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DEVELOPING A PREDICTION MODEL FOR VOCATIONAL REHABILITATION
CLIENTS USING DEMOGRAPHIC, LOCUS OF CONTROL, WORK
MOTIVATION AND WORK HISTORY VARIABLES:

AN EXPLORATORY STUDY

BY

Madan Mohan Kundu

A DISSERTATION

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

DOCTOR OF PHILOSOPHY'

Department of Counseling, Educational Psychology
and Special Education

1983

© Copyright by
MADAN MOHAN KUNDU

1983

'Y‘l

()

I!

“a

ABSTRACT

DEVELOPING A PREDICTION MODEL FOR VOCATIONAL REHABILITATION
CLIENTS USING DEMOGRAPHIC, LOCUS OF CONTROL, WORK
MOTIVATION AND WORK HISTORY VARIABLES:

AN EXPLORATORY STUDY
BY

Madan Mohan Kundu

The rate of successful rehabilitation is declining
nationally. In this study an attempt was made to develop a
prediction model relating clients' domains of predictor
variables, independently andjxxlinear combination,to outcome
criteria. This diagnostic model would determine the probabi-
lity of success early in the rehabilitation process, allowing
counselors to plan and implement innovative interventions to
facilitate successful closure.

The subjects were 115 voluntary Vocational Rehabilita-
tion clients from Louisiana, Michigan and New York, who were
ready for employment in status 20 for not more than 60 days.
Five instruments were administered to elicit clients' charac-
teristic predictors: Demographic Questionnaire, Locus of
Control Scale, Modified Locus of Control Scale, Vocational
Opinion Index and a Work History Questionnaire. The outcome
measures, the employment status and associated characteris-
tics, were collected from both counselors and clients six

months later. Two multivariate correlational methods,

Madan Mohan Kundu

discriminant analysis and canonical analysis, were used to
delineate predictive relationships.

Demographics were significant in differentiating the
employed from the unemployed. Hypothesized increases in work
motivation and employment for those with more internal locus
of control were not supported; however, the internals
obtained employment earlier than the externals. No relation-
ship was found between work motivation measured by the
VOcational Opinion Index and employment status.‘

There exists a significant relationship between the
multidimensional a3pects of pre-VRS work characteristics and
post-VRS work characteristics.

The data support the notion that work experience prior
to Vocational Rehabilitation significantly affects the Voca-
tional Rehabilitation outcome. The relative contribution of
each domain was examined. Before and after disability work
variables in combination make the greatest contribution in
both the independent and the linear model, followed by the
after disability work variables. Although the before work
variables were not significant with respect to the model,
excluding them made the total model non-significant.

The current study demonstrated the feasibility of
developing an outcome prediction model for various disability
groups. The development of a generalized prediction model
for all rehabilitation clients requires the inclusion of all

domains significantly affecting outcome.

In memory of my father
the late Narayan Chandra Kundu
who brought me into this world to enjoy its beauty,

and who planted the seed of learning in my mind and
asked me not to fail nurturing it, even in adversity.

AND

In memory of a fatherly person
the late David Hodges Swanson

who helped advance knowledge and wisdom in this soul.

iii

ACKNOWLEDGEMENTS

Twelve years ago, Swami Lokeswarananda sowedma concept
which was far beyond my dream and imagination--that I must
have a Ph. D. Dr. Gregory A. Miller, my advisor during my
Masters and partly during my doctoral studies, was persistent
in corresponding with me since 1969 and led me to come to
Michigan State University in 1975. The contribution of Mr.
and Mrs. David H. Swanson, and Jim Swanson was god-inspired
in advancing my knowledge of rehabilitation, and for their
support in my endeavor, they are gratefully acknowledged.

My long-standing desire to study the impact of PL-480
Projects in India, and thereby to develop a model for the
developing nations, was abandoned with the premature demise
of my doctoral advisor, Dr. John E. Jordan. He was a scholar
and statesman of national and international repute, my
mentor, colleague and a close friend. I miss him very much.

My respect and appreciation are given to my current
advisor and dissertation chairman, Dr. James R. Engelkes.

He guided me superbly in the juncture of many critical moments
in my doctoral ordeal. His openness and continual support

in reorganizing the procedure, design and methodology for the
best, played a major role in the successful completion of

this project. He never hesitated to give much of his personal

iv

time for my needs. I am deeply indebted to him.

I am thankful to my committee members, Dr. Herbert M.
Burks, Jr., Dr. Richard G. Johnson and Dr. George W. Fair-
weather. Dr. Burks, a teacher of excellence, inspired me
with his perfection. Dr. Johnson was always available for
discussions and suggestions. The acumen of statistical
knowledge in questionnaire design and the strategy of data
collection was from Dr. Fairweather.

Dr. Esther Fergus and Brian Mavis of Ecological
Psychology have made significant contributions imxprogramming
and analyzing the data. Roy Feber, SPSS Consultant at the
Computer Center, Khalil Elaian and Betsy Becker of the Office
of Research Consultation were of great help during the
Discriminant Analysis.

I wish to gratefully acknowledge the contributions
made by Dr. David Vandergoot in conceptualizing the research
project and the partial support provided by his grant from
the National Institute of Handicapped Research, U. S. Dept.
of Education, Washington, D. C., (Grant No. 60008200042) to
Employment Research and Training Center, Human Resources
Center, Long Island, New York.

I would also like to acknowledge the partial support
provided by the Research and Training Center, University of
Wisconsin-Stout through the grant from the National Institute
of Handicapped Research, U. S. Dept. of Education, Washington,

D. C., and the personal involvement of Dr. Charles C. Coker.

Mr. Lester Soileau, Executive Director, Louisiana
Division of Vocational Rehabilitation was instrumental in
saving the project from collapsing. I am grateful to him
personally and to Mr. Everett Wright, Program Supervisor who
efficiently facilitated the collection of data in Louisiana.

My appreciation is due to Dr. Robert Struthers,
Michigan Rehabilitation Services; Mr. Ken Buelow, State
Technical Institute and Rehabilitation Center, Plainwell,
Michigan; and Penny Swiger of Lansing Job Club for their
assistance in data collection.

I would like to acknowledge the sacrifice of all the
district supervisors, counselors, facility administrators,
placement coordinators,and especially the rehabilitation
clients in Louisiana, Michigan and New York. Without their
cooperation the project would have no existence.

