)

 

Note: Numbers do not equal total sample due to missing data. Proportions are of the

people who responded to this question, rather than to the entire sample.

77

Table 3.2

Income Earned by Participant in Categories

 

 

 

 

LD N1 01
Income earned by
participant
None 146 (27.7%) 1001 (18.6%) 337 (19.7%)
$l-5,000 113 (21.4%) 1224 (22.8%) 367 (21.5%)

 

$5,001 -1 0,000

78 (14.8%)

912 (17.0%)

301 (17.8%)

 

 

 

$10,001-25,000 144 (27.3%) 1720 (32.0%) 530 (31.0%)
$25,001-50,000 38 (6.1%) 446 (8.3%) 149 (8.7%)
50,001+ 8 (1.3%) 72 (1.3%) 22 (1.3%)

 

Note: Numbers do not equal total sample due to missing data. Proportions are of the

people who responded to this question, rather than to the entire sample.

78

Table 3.3

Highest Grade Completed and Highest Degree Completed

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LD NI 01

Highest Grade

Completed
2“d-8m grade 59 (9.6%) 310 (4.9%) 101 (5.1%)
9‘“-1 1th grade 172 (27.9%) 1234 (19.5%) 412 (21.0%)
12th grade 245 (39.8%) 1725 (27.2%) 532 (27.2%)
1St year college 44 (7.1%) 677 (10.7%) 198 (10.1%)
2"d year college 44 (7.1%) 757 (12.0%) 259 (13.2%)
3rd year college 26 (4.2%) 610 (9.6%) 168 (8.5%)
4“1 year college 15 (2.4%) 711 (11.2%) 208 (10.6%)
5‘" year college 8 (1.3%) 217 (3.4%) 58 (3.0%)

Wyear college + 3 (0.4%) 80 (1.2%) 21 (1.1%)

Highest

Degree
No HS. Diploma 231(37.5%) 1544 (24.4%) 513 (26.0%)
HS. Diploma 245 (39.8%) 1725 (27.2%) 532 (27.2%)
Some College/ 140 (22.8%) 3052 (48.3%) 912 (46.2%)
College Degree

 

Note: Nlunbers do not equal total sample due to missing data. Proportions are of the

people who responded to this question, rather than to the entire sample.

79

Table 3.4

Grade Point Average in High School

 

 

 

 

 

 

 

 

 

LD N1 01

Grade Point Average (HS

Transcript
~1.0 12 (3.2%) 58 (1.4%) 18 (1.3%)
~1.5 30 (8.1%) 133 (3.1%) 51 (3.7%)
~2.0 71 (19.2%) 506 (11.9%) 156 (11.4%)
~2.5 112 (30.3%) 1089(25.5%) 330 (24.1%)
~3.0 104 (28.1%) 1327(31.1%) 469 (34.3%)
~3.5 33 (8.9%) 915 (21.5%) 269 (19.6%)
~4.0 8 (2.2%) 237 (5.6%) 76 (5.6%)

 

Note: Numbers do not equal total sample due to missing data Proportions are of the

people who responded to this question, rather than to the entire sample.

80

Table 3.5

Crosstabulation of Gender by Income Categories

 

 

 

 

 

 

 

 

 

 

No $1-5,000 $5,001- $10,001- $25,001- 50,001 +
Income 10,000 25,000 50,000
LD
Men 77 66 63 1 14 31 8
(21 .4%) (18.4%) (17.5%) (31.8%) (8.6%) (2.2%)
Women 69 47 15 30 7 0
(41 .1%) (28.0%) (8.9%) (17.9%) (4.2%) (0.0%)
NI
Men 453 502 380 925 285 59
(17.4%) (19.3%) (14.6%) (35.6%) (10.9%) (2.3%)
Women 548 722 532 795 161 13
(19.8%) (26.1%) (19.2%) (28.7%) (5.8%) (0.5%)
OI
Men 138 178 146 279 92 13
(16.3%) (21.0%) (17.3%) (33.0%) (10.9%) (1.5%)
Women 199 189 158 251 57 9
(23.0%) (22.0%) (18.3%) (29.1%) (6.6%) (1.0%)

 

Note: Numbers do not equal total sample due to missing data. Proportions are of the

people who responded to this question, rather than to the entire sample.

81

 

 

 

 

 

 

 

 

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83

Table 3.8

AND VA Table of Income, Employment Expectations, Highest Grade Completed

 

 

 

 

 

 

 

 

 

 

Sum of Mean
df F Sig.
Squares Square
Income Between
25.812 2 12.906 7.33 .001
Groups
Within
13392.699 7608 1.760
Groups
Total 13418.511 7610
Chance Between
Working in 5 Groups 11437.015 2 5718.508 17.1 .000
years
Within
2687410125 8032 334.588
Groups
Total 2698847.140 8034
Highest grade Between
770.682 2 385.341 66.8 .000
completed Groups
Within
51285.598 8892 5.768
Groups
Total 52056.281 8894

 

84

Table 3.9

ANO VA Table of GPA, AS VAB, and Academic Expectations

 

 

 

 

 

 

 

 

 

 

Sum of Mean
df F Sig.
Squares Square
Credit Between
weighted Groups 336781.421 2 168390.711 45.0 .000
overall GPA.
Within
22468348358 6001 3744.101
Groups
Total 22805129779 6003
ASVAB Between 1298472971 1.7 649236485
2 7.64 .000
Score Groups 23 5.861
Within 602313624866 849645401.
7089
Groups 1.320 137
Total 603612097837
7091
3.050
% Chance in Between
Schools Groups 29678.394 2 14839.197 10.2 .000
Years
Within
11713438771 8022 1460.164
Groups
Total 11743117.166 8024

 

85

Table 3.10

ANO VA Table of SAT Scores and Mental Health Scale

 

 

 

 

 

 

 

 

 

 

Sum of Mean
df F Sig.
Squares Square
SAT Verbal Between
7.244 2 3.622 2.76 .064
Groups
Within
3178.634 2421 1.313
Groups
Total 3185.877 2423
SAT Math Between
3.897 2 1.949 1.33 .265
Groups
Within
3606.508 2460 1.466
Groups
Total 3610.405 2462
Mental Between
119.974 2 59.987 9.28 .000
Health Scale Groups
Within
51908287 8019 6.473
Groups
Total 52028.261 8021

 

86

Table 3.11

ANO VA Table of School Fairness Scale and People Available for Support

 

 

 

 

 

 

 

Sum of Mean
df F Sig.
Squares Square
School Between
8.505 2 4.253 .960 .383
Fairness Groups
Within
39762.705 8980 4.428
Groups
Total 39771.210 8982
# People for Between
education, Groups
24.567 2 12.283 .209 .811
employment
advice
Within
425218.316 7245 58.691
Groups
Total 425242.883 7247

 

87

Table 3.12

Correlations of Highest Grade Completed, Income with Age

 

 

 

 

Highest Grade Probability Income with Probability
Completed with value Age Correlation Value
Age Correlation

LD Group .050 .212 .197 <.0005

NI Group .089 <.0005 .240 <.0005

01 Group .064 .005 .243 <.0005

 

88

Figure 1

Frequency Distribution of Highest Grade Completed, LD group

Histogram of Highest Grade Completed

 

 

 

 

 

Learning Disability Group

250-

200-
>.

g 150-
o
:3
8‘
I.

[it 100-

50-

Mean =l 1.58D
Std. Dev. =2.149D
G I | N =616

o 5 1o 15 20
The highest grade completed

89

Figure 2

Frequency Distribution of Highest Grade Completed, NI Group

Histogram of Highest Grade Completed

 

 

 

 

 

No Impairment Group
2,000-
1,500-
5‘
:1
3
cl 1,000"
2
In
500-
Mean =12.7SU
0 Std. Dev. =2.427 D
l T I N =6,322

-5 0 5 10 15 20 25
The highest grade completed

90

Figure 3

Histogram of the Highest Grade Completed, 01 Group

Histogram of the Highest Grade Completed

Other Health or Learing Impairment

 

600-

500-

A

O

o
1

Frequency
0.)
8.

Mean =12.64[]
Std. Dev. =2.394FJ
N =1,957

 

 

 

 

G I l
0 5 10 15 20

The highest grade completed

91

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CHAPTER FOUR

Application of the Social Cognitive Career Theory to the Educational Outcomes of

Adolescents with Learning Disabilities

Abstract

The factors impacting postsecondary education for individuals with learning
disabilities are still largely not understood. Lent, Hackett, and Brown’s (1994, 1996,
1999) Social Cognitive Career Theory (SCCT) was utilized to identify potential factors
influencing the educational performance of students with learning disabilities. Using the
National Longitudinal Survey of Youth, 1997 Cohort (NLSY97) database, hierarchical
regression analyses were completed for individuals with a diagnosed learning disability,
for individuals with other identified learning or medical impairments, and for individuals
with no identified impairments. Support for the SCCT model was found in all three
groups of participants, although the factors influencing the group of participants with
learning disabilities are found to vary from the other two groups in the areas of age, sex
of the participant, and achievement test scores. Implications for social work practice with
transition aged students with learning disabilities is discussed.

Introduction

Individuals with learning disabilities (also called specific learning disabilities)

comprise one of the largest groups of individuals with disabilities. The U.S. Department

of Education estimates that approximately half of all children with disabilities in public

97

school have a learning disability (“Children with Disabilities”, 2007). Although there are
no statistics on the numbers of adults with learning disabilities, it is clear fi'om research
that this is a diagnosis with lifelong implications, so it is likely that the prevalence of the
diagnosis remains fairly stable (Goldberg, Higgins, Raskind & Herman, 2003;
Hollenbeck, 2007; NRCLD, 2007). Boys tend to be diagnosed with learning disabilities
more often and earlier in their academic careers (Bloom & Day, 2006; “Children with
Disabilities”, 2007; Wagner, Newman & Cameto, 2004). Individuals with diagnoses of
learning disabilities, by definition, have near typical to above typical intelligence, but
have difficulties with one or more areas of academics (IDEA, 2004).

Educational achievement in general, and postsecondary educational achievement
in particular, have been particular areas of concern for individuals with learning
disabilities. Approximately 25% of students with learning disabilities in public school
will leave school without a high school diploma (Wagner, Newman, Cameto, Garza &
Levine, 2005). This is significantly lower than high school graduation rates in the general
population.

It is in the rates of college enrollment and attainment that even larger educational
disparities occur between individuals with learning disabilities and individuals without a
learning disability. In 2001, the Cooperative Institutional Research Program, a survey of
freshman in four-year colleges, found that 2.4% of all college freshman identified as
having a learning disability. This is approximately 40% of all students identifying as
having a disability that entered four-year programs (Henderson, 2001). This is
significantly lower, however, from the approximately 6% of students identified as having

a learning disability in the public school system (“Children with Disabilities”, 2007).

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Approximately one in three students with learning disabilities will go on to receive some
postsecondary education, but only a small proportion of that group will complete any
postsecondary degree (Blackorby and Wagner, 1995; McAllister, 2008; Murray,
Goldstein, Nourse & Edgar, 2000; Sitlington & Frank, 1990; Rojewski, 1999; Wagner et
al., 1991; Wagner et al., 2005). For example, Murray, Goldstein, Nourse and Edgar found
that, in their five-year follow up of 1990 high school graduates and a ten-year follow up
of 1985 high school graduates, postsecondary graduation rates for individuals with
learning disabilities were approximately 19% at five years and 43% at ten years,
compared to 35% at five years and 55% at ten years for individuals without a learning
disability. The most striking difference is in graduation rates from four-year colleges. At
ten years, 2.4% of individuals with learning disabilities had graduated from a four-year
college compared to 45.5% of individuals without a learning disability (Murray et al.,
2000)

Previous research has explored a number of potential reasons to explain these
postsecondary educational disparities. Some potential variables hypothesized to impact
lower educational achievement levels include: low self-determination skill levels (e. g.
Algozzine, Browder, Karvonen, Test, & Wood, 2001; Field, 1996; Wehmeyer & Field,
2004), less academic preparation (Rossi, Herting, & Wohnan, 1997; Wagner et al., 2003),
less family and school support for postsecondary education (e. g. Bender, 2004; Cosden,
2001; Newman, 2005; Yehahey & Mestaova, n.d), lower status vocational aspirations
(Rojewski, 1999), little understanding of the impacts of their disability and the
accommodations that could be useful to them (Cosden & Elliott, 1999; Field, 1996;

Goldberg et al., 2003; Skinner & Lindstrom, 2003), and poor social skills (e. g.

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Bauminger & Edelstein, 2005; Bryan, 2005; Cartledge, 2005; Elias, 2004; Kavale and
Mostert, 2004; Vaughn & Haager, 1994).

Although all of these variables have been shown to have significant impacts on
children, adolescents, and adults with learning disabilities, there has not been a model to
describe the relationships between these variables and their impact on educational
achievement. One model that may be able to assist in understanding and predicting
educational achievement is the Social Cognitive Career Theory (SCCT), which has been
applied to the ways that children and adolescents find career and academic interests,
make career and academic choices, and achieve various levels of academic and career
success (Brown & Lent, 2006; Lent, Hackett, & Brown, 1999). SCCT has been shown to
be effective in determining college interests, postsecondary academic self-efficacy, and
college performance (Brown, Tramayne, Hoxha, Telander, Fan & Lent, n.d.; Ali &
Saunders, 2006). SCCT has also shown promise in predicting career exploration
intentions in individuals with learning disabilities (Ochs & Roessler, 2004), but has not
yet been applied to academic related issues for individuals with learning disabilities.
Social Cognitive Career Theory (SCCT)

The Social Cognitive Career Theory was initially developed by Lent, Brown and
Hackett (1994) to explain the processes individuals go through to develop vocational and
academic interests, make choices about education and work, act on these choices, and
potentially find success in a career. Like other social cognitive theories, SCCT is based
on the work of Bandura and his theories of social learning, particularly focusing on the
area of self-efficacy (1986, 1997). Unlike previous career development theories,

however, the SCCT model tried to take into account constructs such as vocational

100

interests, abilities, and vocational goals, and processes such as career choice and
vocational change during a career (Lent et al., 1996). This is a move away fiom the
historical vocational trait theories that previously dominated career development theory
(Lent et al., 1994). This model also uses Bandura’s triadic reciprocal model of causality,
where person specific attributes, external environment factors and overt behaviors are
interactive, bidirectional variables, rather than the previous models of causation that did
not take into account the changes that overt behaviors can make on attributes and the
external environment (Bandura, 1986, Lent et al., 1996). Therefore, SCCT is interested in
the ways that variables interact with each other over time to lead to the choices
individuals make about education and career, rather than looking at career and academic
choice as a static event.

SCCT posits that there are three interacting variables: self- efficacy, outcome
expectations, and interest/ goal formation, that work together to affect the process of
career and academic interests, goals, actions, and performance (Lent et al., 1994, 1996;
Swanson & Fouad, 1999). Self-efficacy, which is described as the most important of the
three variables, is defined by Bandura as “beliefs in one’s capacity to organize and
execute the courses of action required to produce given attainments.” (Bandura, 1997, p.
3) and is a “dynamic set of beliefs that are specific to particular performance domains and
interact complexly with other person, behavior and contextual factors” (Lent et al., 1996,
p. 83). Outcome expectations are the outcomes the individual believes will occur if a
particular behavior is performed (Lent et al., 1996). Finally, interests, goal formation,
actions, and performance is the actual process of making and carrying out individual

vocational and academic choices (Brown & Lent, 2006; Lent et al., 1994).

101

The variables of self-efficacy, outcome expectations, and goals are further
influenced by individual variables such as gender, ethnic background, socioeconomic
status, disability status, which are termed “person inputs” (Lent et al., 1999). These
variables are seen to alter the individual’s opportunity structures. As these variables
change the environment around the individual, the individual will then alter their self-
efficacy beliefs, outcome expectations, and goals. For example, in this theory, women
may not enter the field of math and science because social pressures to stay out of the
field may create environmental hardships, thereby creating a situation where a person’s
self-efficacy beliefs change, leading to a move away fi'om math and science as a career
(Lent et al., 1994).