I have been blessed with friends and well wishers;
they all have contributed in many tangible and intangible
ways, among them: Dr. & Mrs. Kalidas Bhadra, Lester Cadwell,
Dr. Donald Galvin, Asif Khandker, Dr. Robert Keith, Mr.
Sudhin Chandra Roy, Dr. Peter Soderbergh, Terry Vander-Molen,
Dr. Joseph Varghese and Mr. & Mrs. K. Voss. I hope I will be
forgiven if I have failed to acknowledge anyone.

A friend in need is Gerald Juhr. He was next to me in
my happiness and adversities, shared with me my pains during
this ordeal and painstakingly edited my work. Jerry's and
his wife, Janneke's friendship is something that I will

always treasure.

vi

Back home, on the other side of the world, my mother,
brothers and sisters, extended family and well-wishers
offered continual moral support as I pursued my education.

To my beloved wife, Pravati, whose love, support,
endurance and sacrifice have seen me through this entire
endeavor, I express my deepest appreciation. She cared
lovingly for our daughter, Uma, and both of them waited

patiently for Daddy's life to return to normal.

vii

LIST OF

LIST OF

LIST OF

CHAPTER

I.

II.

TABLE OF CONTENTS

TABLES . . . . . . . . . . . . . . . .
FIGURES . . . . . . . . . . . . . . . . .

APPENDICES I O O O O O O O O O O O O O O 0

INTRODUCTION . . . . . . . . . . . . . . .

Context . . . . . . . . . . . . . . . . .
Statement of the Problem . . . . . . . . .
Need for the Study . . . . . . . . . . . .
Purpose of the Study . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . .
Questions to be Addressed by the Study . .
Definition of Terms . . . . . . . . . . .
Overview . . . . . . . . . . . . . . . .

REVIEW OF THE LITERATURE . . . . . . . . .

Introduction . . . . . . . . . . .
Clinical Versus Statistical Prediction . .
Prediction of Rehabilitation Outcome Using
Demographic Variables . . . . . . . . . .
Prediction of Rehabilitation Outcome Using
Psychological Variables . . . . . . . . .
Prediction of Rehabilitation Outcome Using
A Linear Combination of Demographic and

Psychological Variables . . . . . . . . .
Locus of Control . . . . . . . . . . .
Locus of Control and Physically Disabled .

Locus of Control and Psychological Disorders

Changes in Locus of Control . . . . . . .

Generality-Specificity and Unidimensionality-

Multidimensionality . . . . . . . . . . .
Locus of Control and Rehabilitation . . .
Work Motivation . . . . . . . . . . . . .
Work History . . . . . . . . . . . . . . .
Summary . . . . . . . . . . . . . . . . .

viii

PAGE

xi

.xiii

xiv

10
11
15
16
17
21

22

22
23

36

47

52
57
61
66
69

74
78
81
89
93

III.

IV.

PAGE

METHODOLOGY . . . . . . . . . . . . . . . . . . 97

Introduction . . . . . . . . . . . . . . . . 97
Sample . . . . . . . . . . . . . . . . 98
Rationale . . . . . . . . . . . . . . . . 98
Procedure . . . . . . . . . . . . . . . . . 99
Data Collection in Louisiana . . . . . . . . . 100
Data Collection in Michigan . . . . . . . . . . 104
Data From New York . . . . . . . . . . 105
Follow—up Data . . . . . . . . . . . . . . . 106
Instrumentation . . . . . . . . . . . . . . . . 107
Demographic Questionnaire . . . . . . . . . . . 108
Locus of Control Scale . . . . . . . . . Q . . 109
Modified Locus of Control Scale . . . . . . . . 112
Vocational Opinion Index . . . . . . . . . . . 114
Work History Questionnaire . . . . . . . . . . 120
Follow-up Questionnaire . . . . . . . . . . . . 120
Hypotheses . . . . . . . . . . . . . . . . . . 121
Research Design . . . . . . . . . . . . . . . . 122
Analysis . . . . . . . . . . . . . . . . . . . 125
Summary . . . . . . . . . . . . . . . . . . . 127

RESULTS . . . . . . . . . . . . . . . . . . . . 130

Introduction . . . . . . . . . . . . . . . . 130
Description of Sample . . . . . . . . . . 131
Demographics . . . . . . . . . . . . . . . 131
Psychological Characteristics . . . . . 135

. . . . . 135
. . . . . 137
. . 139
. . . . . 141
. . . . . 145
. . . . . 145
. . . . 150

Locus of Control . . . . . . .
Work Motivation . . . . . .

Work History . . . . . .

Outcome . . . . . . . . . . . .
Examination of Hypotheses . . .
Hypothesis 1 . . . . . . . . .
Hypothesis 2 . . . . . . . . .

Discriminant Analysis . . . . . . . . . 153
Discriminant Function . . . . . . . . 154
Standardized Coefficients . . . . . . . . . 155
Raw or Unstandardized Coefficients . . . . . 155
Centroid . . . . . . . . . . . . . . . . . . 156
Measures of Association . . . . . . . . . 156
Test of Significance . . . . . . . . . . . 157
Classification of Objects . . . . . . . 158
Stepwise Procedure . . . . . . . . . . 159

Hypothesis 3 . . . . . . . . . 159
Hypothesis 4 . . . . . . . . . . . . . 164
Hypothesis 5 . . . . . . . . . . . . . . 168
Hypothesis 6 . . . . . . . . . . . . . . . . 174
Hypothesis 7 . . . . . . . . . . . . . . . . 182
Summary of the Hypotheses Tested . . . . . . . 188
Summary of Results . . . . . . . . . . . . . 188

ix

V. DISCUSSION . .

Introduction .
Discussion . .
Limitations . .
Conclusions . .

Implications for Future

APPENDICES . . . . . .

BIBLIOGRAPHY . . . . .