The model also includes proximal and background contextual affordances. Based
on work by Vondracek, contextual affordances are parts of the individual’s enviromnent
that may support or be a barrier to the individual (cited in Lent et al., 1994). The SCCT
model breaks these contextual affordances into background and proximal affordances.
Background contextual affordances are variables that impact the person’s view of him or
herself, and will shape their learning experiences and self-perception. Proximal
contextual affordances are environmental variables that directly affect or moderate an
individual’s goals and actions as they are making them, rather than in the process of
carrying out those goals and actions (Brown & Lent, 2006; Lent et al., 1994, 1996). Some
contextual affordances discussed include cultural values, socioeconomic status and

familial support (Brown & Lent, 2006).

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SC CT Career/ Academic Choice Model

Three models were derived fi'om this basic theoretical structure: vocational and
educational interests, occupational and academic choice, and occupational and
educational performance (Lent et al., 1994, 1999). This study utilizes the career choice
model in looking at academic achievement. In this model, person inputs such as sex,
mental health, race, personality dispositions, and health or disability, along with
background contextual affordances, lead to particular learning experiences. These
learning experiences become the basis for the individual’s self-efficacy and outcome
expectations regarding their perceived ability and belief in positive outcomes for
continuing his or her education. These self-efficacy and outcome expectations impact the
individual’s educational interests, goals, and ultimately educational actions of leaving,
continuing on, and completing high school and postsecondary education. These interests,
goals and actions will also be moderated and directly affected by proximal contextual
affordances (Brown & Lent, 2006; Lent et al., 1994, 1996).
Research Questions and Hypotheses

The main purpose of this study is to examine the ability of the SCCT career and
academic choice model to predict academic achievement levels, or the highest grade
completed by individuals with learning disabilities, individuals with other types of health
and learning impairments, and individuals without any identified impairment. The
secondary purpose of this study is to examine the similarities and differences between the
individual variables, if any, predicting academic achievement in these three groups. With

this in mind, the research questions are as follows:

103

Research Question 1: Does the SCCT Career and Academic Choice Model predict
academic achievement of individuals identified with a learning disability?

Research Question 2: Does the SCCT Career and Academic Choice Model predict
academic achievement of individuals identified with health or learning impairments other
than a learning disability?

Research Question 3: Does the SCCT Career and Academic Choice Model predict
academic achievement of individuals with no identified impairment?

Research Question 4: What are the similarities and differences of model fit
between individuals with learning disabilities, individuals identified with health or
learning impairments other than a learning disability, and individuals with no identified
impairment?

In order to answer Research Questions 1 through 3, hierarchical linear regression
will be completed on each of the subgroups. Hierarchical linear regression is used when
predictor variables are added in blocks to the regression model based on a particular
theoretical construct (Garson, n.d. a; Tabachnick & F idell, 2007). To answer Research
Question 4, the three models will be compared with one another. SPSS Version 15.0 for
Windows (2006) was used to perform all analyses. The Type I error rate for rejection of
the statistic of .05 was set for all tests.

The following hypotheses were generated for the SCCT Model for adolescents
with learning disabilities, based on previous research done in this area.

Hypothesis 1: The full model will be the best predictor of the highest grade

achieved by adolescents with learning disabilities.

104

 

Hypothesis 2: Sex will be a significant predictor of the highest grade achieved for
adolescents with learning disabilities, when all other model predictors are controlled.
Specifically, males will have a higher level of academic achievement, as measured by the
highest grade achieved. This is based on the theory that females who are diagnosed with a
learning disability are often more impaired than males with the diagnosis.

Hypothesis 3: Race/ethnicity will not be a significant predictor of the highest
grade achieved for adolescents with learning disabilities when all other predictors are
controlled.

Hypothesis 4: A participant’s score on the mental health scale will be a significant
predictor of highest grade achieved for adolescents with learning disabilities when all
other predictors are controlled.

Hypothesis 5: The participant’s expectation of percent chance he or she will be in
school in 2005, asked in 2000, will be a significant predictor of highest grade achieved
for adolescents with learning disabilities when all other predictors are controlled.

Hypothesis 6: The highest degrees achieved by the participant’s biological mother
and father will be significant predictors of the highest grade achieved for adolescents
with learning disabilities when all other predictors are controlled.

Hypothesis 7: The income earned by the participant in 2003 will not be a
significant predictor of highest grade achieved for adolescents with learning disabilities
when all other predictors are controlled.

Hypothesis 8: The number of biological children the participant has will be a
significant predictor of the highest grade achieved for adolescents with learning

disabilities when all other predictors are controlled.

105

Hypothesis 9: The amount of government assistance received by the participant
will be a significant predictor of the highest grade achieved for adolescents with learning
disabilities when all other predictors are controlled.

Hypothesis 10: The participant’s high school grade point average (GPA) will be a
significant predictor of the highest grade achieved for adolescents with learning
disabilities when all other predictors are controlled.

Hypothesis 1]: The participant’s perception of high school fairness, measured in
1998, will not be a significant predictor of the highest grade achieved for adolescents
with learning disabilities when all other predictors are controlled.

Hypothesis 12: The participant’s perception of the proportion of his or her peers
that will go to college will be a significant predictor of the highest grade achieved for
adolescents with learning disabilities when all other predictors are controlled.

Method
Study Population

In order to answer these questions using a large, representative sample of
adolescents leaving the K-12 system and entering into various post high school outcomes,
one of the samples of the National Longitudinal Surveys (NLS) was chosen. The National
Longitudinal Survey of Youth, 1997 (NLSY97) was chosen because of the relative
recency of the survey, the sample size, the ability to gather a sample of adolescents with
learning issues, and the types of questions asked of the participants. NLSY97 is a
longitudinal panel study of young people with beginning ages of 12-16 as of 12/31/96
(born between 1980 and 1984). The researchers gathered the initial sample by screening

7 5,291 houses within 147 nonoverlapping primary sampling units (“NLSY97 User’s

106

Guide”, 2007). Interviewers requested that, within the identified homes, all household
residents born between 1980 and 1984 participate in the sample. This led to 8,984
participants within 6,819 households (“NLSY 97 Users Guide”, 2007). Of this sample,
there is both a cross-sectional sample of the US population (6,748 initial participants) as
well as an oversample of Hispanic and black individuals (2,236 initial participants)
(“NLSY User’s Guide, 2007). Data have been collected on the survey participants on an
annual basis. Currently, the retention rate for NLSY 97 is 81.7 for Round Nine
(“NLSY97 User’s Guide”, 2007). The most recent round of data available is Round Nine,
which was collected in 2005.

The NLSY 97 survey began with an interview of the primary guardian of the
participant and a separate interview with the participant. After the initial year, the
participant was the only person interviewed. A wide array of data regarding education,
employment, family status, health, attitudes, beliefs, expectations, socioeconomic
variables, and demographic data were gathered. In addition, a number of other data
sources were used. Students were given the Armed Services Vocational Aptitude Battery
(ASVAB) test and career inventories. Finally, participants’ schools were asked for final
high school transcripts, which provided information regarding courses completed, Grade
Point Average (GPA), and Scholastic Aptitude Test scores, if the participant reported the
scores to the school. (“NLSY97 User’s Guide”, 2007).

Identification of the Sample

In order to analyze the data, three subsets of the sample were created in order to

be able to compare the participants with an identified learning disability with persons

with another identified physical, behavioral or emotional impairment and to persons with

107

no identified impairments. The first subset, which is the sample of participants labeled as
having a learning disability (LD), was identified with a question asked of the participant’s
parent or guardian at the time of the first interview. Specifically, the participant’s parent
or guardian was asked if the participant had a learning disability such as dyslexia or
attention disorder, after stating “yes” to a question of whether or not the student had a
learning or emotional problem that limits the participant’s school or work performance
(“NLSY Parent Questionnaire”, 1997). This particular question was used because there
was no other question asked regarding learning disability asked of the entire sample at
any other time. This led to a sample of 623 individuals, which is 6.9% of the total sample.
Limitations of this identification method are discussed in the limitations section of the
paper.

The second subset consists of participants who were identified as having any
other physical, behavioral or emotional impairment and are labeled as Other Impairment
(OI). This was calculated by combining the yes responses to three impairment related
questions that were asked of the participant’s parent or guardian in the first interview
(“NLSY Parent Questionnaire”, 1997). This led to a sample of 1,974 individuals, which
is 22.0% of the total sample. It is important to clarify that some of the individuals in the
LD group may have other physical, emotional, or behavioral co-occurring impairments,
but the 01 group does not have any individuals identified by the parent or guardian as
having a learning disability such as dyslexia or attention disorder. The third subset of the
sample, identified as No Impairment (NI) consists all of the other participants of the
study, and is the group of individuals who did not have a parent or guardian identify that

the participant had any type of physical, behavioral, or emotional impairment.

108

Identified Variables for Analysis

Utilizing the NLSY 97 Web Investigator system, the NLSY97 Codebook, the
NLSY97 Interview guides for the parent/ guardian and participant, and the NLSY97
User’s Guide, a number of variables with relevance to the SCCT career/ academic choice
model were identified. The model under examination in this paper is an abbreviated
model, as there were no identified variables available for outcome expectations, interests,
or goals. There were, however, a number of relevant variables for the person inputs,
contextual affordances, learning experiences, self-efficacy measures, and actions. These
will be the areas of the SCCT career/ academic choice model tested in this study. In the
SCCT model, the areas of person inputs, leaming experiences, proximal and distal
contextual affordances, self-efficacy and actions will be tested.

Person Inputs

Several person inputs, or demographic variables were utilized. The first is the sex
of the individual. Sex has been found to be a significant predictor of career and academic
choice in both populations of individuals with learning disabilities, as well as for
individuals without a diagnosed disability (Rojewski, 1999). The SCCT Model also
identified sex as an important variable based on the impacts it can have in terms of
cultural values and sex role stereotyping (Lent et al., 1994, 1996).

Race/ethnicity is another key variable identified by the SCCT Model for its
potential impacts on resource availability, cultural values and racial/ ethnic stereotyping
(Lent & Worthington, 2000). NLSY was designed with an oversampling for individuals
identifying as African American/ Black and Hispanic/ Latino in order to be able to have

stronger power when looking at race/ethnicity as part of the analysis. NLSY97 created

109

four racial/ ethnic categories for the study. These include Black/ African American,
Hispanic/ Latino, Mixed Race/ Non Hispanic, and Non Hispanic/ Non Black, which
includes all other racial/ ethnic groups in the study sample (“NLSY97 Codebook”, 2007).
Two dummy variables were created to represent the sample of individuals who were
African American/ Black and Hispanic/ Latino, with the Non-Black/ Non-Hispanic group
used as a reference category. The category Mixed Race/ Non Hispanic was removed fiorn
the analysis due to the small size of the category.

Age is another potential person input of importance in this study, as study
participants ranged in age from 12 to 16 at the beginning of the study. Individuals who
were older at the beginning of the study have had more time out of high school to
potentially enter postsecondary education, so it is possible that age has a relationship to
the highest degree completed. Table 4.1 provides the means and standard deviations of
the ages for all three subgroups.

Finally, the mental health scale used by NLSY97 is used in person inputs, as
mental health is seen as a person input in the SCCT model (Brown & Lent, 2006; Lent et
al., 1996). In 2000, all participants were asked a series of questions regarding their
emotional state during the month previous to the survey, and were answered “All of the
time”, “Most of the time”, “Some of the time”, and “None of the time”. These questions
were turned into a scale, which was entitled the “Mental Health Scale”, with scores that
could range from 5-20. Higher scores denote more positive mental health, and lower
scores might indicate emotional concerns. Cronbach’s Alpha of this scale was estimated
at 0.77 for internal consistency, which is above the cutoff of 0.7, which may be

considered the low cut point for a scale (Garson, n.d. b; “NLSY97 Codebook”, 2007).

110

Table 4.1 provides the means and standard deviations of the mental health scale for all
three subgroups.

Self-eflicacy Variable

In this study, the only question that links to self-efficacy was asked in Round Four
in 2000. All participants were asked to estimate the percent chance, fiom 1 to 100, that
they would be in school five years from the date of the interview (“NLSY97 User’s
Guide”, 2007). This has a link to self-efficacy as defined by Lent, Hackett, and Brown
(1994, 1996), as it is asking how likely they would be to complete an action, which is a
way of determining the individual’s measurement of ability to continue on in school. The
limitations of using this variable to determine self-efficacy will be discussed in the
limitations section of this paper. Table 4.1 provides the means and standard deviations of
this data for all three subgroups.

Contextual Aflordance Variables

Because the determination of whether or not a contextual affordance is proximal
or background to the individual is based on that person’s perspective (Brown & Lent,
2006; Lent et al., 1996), this category is collapsed into one block of variables. A number
of variables were identified in this section. First, the highest degree achieved by the
participant’s biological mother and father were identified. This may impact both the
socioeconomic status of the family, but also the amount and types of support parents will
give to higher education. Table 4.2 provides the means and standard deviations for these
data for all three subgroups. It is presented in the scale provided by NLSY97. The
categories are as follows: 1=No Degree; 2=General Educational Development

Certification (GED); 3=High School Diploma; 4=Associate/ Junior College;

111

5=Bachelor’s Degree; 6=Master’s Degree; 7=Ph.D.; 8=Professional Degree (“NLSY97
Codebook”, 2007).

The number of biological children the participant was also used as a contextual
affordance, as having children will alter the participant’s priorities and ability to remain
in school. Table 4.2 provides the means and standard deviations of this measure for all
three subgroups. Finally, the amount of government assistance and participant’s income
are measures of socioeconomic status, which is another factor identified in the SCCT
model within proximal and background contextual affordances (Lent et al., 1994, 1996).
Table 4.2 lists the means and standard deviations for all three subgroups on both of these
socioeconomic measures.

Learning Experiences

A number of learning experience variables were identified, as these experiences
would have shaped a participant’s attitudes towards education and ability to continue on
in higher education. The variables identified include Grade Point Average (GPA), Armed
Services Vocational and Aptitude Battery, a School Fairness scale, and the proportion of
the participant’s peers the participant believes will go to college. To calculate GPA for
this study, NLSY 97 interviewers gathered high school transcripts for all individuals who
had lefi high school on two separate occasions. This led to transcript data being available
for approximately two-thirds (66.8%) of the participants. The NLSY 97 staff converted
all grades on transcripts to a standardized GPA based on the Carnegie Credit system
(“NLSY97 Codebook”, 2007). Table 4.3 presents the means and standard deviations for

all three subgroups on this measure.

112

For the ASVAB scores, which were taken by 7,093 of the participants (79.0%),
NLSY97 staff created age based customized sampling weights for the tests of
Mathematical Knowledge, Arithmetic Reasoning, Word Knowledge and Paragraph
Comprehension, and created a percentile score between zero and 99 for each participant
taking the exam (“NLSY97 Codebook”, 2007). Table 4.3 presents the means and
standard deviations for all three subgroups on this measure.

For the School Fairness scale, NLSY97 asked several questions regarding the
relative fairness of the school on a scale of one to four. These items were combined to
create a composite score for this analysis. The Cronbach’s Alpha of this scale is low
(0.59), meaning that the items have low internal reliability (Garson, n.d. b). Finally,
students were asked what proportion of their peers would go to college, with responses
being “Almost None (less than 10%)”, “About 25%”, “About half (50%)”, “About 75%”,
and “Almost All (more than 90%)” (“NLSY97 Codebook”, 2007). Table 4.3 presents the
means and standard deviations for all three subgroups on this measure.

Results
Descriptive Statistics

Although this is not a primary focus of this particular study, a number of these
variables differ significantly between the three identified sub-groups in this study
(individuals with learning disabilities, individuals with other health or learning
impairments, and individuals with no identified impairment). On ahnost all variables, the
groups of individuals with other identified health and learning impairments are similar to
each other, and the group of individuals with learning disabilities differs fi'om those two
groups. Individuals with learning disabilities have lower levels of education, are more

likely to be male, less likely to be Hispanic/ Latino, to have lower academic expectations,

113

to have a lower mental health score, to have parents with lower levels of education, lower
income, higher amounts of government assistance, fewer biological children, and a lower

GPA. (For more information regarding these similarities and differences, please see
McAllister, 2008.)
Missing Data

Like many longitudinal studies, almost every variable in the NLSY97 has some
proportion of missing data. In addition, a number of questions or sections of interviews
were asked only of a subsection of the study population, typically based on the age of the
participant (“NLSY97 User’s Guide”, 2007). In order to manage missing data, two
strategies were used. First, the only variables used in this study were those that were
posed to the entire sample, rather than a subsection of the population. In addition,
analyses were run and compared between the scenario where missing data were removed
fi'om the study listwise, and where missing data were imputed using linear interpolation
(Tabanchnick & F idell, 2007). The significant reduction in sample size along with
significant results in some individual variables led to the decision to use linear

interpolation for missing values (Tabanchnick & Fidell, 2007).