Research

PAGE
194

194
194
212
214

217
220

251

TABLE

3.2

4.1

4.2

4.5

4.7

4.8

4.9

LIST OF TABLES

PAGE

Number of Clients Who Agreed to Participate,

Number Rejected, and Sample from District

Offices and Training Facilities in Louisiana,

and Training Facilities in Michigan and New '

York . . . . . . . . . . . . . . . . . . . . . . 103

Coefficients of Homogeneity for the Vocational
Opinion Index from Transition to Work III . . . . 118

Demographic Characteristics of the Sample . . . . 132

Means, Standard Deviations, Medians and Ranges
of the Locus of Control Scale (LOC) and the
Modified Locus of Control Scale (MODLOC) for the ,
Sample . . . . . . . . . . . . . . . . . . . . . 136

The Vocational Opinion Index (VOI)-Three
Dimensions and 13 Factors for the Sample . . . . 138

Number of Jobs Held Before Disability, After
Disability, and at Follow-up as Reported by the
Counselors, Clients and Computed Best Values . . 140

Comparison of Mean Work Characteristics Before
Disability, After Disability, at Follow-up and
Computed Best Values . . . . . . . . . . . . . . 142

Outcome Measures at Follow-up as Reported by the
Counselors, Clients and Computed Best Values . . 143

Employment Status at Follow-up as Reported by
the Counselors, Clients and Computed Best
values 0 O O I O O O I O O O O O O O O O O O O O 144

Sources of Support at Referral, at Testing, at
Follow-up as Reported by the Counselors, Clients
and Computed Best Values . . . . . . . . . . . . 144

Chi-square Values Between the Locus of Control
(LOC) and the Vocational Opinion Index (VOI) . . 148

xi

4.14

4.24

Chi-square Values Between the Modified Locus of
Control (MODLOC) and the Vocational Opinion

Index (VOI) . . . . .

Chi-square Values Between the LOC, the MODLOC

and Employment Status

After VRS . . . . . . . .

Pearson Product Moment Correlation Coefficients
of the LOC and the MODLOC with Number of Months

Worked . . . . . . .

Discriminant Analysis
MethOd O O O O O O O

Stepwise Discriminant

Discriminant Analysis
by Direct Method . .

Stepwise Discriminant
the VOI O O O O O O O

Stepwise Discriminant
After Disability Work

Stepwise Discriminant

of Demographics by Direct

Analysis of Demographics

of 13 Factors of the VOI

Analysis of 13 Factors of

Analysis of Before and
Characteristics . . . . .

Analysis of After

Disability Work Characteristics . . . . . . . .

Summary Statistics of

Discriminant Function

Five Linear Combinations

Coefficients for the

Stage II Linear Combination by Direct Method .

Stepwise Discriminant

Function Coefficients for

the State II Linear Combination . . . . . . . .

Canonical Correlational Analysis of Before and

After Disability Work
Work Variables . . .

Variables with Post VRS

Canonical Correlational Analysis of After
Disability Work Variables with Post VRS Work

Variables . . . . . .

Summary of Hypothesis~

Tested . . . . . . . . .

xii

151

161

163

165

167

170

173

175

180

181

184

189
190

LIST OF FIGURES

FIGURE PAGE
5.1 Schematic Diagram of Client Groups . . . . . . . 207

5.2 Schematic Diagram of Client Groups and Their
Prediction Rate Based on Work History . . . . . . 207

xiii

LIST OF APPENDICES

Supervisors

APPENDIX
A. Correspondence . . . . . . . . . .
B. Introduction to the Counselors and
Introduction to the Clients . . .
C. Consent and Release of Information
D. Demographic Questionnaire . . . .
E. Locus of Control Scale . . . . . .
F. Modified Locus of Control Scale .
G. Vocational Opinion Index . . . . .
H. Work History Questionnaire . . . .
I. Follow-up Questionnaire - Counselors . . . . .
Follow-up Questionnaire - Clients
J. Follow-up Letters . . . . . . . .

xiv

PAGE
220

225
228

231
233
234
238
239
246

247
248

249

CHAPTER I

INTRODUCTION

Context

The passage of the Vocational Rehabilitation Act of
1920 under the Smith Fees Act of 1920 (P. L. 66-236) marked
an era in the field of Vocational Rehabilitation of the
United States. The act established the Federal-State
Vbcational Rehabilitation Program for the purpose of pro-
viding rehabilitation services to disabled veterans and
persons with disabilities. The basic intent of these
services was to enable disabled people to participate in
society in their fullest capacity possible. The original
concept and philosophy of services remain as relevant today
as they were sixty years ago. However, they have passed
through a number of trials and tribulations in the form of
amendments, expansions, changes in the priority needs,
eligibility criteria, employment and service deliveries, and
outcome criteria. Over the years, qualifying disabling
conditions have been expanded to include mental retardation,
mental illness, severe disabilities, alcoholism, drug
addiction, and most recently behavioral and character dis-

orders.

Since the passage of the Vocational Rehabilitation
Act Amendments of 1954, a growing concern has been to extend
services to severely disabled persons in greater numbers,
to provide comprehensive services, and to support activi-
ties of independent living. Accordingly, the Rehabilitation
Act of 1973 (P. L. 93-112) extended the eligibility criteria
for persons with severe disabilities who previously were
denied services. Subsequently, the Rehabilitation Compre-
hensive Services and Developmental Disabilities Amendments
of 1978 (P. L. 95-602) made provisions for independent
living, non-vocational rehabilitation services, facilities
for independent living and recreational activities. Reha-
bilitation counselors are faced with a tremendous complex-
ity of service needs to meet the outcome criteria mandated
by the act.

Rehabilitation means restoration of the residual
abilities and skills through a process of gaining something
new or regaining something lost. Among others, the main
outcome criterion of success of vocational rehabilitation
services is the placement of the client in satisfying and
remunerative employment or, in other words, Status 26
closure. Status 26 closure means that the client received
appropriate diagnostic services, completed an individual-
ized written rehabilitation plan, participated in employ-
ment satisfactorily for at least 60 days and met the goal

of rehabilitation services.

"Without Placement, There Is No Rehabilitation" was
the theme of a conference held in 1973 (Rusalem & Malikin,
1976). The original 1920 Act designated placement as one
of the major services. But it was not until passage of the
1973 Act that the importance of placement services was made
explicit. Title V of the latter act, under sections 501
through 504, provided counselors with four major tools to
enhance employment opportunities for their clientele.
Counselor effectiveness is being judged to a large extent
by the “number games," the yardstick of successful closure
at Status 26.