Correlations

Zero-order correlations were run on the data for all three groups before running
regression analyses to determine the relationships both between predictors and outcome
variables, but to look at the relationships between individual variables. For the LD group,
the only variables that are not significantly correlated to the highest grade completed are
age, income of the participant, ASVAB scores, the school fairness scale, and the

proportion of peers planning to go to college. For the 01 group, the only variable not

114

significantly correlated to the highest grade completed is the income of the participant.
For the NT group, all of the variables are significantly correlated with the highest grade
completed. Because all of the variables are correlated with the NT group, all of the
potential predictor variables will be used in the hierarchical regression models for
comparison between groups. Because there are a number of significant correlation
between predictor variables, multicollinearity diagnostics were completed during the
regression analyses. There were no identified variables in any of the regression models
that would lead to threats of multicollinearity (Garson , n.d.a).
Model Blocks for Hierarchical Regression

Four blocks of variables were created for the hierarchical regression based on the
theoretical constructs of the SCCT model. These blocks were entered into the model in
order of causality or theoretical importance (Tabachnick & Fidell, 2007). The criterion to
determine whether or not a block of variables will remain in the final model is the
Adjusted R Square variable and the Significance of the F Change for each block. An
increase in the Adjusted R square along with a p value of less than .05 for the F Change
variable led to the block remaining in the model (Garson, n.d.a; Tabachnick & Fidell,
2007). The first block is the person inputs. Person inputs may be one of the strongest
predictors of actions, as they will shape contextual affordances and learning experiences,
leading to changes in self-efficacy and outcome expectations (Lent et al., 1994, 1996,
1999). Next, a block for self-efficacy is entered, as self-efficacy is modeled to have
direct and strong effects on actions (Lent et al., 1994, 1996, 1999). Third, a block of

contextual affordances is entered. Finally, a block of learning experiences are put into the

115

regression model. The criterion variable is the highest grade completed as of the 2005
survey.
Results of Hierarchical Regression

Individuals with Learning Disabilities (LD)

Please see Tables 4.4 through 4.6 for the relevant output for this model. Each
block entered into the regression model led to a significant adjusted R square change and
significant F change, so the full model will be used to predict highest grade completed
(Table 4.4). The full model is significant F (15, 585)=14.7, p<.0005, and accounts for
25.6% of the variance (Table 4.5). Table 4.6 provides results regarding the individual
predictor variables in the model. Significant individual predictors in the model include
the following: Mental Health Status (B=.083, p=.022), Academic Expectations (B=.l33,
p<.0005), Biological Mother’s Highest Degree (B=.206, p<.0005), Biological Father’s
Highest Degree (B=.175, p<.0005), Amount Received by Government Programs (B=-.113,
p<.0005), and GPA (B=.l60, p<.0005). All other individual predictors, which include
Sex, Age, Race/ Ethnicity, Income Earned by Participant, Number of Biological Children,
ASVAB score, School Fairness, and Proportion of Peers Planning to Go to College were

not significant.

Individuals with no Identified Impairment (NI)

Please see Tables 4.7 through 4.9 for the relevant output for this model. Each
block entered into the regression model led to a significant adjusted R square change and
significant F change (Table 4.7), so the firll model will be used to predict the highest

grade completed. The firll model is significant F (15, 6218)=195.6, p<.0005 (Table 4.8),

116

and accounts for 31.9% of the variance in the highest grade completed. Table 4.9
provides the results regarding the individual predictor variables in the model. Significant
individual predictors include the following: Sex (B=.120, p<.0005), Age (B=. l 57,
p<.0005), Mental Health Status (B=.024, p=.023), Academic Expectations (B=.l 17,
p<.0005), Biological Mother’s Highest Degree (B=.l94, p<.0005), Biological Father’s
Highest Degree (B=.099, p<.0005), Income of Participant (B=.-.O33, p=.003), Number of
Biological Children (B=-. 120, p<.0005), Amount Received by Government Programs
(B=-.061, p<.0005), ASVAB score (B=.051, p<.0005), GPA ([3=.270, p<.0005), and
Proportion of Peers Planning to Go to College (B=.022, p=.046). The only individual
predictors that were not significant were Race/Ethnicity and School Fairness.

Individuals with other Health and Learning Impairments (01)

Please see Tables 4.10 through 4.12 for the relevant output for this model. Each
block entered into the regression model led to a significant adjusted R square change and
significant F change, so the full model will be used to predict the highest grade completed
(Table 4.10). The full model is significant F (15, 1915)=57.4, p<.0005, and accounts for
30.5% of the variance in the highest grade completed (Table 4.11). Table 4.12 provides
the results regarding the individual predictor variables in the model. Significant
individual predictors include the following: Sex ([3=.119, p<.0005), Age (B=.132,
p<.0005), Mental Health Status (B=.063, p=.001), Academic Expectations (B=.089,
p<.0005), Biological Mother’s Highest Degree (B=.200, p<.0005), Biological Father’s
Highest Degree (B=.l34, p<.0005), Number of Biological Children (B=-.133, p<.0005),
ASVAB score (B=.065, p=.002), and GPA (B=.271, p<.0005).The individual predictors

that were not significant include: Race/ Ethnicity, Income of Participant, Amount

117

Received by Government Programs, School Fairness, and Proportion of Peers Planning to
Go to College.

Response to research questions

In all three subgroups of individuals with an identified learning disability,
individuals without an identified impairment, and individuals with other learning or
health impairments the final, full model that matched the SCCT model most closely were
the best fit to the data. Therefore, in all three populations, the SCCT model is supported
with these data Therefore, the evidence from this data suggests that the SCCT Career and
Academic Choice model predicts the academic achievement of individuals with learning
disabilities, individuals with other health and learning impairments, and individuals with
no identified impairment or disability.

Comparisons between models

There are, however, a number of areas where individual predictors were
significant in one or two subgroups but not in the third subgroup. For all three groups, the
predictors of race/ ethnicity and School Fairness were not significant. In terms of the
Race/ Ethnicity variables, which were in the first block of predictors, they lost
significance as predictors as additional blocks of predictors were entered into the model.
Because the School Fairness predictor was added last, it was never significant for any of
the subgroups.

For individuals in the LD group, however, there were four nonsigrrificant
variables that were significant in the NT and 01 groups. The first is the sex of the
individual. For both the group of individuals with no identified impairment and the group

of individuals with other health or learning impairments, this was a significant variable.

118

In this case, for both groups, being female led to a higher level of school completion.
Age of the participant also was not related to the highest grade completed. This means
that older students are no more likely to have a higher level of education than younger
students. For the 01 and NT group, older participants are more likely to have higher levels
of education, which makes sense given the age range of participants when last surveyed
in 2005. Participants, when last surveyed, would be between the ages of approximately
21 and 25, a typical age range for young adults to be participating in higher education.
ASVAB scores for individuals with learning disabilities also did not predict the highest
grade completed. Finally, the number of biological children a participant has did not
relate to the highest grade completed for individuals with learning disabilities, although it
did for the 01 and NI groups.
Discussion

This study provides some initial support for the use of the SCCT model in
explaining the academic actions taken by individuals with learning disabilities, as well as
for individuals with other impairments and individuals with no identified impairments.
Although it is not possible to determine from this analysis which of the blocks of
variables is the most important in predicting the academic achievement of the participants,
the blocks of person inputs, academic self-efficacy, contextual affordances, and learning
experiences all were significant in the final model. Although this study did not include
all of the theoretical constructs in the SCCT model, it does provide partial support to the
utility of the SCCT model in predicting academic achievement for all students.

A number of the individual variables that were significant in all populations

should receive further exploration. For example, in all groups the highest degree

119

completed by the participant’s biological mother and father were significant. In a
previous exploration of the SCCT model, this measure was not significant in explanation
of college expectations (Ali & Saunders, 2006). However, that study did not look at
college performance but expectations of performance. It may be that parental educational
level may not impact a student’s interest in higher education, but may impact their
actions of entering and continuing in education. At least two potential explanations for
the significant findings in this study may be possible. Education in this study may be
acting as a proxy for family socioeconomic status, which has a link to educational level
(US Census Bureau, 2008). It may also be that parental expectations for children’s
educational attainment may correlate with their own educational levels, and parental
expectations and support for education have been hypothesized to influence their
children’s educational outcomes (Blalock & Patton, 1996; Hogan, Sandefur, & Shandra,
2006; Koehler & Field, 2003).

The findings regarding the differential impacts of age and sex of the participant
for individuals with learning disabilities has some support from previous longitudinal
research on educational outcomes. It has been hypothesized that students with learning
disabilities will take longer than students without a disability to enter postsecondary
education (Blackorby & Wagner, 1996). However, findings fi'om this study as well as the
only other study to look at graduates more than five years out of college, found that the
additional years out of school did not seem to have a large impact on college acquisition
in the population of individuals with learning disabilities (Murray et al., 2000). At least
two studies have found that females with learning disabilities are less likely to go on in

postsecondary education than are males (Rojewski, 1999; Sitlington & Frank, 1990). The

120

males and females with learning disabilities in this study have almost equal means in
terms of highest grade completed (11.59 for males and 11.55 for females).
Limitations

Learning disability measurements

The largest limitation to this study is the measure of learning disability within
NLSY97. The only measure within the instrument is a question asked of the participant’s
guardian in the first round of surveys. Specifically, the parent or guardian was asked if
the participant has a learning or emotional problem that impacts his or her education.
This question leaves a lot unanswered. Does the participant have a diagnosed learning
disability? Does the participant receive special education services? This limitation is
similar to limitations of other large representative data sets, such as the National
Educational Longitudinal Study (1988). A review of the disability indicators in NELS
found that parental identification of disability was potentially more accurate than other
indicators such as teacher identification (Rossi et al., 1997). This indicator also makes it
possible to include students with learning difficulties who have not been identified by the
school system. However, the use of this measure only leads to difficulty in interpretation
and generalization of the results to students with a diagnosed learning disability.

Secondly, the sample of individuals identified as having a learning disability is
mixed in with individuals who are identified by their parents as having Attention Deficit
Disorder (ADD). Previous research has found a significant co-occurrence of these
diagnoses. For example, between 20-40% of individuals with Attention Deficit
Hyperactivity Disorder (ADHD) also have a learning disability (Del’Homme, Kim, Low,

Yang & Smalley, 2007). This is a significant challenge to these data, as the two

121

populations, although overlapping, do have different issues. Based on how the question
was asked to the parent or guardian, there is no way to separate out the two groups in this
study. Therefore, all findings must be tempered with the fact that there is an unknown
proportion of this sample that may not identify as having a learning disability, but rather
an attention related disorder, or identify as having both diagnoses.

Third, having only one measure of learning disability leaves out the chance for
individuals to either receive a later diagnosis of a learning disability, or to have it
determined later that the individual does not have a learning disability. A number of
individuals will be diagnosed with a learning disability in high school or later, and
individuals with higher than typical intelligence scores are also likely to be diagnosed
with a learning disability later in their academic career (F erri, Gregg & Heggoby, 1997;
Holliday, Koller & Thomas, 1999; Mellard & Byme, 1993) Again, this is a limitation
with no solution in this study, unless that type of information was gathered along with the
transcript analysis and not provided at this time.

A final limitation comes fiom larger issues about the definition of learning
disabilities that are outside of the purview of this study. The definition of learning
disability is a topic of much debate. Because IDEA leaves the specifics of determining a
learning disability to the school districts, each state or school district may come up with a
different set of standards to determine learning disability (Gordon & Lewandowski, 2002;
Hallahan & Mercer, 2001; IDEA, 2004; “SLD Determination”, n.d.). The learning
disability community has also been examining and has come to consensus that alternative
measurements for learning disability that follow a Response to Intervention, rather than

an Aptitude/ Achievement discrepancy approach, should be utilized (N CRLD, 2007).

122

Lastly, there is a wide range of types of learning disabilities, with varying impacts, and
thus the specific impacts of this diagnosis on the individual may be very difficult to
measure. However, the information gathered from this study, with the aforementioned
limitations, still has relevance for current practice and policy issues.

Limitations of SCCT model testing using NLSY97

The main limitation to this study is the lack of variables available to test a number
of the theoretical constructs used in the SCCT model. For example, there were no
questions or measures of outcome expectations regarding educational or employment
outcomes obtained for the NLSY97. This led to the testing of only a part of the model,
rather than the model in its entirety. Additionally, because there was only one question
asked about academic self-efficacy, and it was only asked one time of participants, this is
likely an inadequate measure of the participant’s academic self-efficacy.
Implications for Practice with Adolescents with Learning Disabilities

Academic self-eflicacy

Previous research on academic self-efficacy in adolescents with learning
disabilities has shown both that these individuals either miscalibrate their levels of
academic self-efficacy, believing themselves to have higher academic skills than they do
(Klassen, 2002; Meltzer, Katzir, Miller, Reddy & Rodhti, 2004), or to have lower
academic self-efficacy than individuals without a learning disability (Elbaum, 2002; Field,
1996; Lackeye, Margalit, Ziv & Ziman, 2006). This study provides some support to the
hypothesis that measures of self-efficacy have a relationship to performance, as students
in the LD subgroup, similar to other two subgroups, showed a relationship between

believing they were more likely to continue on in schooling and their likelihood of

123

actually continuing on in schooling. Therefore, whether or not academic self-efficacy is
accurately representing actual academic ability, it is predicting academic performance,
which is consistent with the SCCT model, along with previous research for individuals
with learning disabilities.

The SCCT model hypothesizes that an individual’s self-efficacy beliefs are
indirectly influenced by their person inputs and background contextual affordances, and
directly by their learning experiences and outcome expectations. For individuals with
learning disabilities, the primary impacts of the disability, which may include academic
difficulties, and the secondary impacts of the diagnoses, which may include social skills
issues, psychological impacts, and stigma from peers, educators, and family, self-efficacy
beliefs, as well as outcome expectations relating to postsecondary education may be
negatively impacted. Some studies have shown that learning to contextualize the impacts
of the disability and to have a better understanding of the educational accommodations
needed may increase students’ beliefs in their ability to successfirlly complete higher
education (Cosden & Eliott, 1999; Field, 1996; Goldberg et al., 2003; Skinner &
Lindstrom, 2003). Several training programs that focus on self-determination skills for
adolescents with learning disabilities do focus on helping the individual have a greater
understanding of the impacts of a learning disability and to identify and advocate for
appropriate educational accommodations (Field et al., 2003; Griffith & Wade, 2001;
Mason &Field, 2004; Weymeyer & Field, 2004).

The importance of research on specific disability categories

Another important finding of this research is that the results for the group of

individuals identified with a learning disability are different from those of individuals

124

identified with another type of learning, physical, or emotional impairment. Although
research that looks at individuals with a wide variety of diagnosed disabilities may be
important in some areas, it is also important to do separate research on individual
disability categories, rather than completing blanket studies comparing individuals with
disabilities to individuals without a diagnosed disability. It also points to the need for
professionals working with individuals with disabilities to have an understanding of the
impacts of specific disabilities to insure that interventions and support are appropriate for
their client populations.

Recommendations for Future Research

Future research should explore a number of variables that could not be explored
based on the limitations of this dataset. For example, having more explicit questions
about career and academic interests, outcome expectations of the student and parents/
guardians of entering postsecondary education and measures of academic self-efficacy as
it relates to postsecondary education should be explored.

Other areas of future research may be to look at the way that measures of
academic self-efficacy, academic and career interests, and outcome expectations related
to postsecondary education, change over time for individuals with learning disabilities as
they enter and leave high school and make decisions about postsecondary education. One
key aspect of the SCCT model is the ways that career and academic interests and goals
change over time. This study did not reflect the changes individuals make over time as
proximal and distal contextual affordances, learning experiences, and the maturation
process change self-efficacy and outcome expectations, leading to changes in interests,

goals and actions (Brown & Lent, 2006; Lent et al., 1994, 1996, 1999).