New emphases on the outcome criteria have created
strains within the Vocational Rehabilitation System.
Vbcational rehabilitation counselors face the challenge of
serving disability groups with which they are unfamiliar.
Even with the counselor's best effort, it is often impos—
sible to place them as the severity of disability itself
imposes certain restrictions for effective placement. The
labor market is not yet educated and prepared to absorb
them. Also, the current economic depression in the United
States restricts and frustrates the counselor's work. This
results in fewer Status 26 closures and more Status 08
closures (closed from referral, applicant, or extended
evaluation statuses and not accepted for VR Services),
Status 28 closures (closed for other reasons after Indi-
vidualized Written Rehabilitation Plans were initiated),

and Status 30 closures (closed for other reasons before

Individualized Written Rehabilitation Plans were initi-
ated). Counselors need interventions that would assist
them in meeting the rehabilitation outcome criteria and
thus meet the challenges presented by the Rehabilitation
Act.

Not only the counselor but the agency as a whole is
subjected to strict accountability. The program adminis-
trator and the legislature who must approve the budget for
the vocational rehabilitation program welcome evidence of
increased cost-effectiveness in order to support requests
for appropriations in competition with other human service
agencies. Indeed, the higher standards and higher rates of
effective rehabilitation are deemed urgent as a measure of
counselor effectiveness as well as the effectiveness of the
agency and its administrators, in order to warrant better

funding in the future.

Statement of the Problem

 

Rehabilitation counselors working within the
Federal-State Vocational Rehabilitation System have
acknowledged the frustration of meeting the legislative
requirement of accountability by closing their clients
successfully at Status 26. Without this c10sure status,
VOcational Rehabilitation Services are considered a failure
in achieving the chief outcome criteria of the rehabilita-

tion process.

National data from the R—300 Case Form indicated
that for the Fiscal year 1972 (July 1, 1971 through June
30, 1972) there were 67,962 persons rejected by Vocational
Rehabilitation Agencies (closure Status 08, 28, 30) because
of the severity of their impairment. Bolton (1972)
reported that in the Arkansas Vocational Rehabilitation
Program approximately two-thirds of the clients who
received services were successfully closed and one-third
did not achieve rehabilitation status. Similarly, in the
VOcational Rehabilitation Program in the State of Oregon
the Status 26 closure was 67% vs. Status 28 and 30 closure
33% for the fiscal year 1978 and 1979 (WOrral & Vandergoot,
1980, 1982).

The Report of the Comprehensive Service Need Study
conducted by the Urban Institute and initiated by Congress
estimated that in 1975 there were 23.3 million non-
institutionalized disabled (of all degrees of severity)
persons of working age 18-64 in the United States. The
report further stated that data from the Vocational Reha-
bilitation Program indicated that of roughly one million
disabled people who apply for Vocational Rehabilitation
Services each year, only half of them actually receive
services and the other half of the applicants are being
rejected as "unemployable' due to the severity of the
handicapping condition and various other reasons.

The findings of the Urban Institute (1975) study

corroborate the national data reported by the Rehabilitation

Services Administration (1980). The acceptance rate for
rehabilitation clients to vocational rehabilitation serv-
ices declined nationally over the last decade, 53.7% in
1970 to 45.7% in 1980. However, for the State of Michigan
Rehabilitation Services, the converse was true. It showed
a gradual increase over the same period from 61.4% in 1970
to 68.8% in 1980 (Michigan Rehabilitation Services, 1982).
National data also indicate that there is a gradual de-
clining trend of rehabilitation or Status 26 closure (for
those who have been accepted for services) of 77.2% in 1970
to 64.5% in 1980. For the State of Michigan these figures
were 72.9% in 1970 to 48.4% in 1980. In 1980 Michigan's
rehabilitation rate was 48.4%, the third lowest in the
country following New Mexico 48.2% and New York 43.7%.
This significant lower rate of rehabilitation for Michigan
may be due in part to the depressed economy and high
unemployment rate in the State and higher acceptance rate
for services. In 1981, the three lowest rehabilitation
rates in the nation were New York 52.5%, Michigan 45.5% and
California 42.3% while the national average was 61.9%.

The Urban Institute (1975) made a national survey
of persons closed for the reason of severity in Status 08,
28, and 30 within one to two years of closure during the
period from September 1, 1972 through August 31, 1973. Of
those who consented to participate in the study, the most
disabling conditions were orthopaedic handicaps, followed

by cardiac and circulatory condition. Even though half of

them were completely independent in self-care and mobility
measured by the Barthel Index, the report stated:
A disturbing finding, however, was that 57% of
the younger persons (aged 16-40) were physically
independent or only slightly dependent. It was
not clear why people of prime working age who
were physically fit had been rejected because of
severity. Further analysis showed that almost
one-half of these people had recent work exper-
ience and wanted to work. (p. 150)

Among the respondents 39% needed no additional
services. The 61% who needed services indicated an average
of three services per person. These included vocational
training 21%, transportation 18%, physical therapy 10%,
vocational placement 25%, vocational counseling 14%, and
education 12%. Most of the services needed fell within the
scope of Vocational Rehabilitation Services, yet Vocational
Rehabilitation rarely provided these services (Urban
Institute, 1975).

The entire rehabilitation process, from the higher
administration through the counselor, is affected by the
rejected severely disabled populations or eventual dropouts
that require multiple services of a comprehensive nature
over a longer period of time. This attrition raises the
”cost-benefit" factor for Federal-State Vocational Rehabili-
tation Programs. The program may be ineffectively utilizing
its personnel and resources. Further, the inability to
serve effectively this population poses an overwhelming
problem in terms of wasted potential.

The idealistic outcome criteria of Status 26 closure

mandated by legislation are quite laudable but often

unattainable. The system of accountability is being criti-
cized from many corners (Miller & Barillas, 1967; Reagles,
Wright, & Butler, 1971, 1972; Lawlis & Bozarth, 1971;
Conley, 1973; Thomas, Hanke, & Pool, 1976; Rusalem &
Malikan, 1976) due to inherent problems associated with the
dichotomous outcome criteria of successful vs. unsuccessful
closure. The quantitative measures of accountability of
the present system over the qualitative gain in the client
have the following drawbacks:

(a) The system encourages the counselor to select a
client with easily obtainable goals rather than goals which
would be in the best interest of clients;

(b) To meet the ”quotas” of successful closure,
counselors often prematurely close cases to fulfill the
"number games';'

(c) The system encourages the counselor to avoid
serving clients who require a great deal of counselor time
and effort, for whom the probability of non-success is
higher (Status 08 closure); and

(d) The system fails to reward counselors by taking
into account the counselor's time, effort, and creativity
(for clients who are closed at Status 28 and 30) in meeting
the vocational and extra-vocational needs of the most
severely disabled clients.

Since enactment of the 1973 Rehabilitation Act, the
rehabilitation counselor serves a more severe and complex

caseload, and coordinates a conglomerate of client services

over a more extended period of time than ever before. As a
result, the number of Status 26 closures is falling nation-
ally and the 28 and 30 closures are increasing. In general,
the characteristics of these unsuccessfully closed clients
are severely disabled, severe personality disorder, hard-
core unemployed, less educated, public offender, emotional
problem, behavioral disorder, drug addict, public assist-
ance and welfare recipient, not motivated, too old, etc.

Is it possible for the counselor to identify these poten-
tial high-risk non-rehabilitants at the time of acceptance
into the Vocational Rehabilitation Services?

Counselors could use their intuitive clinical judg-
ment to diagnose these high risk clients based on their
experience in handling different types of caseload. Clini-
cal and intuitive judgment, however, has its own drawback.
It emphasizes an indepth understanding of a conglomerate of
client behaviors (Rubin & Roessler, 1979). Rehabilitation
counselors who spend less than one-third of their time in
counseling and guidance (Rubin & Emener, 1979) will seldom
achieve any prediction standard for each client by clinical
and intuitive judgment. This may lead to bias. Therefore,
a statistical prediction model has been favored in the
research literature (Meehl, 1954, 1957; Sawyer, 1966;
Miller, Kunce, & Getsinger, 1972; Bolton, 1972; Weikel &
Johnson, 1974; Berkowitz, Englander, Rubin, & Worral, 1975:

Cook 1980; Anastasi, 1982).

10

Need for the Study

 

The Rehabilitation counselor in the State-Federal
Vocational Rehabilitation System is in immediate need of a
simple, inexpensive, instrument/tool/model which is easy to
administer and capable of being used by the counselor with
basic arithmetic skills to identify/diagnose the high risk,
probable non-rehabilitant clients early in the rehabilita-
tion process. The intent of developing such a model is not
to screen out the high risk, probable non-rehabilitants from
the program. This would be most unethical. The model
would be able to identify those who may need additional
help or who may benefit from increased or novel intervention
and treatment strategies to prevent failures or unsuccessful
closures at Status 28 and 30. Findings from this model
would alert the counselor to the idiosyncratic needs, capa-
cities, and interests of these exceptional clients, and
facilitate some creative intervention.

The challenge associated with the accountability of
performance of the counselor and the agency in states like
Michigan, New Mexico, and New York in particular and the
nation in general, and the legislative mandate of the Reha-
bilitation Acts for successful closure demand immediate
response in the development of and validation of such
devices. The major focus of this response is the enhance-
ment of employment potential of the rehabilitation client.

This concern for predicting client probability of success

11

in any rehabilitation program is a researchable matter, and

this study is directed to that end.

Purpose of the Study

 

A review of literature revealed a number of studies
regarding identification and prediction of success and
failure for handicapped clients in vocational training and
rehabilitation. Most of these studies used demographic
variables such as age, sex, race, education, marital status,
number of dependents, age at onset of the disability, and
the type of disability to predict the rehabilitation outcome
criteria of successful employment. Some studies attempted
to find how the public assistance and welfare recipient, or
social security disability status related to the outcome
variable. Demographic variables were useful in predicting
outcome for some types of handicapped populations but not
for others. Using demographic variables, Aiduk and
Langmeyer (1972) could not predict outcome for emotionally
disabled clients and concluded that certain variables would
predict outcomes for some sub-populations over others.

Some encouraging results in terms of feasibility
and effectiveness of a predictive model with demographic
variables are present in the literature (Novis, Marra, &
Zadrozny, 1960: DeMann, 1963; Kunce & Miller, 1972: Miller,
Kunce, & Getsinger, 1972; Bowmen & Micek, 1973; Anthony &

Buell, 1974: Weikel & Johnson, 1974; Worral & Vandergoot,

12

1980, 1982). However, it seems logical to explore other
domains of the independent predictor variables which have
correlation with the criterion variable, in this case
employment status. For a rehabilitation client to be ready
for work, to obtain a job, and to maintain that job leading
to upward mobility requires certain stable personality
characteristics. A client with a good personal adjustment
to his disability, and good intrapersonal and interpersonal
abilities, enhances his employment potential.

Research literature suggests that psychometric
variable(s) combined with demographic variables could
possibly increase predictive efficiency of the model
(DeMann, 1963; Miller, Kunce, & Getsinger, 1972; Worral &
Vandergoot, 1980, 1982). Locus of Control (Rotter, 1966),
a personality construct which emerged from social learning
theory, was found to be useful in predicting classroom
success, academic achievement, confidence, expectation,
aspiration, professional achievement, intrapersonal and
interpersonal adjustment, satisfaction in life and job,
success in employment, etc. Research studies have pro-
liferated with application of the Locus of Control using
children, adults, aged, ethnic minorities, socio-economic
classes, socially disadvantaged, emotionally disturbed, and
psychiatric disordered populations. However, only a few
studies have used the physically handicapped as subjects

(Lipp, Kolstoe, James & Randall, 1968; Walls & Miller, 1970;

13

Tseng, 1970) and none have attempted to develop a prediction
model using Locus of Control.

Locus of Control refers to expectancies for control
over one's environment. In social learning theory, expec-
tancies are regarded as prime determinants of behavior.

All behaviors cannot be explained by reinforcement alone.
Human behavior is complexly determined by a conglomerate of
variables. In predicting human behavior, social learning
theory requires that expectancies, reinforcement values (or
need) and the psychological situations be considered.
Expectancies for the outcome of behaviors are learned, and
this depends upon the degree to which one perceives that
one's behavior will lead to achievement of goals, rein-
forcement through goal achievement, and the degree of
success or failure in past experiences (Phares, 1976).
Lefcort (1976) stated that:

The perception of control is a process, the exer-

cise of an expectancy regarding causation; and

the terms internal and external control depict

an individual's more common tendencies to expect

events to be contingent or non-contingent upon

their action. (p. 153)

Internally controlled individuals are those who
believe that they have some control of their reinforcement
and who can be and are motivated to change their circum—
stances. Externally controlled individuals are those who
believe that their lives are controlled by chance, fate,

and powerful others, do not believe they can control their

reinforcement and do not try to effect change because

14

of negative expectancy. The usefulness of Locus of Control
in predicting success of vocational rehabilitation clients
seems promising (MacDonald, 1971c). Hence this study will
utilize the client's Locus of Control, Internal or
External, as one of the independent predictor (personality)
variables and study how it is related to the criterion
variable of employment status.