125

Table 4.1

Means and Standard Deviations of Dependent Variable, Self-Efi’icacy Measure And

Demographic Variables

 

 

 

 

 

LD Mean LD SD NI Mean NI SD 01 Mean 01 SD
Highest Grade 11.58 2.14 12.75 2.42 12.64 2.41
Completed
Academic 37.1 38.1 44.8 38.3 44.1 38.1
Expectations in
Five Years
Mental Health 15.0 2.80 15.4 2.50 15.2 2.60
Scale
Age (12/31/96) 13.9 1.40 14.0 1.40 14.0 1.43

 

126

Table 4.2

Means and Standard Deviations of Proximal and Distal Contextual Variables

 

LD Mean LD SD NI Mean NI SD OI Mean 01 SD

 

 

 

 

 

Highest Degree 3.05 1.38 3.13 1.38 3.20 1.31
Mom

Highest Degree 3.00 1.33 3.22 1.50 3.22 1.43
Dad

Earned Income 13500 10700 14800 11600 14400 10800
Amount 433 1530 337 1280 417 1420
Government

Assistance

Number of Bio .300 .677 .350 .743 .360 .755
Children

 

127

Table 4.3

Means and Standard Deviations of Learning Experience Variables

 

LD Mean LD SD NI Mean NI SD 01 Mean OI SD

 

 

 

 

Overall GPA 2.53 63.3 2.84 .610 2.84 .610
ASVAB Score 45.0 29.0 44.5 29.0 47.8 29.4
School 11.0 2.10 11.1 2.10 11.1 2.13
Fairness

Educational 5.00 9.60 5.20 7.50 5.27 7.40
Vocational

Support

 

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143

CHAPTER FIVE

CONCLUSION
Summary of Manuscripts

The most consistent finding throughout this dissertation is the slow advances for
adolescents and young adults with learning disabilities in the context of higher education.
Although the number of individuals with learning disabilities has increased greatly in
higher education settings over the past 20 years (Henderson, 2001), the numbers of
graduates of higher education does not appear to be increasing at the same rate
(Blackorby and Wagner, 1995; McAllister, 2008; Murray, Goldstein, Nourse & Edgar,
2000; Sitlington & Frank, 1990; Rojewski, 1999; Wagner et al., 1991; Wagner et al.,
2005). Two findings from this study, in particular, emphasize this conclusion. The first is
that persons with learning disabilities, as recently as 2005, continue to have a
significantly lower rate of high school graduation and postsecondary enrollment than
both individuals without an identified disability and individuals with other learning or
physical impairments. The other finding is that the age of the individual with an
identified learning disability does not have a relationship to their educational level in this
study. Whereas the number of years of schooling increases for older participants in the
other two subgroups, older participants with learning disabilities have approximately the
same level of education as younger participants.

The current research on learning disabilities does provide a number of ideas to
support adolescents with learning disabilities towards a goal of postsecondary education,

and the evidence presented supports the benefit of practices such as: (a) self-

144

determination training (Field, 1996; Field & Sarver, 2003); (b) inclusion in general
education programs (IDEA 2004; McLeskey, Henry, & Axelrod, 1999; Wagner et al.,
2003; Wehmeyer & Field, 2004); (c) providing vocational training and preparation in
high school (Benz, Lindstrom, & Yovanoff, 2000; Evers, 1996; Griffith & Wade, 2001;
Wagner, Newman & Cameto, 2004) (d) social skills training and support (Bender, 2004;
Bryan & Burstein, 2004; Cartledge, 2005; Elias, 2004; Garrett, 2005; Kavale & Mostert,
2004; Mishna & Muskat, 2004; Raines, 2006); and (e) transition planning that begins in
early high school, actively involves the student in the process, and leads to informed and
prepared students and families for the challenges and changes in environment that
postsecondary education brings (Babbit & White, 2002; Brinkerhoff, 1996; Field &
Sarver, 2003; Gil, 2007).

This study also supports the use of the Social Cognitive Career Theory as a model
for explaining academic achievement in individuals with learning disabilities, as well as
in individuals with no identified impairment and individuals with other impairments than
a learning disability. A number of areas should be considered when thinking about the
SCCT model in regards to academic performance. First, the importance of academic self-
efficacy beliefs towards academic goals and actions (Bandura, 1986; Lent, Hackett &
Brown, 1994, 1996, 1999) is reinforced by this study. As self-efficacy is directly
impacted by the learning environment, and indirectly by person inputs and contextual
affordances (Brown & Lent, 2006; Lent et a1, 1994, 1996, 1999), an examination of the
ways this may lead individuals with learning disabilities to have low academic self-
efficacy beliefs is important. Inclusion in general education classrooms, for example, may

lead students with learning disabilities to believe that they will be capable of

145

postsecondary education, for example. Having “mastery experiences” (Bandura, 1986,
Lent et al., 1999) that provide positive educational or prevocational experiences, or
having role models of individuals with learning disabilities who have successfully
completed postsecondary education may also lead individuals with learning disabilities to
believe that they are capable of postsecondary education may be important as well.

The SCCT model theorizes that self-efficacy beliefs are indirectly, not directly
related to person inputs and background contextual affordances via the learning
experience of the individual. It is possible, however, that academic self-efficacy may be
directly, as well as indirectly, affected by person inputs for individuals with learning
disabilities more so than for other groups of students. For example, if persons with
learning disabilities, based on errors or deficits in metacognition, do rrriscalibrate their
academic self-efficacy levels as is posited by Klassen (2002), then it may not be adequate
to provide a supportive and inclusive learning environment in high school. In line with
the benefits of understanding, accepting, and learning to compartmentalize disability
(Higgins & Raskind, 2002; Stewart, 1999), it may be important for educators and families
to assist the individual with a learning disability to see their academic strengths and
challenges in a more realistic fashion (Cosden & Eliott, 1999; Field, 1996; Goldberg et
al., 2003; Skinner & Lindstrom, 2003). Perhaps if the adolescent with a learning
disability can accurately perceive his or her academic skills and deficits while in high
school, he or she can have more time to learn what accommodations and learning
techniques can be most effective before entering college. Ultimately, having accurate

academic self-efficacy beliefs may increase the likelihood of students with learning

146

disabilities choosing postsecondary environments that will emphasize their strengths and
support their area of learning needs.
Implications for Social Work Practice

On a daily basis, social workers, based on our many roles in human service
systems, work with individuals with a wide variety of disabilities. This may occur by
mandate, such as in school districts or mental health settings, where issues related
directly to the disability may be the focus of treatment, or in agencies where services may
be indirectly related to or independent of the individual’s particular disability
(Beaulaurier & Taylor, 2001; Jonson-Reid, Kontak, Citerman, Essma & Fezzi, 2004;
Quinn, 1995). Within all of these settings, social workers will work with individuals with
learning disabilities.

In order to work more effectively with individuals with learning disabilities, the
social worker needs to start by asking questions regarding disabilities in assessment and
treatment. This may be as simple as asking the client and his/her family if he/she has ever
been diagnosed with a learning disability. Even if the social worker is not familiar with
the particular assessment techniques for learning disability, he/she can assess for the
potential impacts the individual identified with a learning disability might be
experiencing, such as educational or vocational issues, social skills issues, or emotional
impacts. It is also important that social workers in all settings be prepared to assess the
potential psychosocial impacts of having a learning disability and work with the client to
understand the impact of disability discrimination and acceptance of disability on the
individual and his/ her systems (Beaulaurier & Taylor, 2001; Gilson & DePoy, 2002;

Mackelprang & Salsgiver, 1996, 1999).

147

The findings of this dissertation also have relevance for social work practice with
adolescents with learning disabilities and their families. For example, having knowledge
of the educational level of the adolescent’s parents may assist the social worker in their
assessment of the family support the adolescent may have towards postsecondary
education. Because learning disabilities appear to have genetic or biological components,
an adolescent with a learning disability is more likely to have a parent or sibling with a
learning disability or with ADHD (Del’Homme, Kim, Loo, Yang & Smalley, 2007).
Therefore, the family may experience disability within more than one member of the
family, potentially leading to higher levels of stress for all family members or to
multigenerational discrimination on the basis of disability diagnoses.

Finally, social workers in a wide variety of fields should be prepared to work on
goals such as self-determination and empowerment when working with adolescents with
learning disabilities. Social workers can enable the adolescent and his or her family to
view and enhance the strengths and skills the adolescent possesses. Even if the social
worker is not working within the educational system, he or she could work with the
adolescent towards educational and/ or vocational goals. Self-determination, social skills,
independent living skills and strengths based interventions can be provided in a wide
variety of settings, with a goal of supporting the adolescent to make and carry out plans
leading to academic and vocational success. Social workers may also encourage
adolescents with learning disabilities to explore the range of postsecondary educational
environments available, including four-year colleges, community/ junior colleges, and
professional/ trade schools to find the best fit and support before the student leaves high

school.