Another personality variable, motivation to work,
often has been shown to be positively related to success in
employment. However, there have been few published reports
of research that investigated the relationship between work
motivation and rehabilitation outcome. Internal and exter-
nal attitudes refer to the degree to which an individual
perceives success as being contingent upon personal initi-
ative. The perceived payoff for initiative is the crucial
element in generating initiative to work. In one sense
this is formally rooted in the "Protestant work ethic."
Rehabilitation clients who are motivated for rehabilitation
services are more likely to be successfully rehabilitated
than unmotivated clients (Salomone, 1972).

For an adventitiously disabled person, work initi-
ative and work motivation seem to be imbedded in pre-morbid
work history and experiences. For congenitally disabled,
who may not have been able to explore the labor market
experience with normal freedom, the influence and inter-
action of the family, society, and environment determine

the effect of past experience and activity in the future

15

vocational success. The best predictor is past experience.
The old adage is "success predicts success; and failure
predicts failure." Previous work history and work experi-
ence help to build attitudes of competence, self-reliance,
and self-direction for future vocational adjustment and
success in employment.

The extent to which the client's labor market ex-
perience and his/her degree of success in jobs may predict
the probable degree of success or failure in future voca-
tional pursuits is based on: (1) the status of the job -
high level vs. low level; (2) depth of involvement -
apprenticeship, internship, part-time, or full-time job;
(3) time spent - 18 months or more. Thus, it seems that
the effect of previous work history has a multidimensional
effect in terms of work experience, complexity of the task
performed, training and experience required, wages and
other benefits received, licensing or certificate required,
and overall satisfaction in the job. Therefore, this study
will analyze the client's prior work history and will

attempt to incorporate it in the prediction model.

Summary

This study reflects an attempt to develop a pre-
diction model using demographic variables and psychological
variables--Locus of Control and work motivation--along with

previous work history, to predict success of vocational

16

rehabilitation clients. It is anticipated that psycho-
metric data combined with demographic data could possibly
increase predictive efficiency of the model. The model
will identify what variable or combination of variables
most effectively predicts desired rehabilitation outcome.
Such a development could help the counselor and the agency
to identify/diagnose the high risk probable non-rehabili-
tants early in the rehabilitation process and afford an
opportunity for the counselor and agency to plan an early
intervention which would lead to successful achievement of
the outcome criteria of rehabilitation and meet the legis-

lative mandate of accountability in higher standards.

Questions to be Addressed by the Study

 

1. Do clients with an internal locus of control
have more work motivation than clients with an external
locus of control?

2. Do clients with an internal locus of control
receive more jobs and obtain jobs earlier than clients with
an external locus of control?

3. Do clients' previous work history and experience
make a difference with respect to successful employment
after receiving vocational rehabilitation services?

4. Do clients' previous work history and experience
in related or unrelated occupations make a difference with
respect to successful employment after receiving vocational

rehabilitation services?

17

5. Are there any relationships among locus of
control, work motivation, work history, and demographic
variables?

6. Can an additive combination of predictor vari-
ables, such as locus of control, work motivation, work
history, and demographic factors be identified to predict

success or non-success of vocational rehabilitation clients?

Definition of Terms

Status 00. Referral: Entrance into the VR process by fur-

 

nishing minimun information about an individual who may
apply directly or referred to VR through various communica-
tion media.

Status 02. Applicant: The Counselor has a document signed
by the individual requesting VR Services and obtained
sufficient information for determination of eligibility or
ineligibility for VRS.

Status 06. Extended Evaluation: The Counselor has written
certificate that the severity of the disability is such
that an extended evaluation of rehabilitation is necessary
prior to making Certification of Eligibility or Ineligi-
bility for VRS.

Status 08. Closed After Referral, Applicant, or Extended
Evaluation: This status is used to identify all persons
not accepted for VR services, whether closed from referral

(00), applicant status (02), or extended evaluation (06).

18

Status 10. Individualized Written Rehabilitation Plan (IWRP)

 

Development: In this status comprehensive case study and

 

diagnosis provide a basis for the formulation of the IWRP.
The individual remains in this status until the rehabilita-
tion plan is written and approved.

Status 12. Individualized Written Rehabilitation Plan(IWRP)

 

Completed: A case is placed in this status when a plan has

 

been written and approved by the proper personnel, and
necessary arrangements are made to begin services to the
client.

Status 14. Counseling and Guidance Only: This status is
used for those individuals having an approved program which
outlines counseling, guidance, and placement as the only
services required to prepare the client for employment.
Status 16. Physical or Mental Restoration: A client is
placed in this status if he is receiving medical, surgical,
psychiatric treatment, or is being fitted with an artificial
appliance until services are completed or terminated.
Status 18. Training: The client is receiving academic,
business, vocational or personal adjustment training from
any source.

Status 20. Ready for Employment: The client completed all
preparations for employment and is ready to accept a job
but has not yet been placed or begun employment.

Status 22. In Employment: The client is in employment for

 

a minimum of 60 days before being closed employed in Status

26.

19

Status 24. Service Interrupted: Rehabilitation Services are

 

interrupted while the client is in one of the Statuses 14,
16, 18, 20 or 22. These cases are then held in this status
until the client's return to one of those statuses.

Status 26. Closed Rehabilitation: Active cases closed re-

 

habilitated must as a minimun (1) have been declared eli-
gible for services, (2) have received appropriate diagnos-
tic and related services, (3) have had a program for VR
services formulated, (4) have completed the program,

(5) have been provided counseling, and (6) have been deter-
mined to be suitably employed for a minimum of 60 days.

Status 28. Closed Other Reasons After IWRP Initiated:

 

Cases closed into this category from Statuses 14 through 24
must have met criteria (1), (2) and (3) above, and at least
one of the services provided for by the IWRP must have been
initiated, but for some reason one or more of criteria (4),
(5), and (6) above were not met.

Status 30. Closed Other Reasons Before IWRP Initiated:

 

Cases accepted for services but did not progress to the
point that rehabilitation services were actually initiated
under a rehabilitation plan (closures from Status 10 and
12).

Status 32. Post-employment: Persons previously rehabili-
tated are placed into this status while in receipt of post-
employment, follow-up or follow-along services devoted to

helping the client maintain employment.

20

Active Caseload: The number of cases in the active Statuses

 

10 through 30.

Total Caseload: The overall number of cases handled by, or

 

known to State agencies in Statuses 00 through 30.

Severely Disabled: Cases of individuals who fall into any

 

of the fOur categories listed below: 1. Clients with
major disabling conditions such as blindness and deafness,
which are automatically included, and other disabilities as
qualified, such as a respiratory disorder with sufficient
loss of breath capacity, 2. Clients who, at any time in the
VR process, had been Social Security Disability Insurance
(SSDI) beneficiaries, 3. Clients who, at any time in the VR
process, had been recipients of Supplemental Security Income
(SSI) payments by reason of blindness or disability, and 4.
Other individual cases with documented evidence of sub-
stantial loss in conducting certain specified activities.

Severely Disabled Caseload: The number of cases in the

 

active caseload classified as severely disabled.

Acceptance Rate: The number of cases accepted for VR as a

 

percent of all cases processed for eligibility. (Accept-
ances as a percent of the sum of acceptances and non-
acceptances.)

Rehabilitation Rate: The number of cases closed rehabili-

 

tated as a percent of all oases closed frOm the active
caseload. (Rehabilitations as a percent of the sum of

rehabilitations and non-rehabilitations.)

21

Overview

This chapter addressed the need for a diagnostic model for
rehabilitation clients in order for the rehabilitation
counselors and the agency to deliver appropriate services
and achieve the goal of rehabilitation. The remainder of
this work will develop further in detail as outlined in
this chapter. In Chapter II, the review of literature
begins with the state of the art in clinical vs. statisti-
cal prediction in rehabilitation and the usefulness of
predictive studies with demographic and psychological
characteristics of the clients. The construct of Locus of
Control has been studied extensively to determine its
influence on rehabilitation clients. The impact of work
motivation and work history on becoming successful in
vocational rehabilitation have been reviewed. Methodology
of the study is presented in Chapter III. Included in
Chapter III will be discussions of sample, selection
criteria, rationale, procedure, instrumentation, research
design, hypotheses in testable form, and a discussion of
the statistical models used for data analyses. The statis—
tical result of the research is the topic of Chapter IV. A
discussion of the results, limitations of the study, recom-
mendations for future research and conclusions comprise the

contents of Chapter V.

CHAPTER II

REVIEW OF THE LITERATURE

Introduction

 

One of the major functions of the vocational reha-
bilitation counselor is to assess, evaluate, and analyze
the characteristics of the client in terms of the client's
potentials and limitations. These activities provide then
the basis for developing with the client a rational inter-
vention and rehabilitation plan. In this process the coun-
selor makes decisions on the basis of intuitive judgment
and estimates the extent of the client's probability of
success. Decisions based on intuitive judgment and experi-
ence have both merits and drawbacks in comparison with
methods based on quantifiable data subjected to statistical
analysis. This review of literature first deals with the
state of the art in Clinical versus Statistical Prediction
and their role in rehabilitation.

The outcome of any social, behavioral or rehabili-
tation intervention is a function of its participants and
its internal and external social situation in which it
operates (Fairweather & Tornatzky, 1977). The outcome of

rehabilitation will be viewed as a function of the

22

23

characteristics of its participants, the rehabilitation
clients. Characteristics which will be studied extensively
include demographic variables of the client, psychological
variables, and a combination of demographic and psychologi—
cal variables. However, the outcome of rehabilitation and
its relationship with internal and external social
variables are beyond the scope of the current study.

The construct of Locus of Control, a psychological
variable and a personality characteristic which emerged
from Social Learning Theory, will be examined in detail to
determine its impact on the rehabilitation of physically
and psychologically disabled persons. An attempt will be
made to isolate the role of work motivation or client
motivation in rehabilitation from the complexity of vague
and broad general concepts of motivation. The concept of
motivation will instead be viewed from the behavioral point
of view, by considering incentives and disincentives to
employment that are operative in the client's environment.
Finally, the classification and utilization of client's
prior work history and its multidimensional impact on

rehabilitation outcome will be reviewed.

Clinical Versus Statistical Prediction

 

Prediction is a fundamental concern for the voca-
tional rehabilitation counselor, social worker, clinician,

physician, psychologist, psychiatrist, sociologist,

24

educator, economist, and industrialist. Attempts are made
to forecast future success or failure of academic or voca-
tional training programs; rehabilitation programs; the
availability of jobs in the labor market; the quality or
quantity of work to be expected from a particular group of
trained people, the handicapped, or disadvantaged; and the
likelihood of parole success or violation; etc., on the
basis of present information or knowledge about a person or
a condition. The accuracy or precision of predictions and
the ways in which predictions are made have been the center
of controversy for over half a century. In this respect
one especially important issue has been that of clinical
versus statistical prediction.

A counselor, social worker, clinician, or psycholo-
gist makes predictions on the basis of information gathered
during an interview with the client, including past history,
and psychometric test scores. One formulates a tentative
hypothesis and makes a reasonable expectation from one's
own expertise, and an intuitive judgment is made as to how
the client is going to behave in the future. This method
of prediction is called clinical or the case study method.
On the other hand, when the same client's pertinent infor-
mation is mechanically combined for the purpose of classi-
fication, and for making a probability statement which is
empirically determined from an actuarial table, frequency
distribution, or mathematical equation as to how the client

is going to behave in the future, this is called the

25

statistical or actuarial method. In a counseling situation,
if the counselor forms an early hypothesis influenced by
intuitive hunches, then one may look only for data that
support these hunches. This could be a selffulfilling pro-
phecy and introduce sources of error in counseling judgment
and prediction. The statistical method is free from this
kind of error and bias. However, if the counselor's hunches
are neutrally observed, recorded, tested, and interpreted
with scientific rigor, this may lead to the development of
sound hypotheses and theories that will have wide applica-
tion in predicting human behavior beyond the counseling or
clinical setting.