148

Implications for Social Work Education

In order to empower individuals with learning disabilities to reach their
educational, vocational, and other goals, it is important that social workers gain a stronger
understanding of this diagnosis, and particularly how it impacts individuals in
adolescence and young adulthood in relation to educational and career-related goals.
Although it is important to understand the issues and impacts of having a disability in
general (Gilson & DePoy, 2002; Mackelprang & Salsgiver, 1996, 1999), and that each
person will be uniquely impacted by his or her disability diagnosis, there are common
traits and challenges that are shared by many individuals with learning disabilities and
other specific disabilities. As this study emphasizes, looking at persons with disabilities
as a group may lead students and practitioners to overlook the particular strengths and
challenges of people in particular disability categories. It is important that social work
education helps students see the way that a specific disability diagnosis impacts the
system of the individual with a learning disability on multiple levels, and ways that social
workers can team with the individual with a learning disability to improve their person-
in-environment fit.

Yet social work education has not emphasized work with persons with disabilities
in general, or with persons with specific disabilities in particular (Galarnbos, 2004; Kirlin,
1986; Gilson & DePoy, 2002). This may lead social work students to perpetuate
stereotypical thinking about individuals with disabilities. In addition, a number of social
work roles continue to emphasize the medical model of disability, which emphasizes
“curing” the individual, rather than making changes at multiple systems levels and

focusing on individual strengths (Hahn, 1999). This continued deficit based perspective

149

on disability leads many persons with disabilities to feel distrust and animosity towards
social workers (Beaulaurier & Taylor, 2001; Gilson, Bricourt & Baskind, 1998).
Strengths based, empowering practices with persons with disabilities starts with an
educational foundation on work with persons with disabilities. Integrating content about
practice with individuals with disabilities can be done in almost all areas of social work
class content. Helping social work students see the ways that systems oppress and
discriminate against persons with disabilities, and to look at disability within the context
of human systems is crucial to appropriate work with persons with disabilities
(Mackelprang & Salsgiver, 1996, 1999). A number of lesson suggestions and plans,
course syllabi for required and elective courses with disability content, and textbooks on
social work practice with persons with disabilities are available (Gilson & DePoy, 2002;
Gilson, MacDuffie & Meyershon, 2002; Liese, Clevenger, & Hanley, 1999; Mackelprang
& Salsgiver, 1996, 1999; Rothman, 2003).
Implications for Future Research

The findings of this study, and the limitations of the data available for this study,
lead to a number of potential areas of future research. The NLSY97 dataset does have a
major limitation in the area of disability identification, in general, and learning disability
identification in particular. On the other hand, the data analysis done for this study
provides evidence that the sample of individuals identified as having a learning disability
may have a significant overlap with other populations of individuals identified with a
learning disability. For example, the educational outcomes, proportion of males and
females, and employment outcomes are similar in this sample to samples of individuals

with learning disabilities in other studies (Blackorby and Wagner, 1995; Murray et al.,

150

2000; Sitlington & Frank, 1990; Rojewski, 1999; Wagner et al., 1991; Wagner et al.,
2005) With that in mind, future research looking at educational outcomes as the NLSY97
sample ages would be an important addition to the limited longitudinal data available on
individuals with learning disabilities. It may also be useful to look at the vocational
trajectories and incomes of individuals with leaming disabilities using this dataset. One
of the strengths of this dataset is that it measures each job the individual has engaged in
each year. These data could be used to look at the types of professions and incomes
individuals with learning disabilities enter or transition into over time.

Future testing of the SCCT model as it applies to individuals with learning
disabilities should also be completed. For example, tests of individual relationships
between some of the SCCT constructs as they relate to individuals with learning
disabilities may help practitioners find new ways to assist this population. Future testing
of the SCCT model may also look at the changes in measurements such as self-efficacy
and outcome expectations for students with learning disabilities during their final years of
high school and their first years out of high school. Do self-efficacy and outcome
expectations decrease when the individual takes high school exit exams? What kinds of
transition planning and interventions best support goals of higher education? What is the
link between self-efficacy, outcome expectations, and self-determination training, for
example?

Finally, it is important to add to the research done on students with learning
disabilities who are currently in college. For example, are there different postsecondary
success rates for individuals who are diagnosed with a learning disability in college

versus being diagnosed before college? One study found that over one third of students

151

with learning disabilities in that college setting were not diagnosed until college (F erri,
Gregg, & Heggoby, 1997). What makes these populations of students similar or different
from each other? Another area of research is to look at college persistence and learning
disability. What factors lead a student with a learning disability to persist and succeed in
postsecondary education? What can be learned from individuals with learning disabilities
that are successful in college that can be used to assist other individuals with learning
disabilities? Some of the factors that could be explored are individual variables such as
self-efficacy levels, outcome expectations, and other types of person inputs. Other
variables that might be explored include family systems, educational and vocational
experiences in high school, and perceptions of the college learning environment.

Since the IDEA amendments of 1997, smooth and successful educational and
vocational transitions have been emphasized for individuals with disabilities in the public
school system. At this point, however, the emphasis on transition planning does not seem
to have a significant impact on the educational outcomes for individuals with learning
disabilities. Higher education is more important than ever for financial independence and
stability. It is important to continue to research the factors impacting these educational
disparities and to seek intervention strategies at the individual, family, and educational

systems levels to support individuals with learning disabilities in higher education.

152

Addendum: Reflections on a Multiple Manuscript Dissertation

As this is my first and last dissertation, it is impossible to compare all of the pros
and cons of completing a dissertation as a series of manuscripts versus one integrated
paper. I can give several reflections on this process, however. First, I can say that
separating the work of the dissertation writing was easier than I would have imagined.
Although these three papers overlap each other, I was able to see them as distinct pieces
of work with different structure, audience, and goals. One challenge of trying to write
three concise pieces for publication, however, was trying to balance brevity with the
desire to express what I had learned. For example, Chapter Two includes a discussion of
the implications for social work practice in a variety of practice settings. If I were sending
this manuscript out for potential publication, however, I would likely focus the discussion
more on one area of practice. I also provide more tables in Chapters Three and Four than
would ever be allowed in a publication, because I wanted to make the process of analysis
clear. Overall, I can say that this was a good choice for me, and provided me with more

chances to write in a manner consistent with writing for potential publication.

153

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Appendix A: [RB Approval

159

 

 

OFFICE OF
REGULATORY
AFFAIRS

Human Research
Protection Programs

BIOUEDCAL & HEALTH
WW

BOARIHBIRB)

OOHIUNITY RESEARCH
W REV“
BOARD (ORR!)

MW
mmrauwmou
WW
whom

202 Girls it!

E!!! WIN. W
45324-1040
517-355-2100

Fax: 517m

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GRIPS: «Imam

 

 

N ' V E 5 ' T Certification ior
use of an

December 3,2007 approved pupiic
date iiie

To: Rene Herold
232 Baker Hall
College of Social Science

Re: Certification # PDO7-013

Variable Affecting the poet-school outcomes of students with learning
disabilities

Thank you for submitting your certification for the use of an approved public data file.
The Social Science I Behavioral I Education Institutional Review Board (SIRB) has
received and accepted your certification.

You may conduct this research project using the approved data file until its completion
without any further review from the SlRB. if your project expands to include human
subjects or identifiable data from human subjects you will have to submit a commete
application to SIRB.

In the future, if you wish to conduct another project, not directly related to this project, or
use another approved data file not listed on this certification, please submit another
certification form. This will allow you to continue your work without IRB review. In
addition, the certification allows us to keep a total of all human research being perfon'ned
and gives credit to you and your department in any reports or statistics.

Please use the certification number listed above on any tonne submitted which relate to
this project, or on any correspondence with SIRS.

Thankyouforyourcooperatlon andgood luckwith yourresearch. lfwecan beoffurther
assistmce. please contact us at 517-355-2180 or via email at W.

Sincerely,

74/2:

Peter Vasllenko ‘ '
Director, Human Research Protection Program

160