Proponents of the clinical method call it dynamic,
meaningful, holistic, deep, genuine, and sophisticated; its
opponents call it mystical, hazy, vague, subjective, crude,
intuitive, unscientific, unreliable, and verbalistic. On
the other hand, proponents of the statistical method
describe it as operational, objective, reliable, sound,
verifiable, testable, sceintific, and precise; whereas its
opponents denounce it as mechanical, atomistic, additive,
artificial, pedantic, static, and pseudo-scientific (Meehl,
1954). It seems there exist two warring camps poles apart,
and a concerned individual may tend to bend favorably
toward one camp and unfavorably away from the other camp.

The "polarizing" effect (Gough, 1962) of clinical
versus statistical methods may have originated from, or at

least have some parallelism with, the concept of the study

26

of the individual case and the study of the general.case.
Windelband (in Allport, 1937), a German philosopher pro-
posed this sharp distinction to separate the idiographic
and nomothetic disciplines; and later on Allport (1937,
1940, 1942, 1946, 1955, 1962) brilliantly expanded the
concept in the United States. Idiographic pertains to or
involves the study of cases or events as individual units,
with a view to understanding each one separately, indi-
vidually dealing with structural patterns; whereas nomothe-
tic study treats cases or events as whole, universal, and
with a view to formulating general laws. Another distinc-
tion between the two terms is that the idiographic approach
deals with individuality or intra-individual comparisons
whereas the nomothetic focuses on individual differences or
inter-individual uniformities. In other words, the former
tends to emphasize particularization and the latter is
associated with abstractions and generalization (Luchins &
Luchins, 1965).

In the study of human psychology and behavior this
dichotomy is too sharp. In the clinical situation,
diagnosis, prognosis, and therapy are idiographic.

However, these rest intimately upon a knowledge of the
common factors; for example, behavioral or pathological
disturbance is determined by the nomothetic science of
behavioral, social, environmental, and psychological
knowledge or concepts. A holistic study of the individual

is neither exclusively idiographic, nor exclusively

27

nomothetic. It occupies an "intermediate position," and
seeks an equilibrium between the two extremes (Allport,
1937).

Similarly, in the clinical and statistical methods
of prediction there is a continuum between the two ex-
tremes. When to use the clinical method or when to use a
statistical method depends solely upon what one wants to
predict, on the basis of what kind of information, data,
and situations. Whether a clinician or a statistician
would do better in making a particular kind of prediction
is an empirical question. Clinical and statistical dicho-
tomy is mainly a methodological one and it is often diffi-
cult to specify clearly where one method is overlapping
with the other. The relative efficacy of both methods is a
debatable issue as to any given prediction situation; that
is, which method is more accurate, more efficient, more
precise, and more informative in a scientific way.

Clinical versus statistical debate started when
Viteles (1925) and Freyd (1925) exchanged views on the
general issues of predicting job success. Lundberg (1926)
published a paper on case work and the statistical method;
and his contentions were:

...that the assumed opposition or incompatibility
between these two methods is illusory for three
principal reasons: (1) the case method is not

in itself a scientific method, but merely the
first step in scientific method; (2) individual
cases become of scientific significance only

when classified and summarized in such forms as
to reveal uniformities, types, and patterns of

behavior; (3) the statistical method is the best,
if not the only, scientific method of classifying

28

and summarizing large numbers of cases. The two
methods are not, therefore, under any circum-
stances opposed to each other, nor is one a sub-
stitute for the other. (p. 549 in Gough, 1962)

The clinician's side of the controversy appeared in
the field with the publication of Allport's book on
Personality in 1937 and was expanded upon by Murray (1938)
and Sarbin (1941). Allport distinguished three levels of
predicting human behavior: first, when people are viewed
en masse, and only the average behavior is of interest
(like some other men, group): second, when based upon
knowledge of human nature in general (like all other men,
universal); and third, for the study of the individual
(like no other men, idiosyncratic). Allport's (1942) mono-
graph gave a good boost in favor of the case study or idio-
graphic point of view and criticized the actuarial method.
He cited the deficiencies of the actuarial method--that it:
(1) fails to distinguish between frequency of recurrence
and the causation of the recurrence; (2) assumes the same
meaning (and hence the same causal value) for all persons;
and (3) is unable to deal with latent (unmanifested)
trends. Holt (1962) stated that following Allport (1942),
Dymond (1953) attempted to bolster and Hoffman (1960)
attacked the contention that clinical prediction must be
superior to statistical procedure as the clinician uses
idiographic methods, which alone are appropriate to
individual predictions.

The publication of the book, Clinical versus

 

Statistical Prediction by Meehl in 1954 brought the issues

29

in prediction into the limelight. He developed the formal
and empirical analysis of the prediction problem in good
foundation. He classified the distinction between the
methods of prediction and kinds of data used in making the
prediction. Given any set of data, how may it best be
treated so as to yield accurate, meaningful prediction?

He evaluated the efficacy of prediction from 20 studies
which compared the validity of the two methods. These
studies predicted three main types of criteria: (1)
success in some kind of training and schooling, (2)
recidivism on parole or probation, or (3) recovery from a
major psychosis. The results were overwhelmingly in favor
of the actuarial methods. "... in all but one ... the
predictions made actuarially were either approximately
equal or superior to those made by a clinician." Again he
stated, ”In about half of the studies, the two methods are
equal: in the other half, the clinician is definitely
inferior." (p. 119)

A few years later, Meehl (1957) added seven more
titles to his list of comparative researches and found, out
of 27 studies, 17 showed a definite superiority for the
statistical method; 10 showed the methods to be of equal
efficiency; none of them showed the clinician predicting
better. Meehl's (1965) review of available research
reaffirmed his earlier contention. Out of 50 studies
examined, about two-thirds favored statistical prediction

and the remaining one-third showed no significant

30

difference. Based on his findings, he concluded earlier

(Meehl, 1957):
I have reservations about some of these studies;
I do not believe they are optimally designed to
exhibit the clinician at his best; but I submit
that it is high time that those who are so sure
that the ”right kind of study" will exhibit the
clinician's prowess, should do this right kind of
study and back up their claim with evidence.
Furthermore, a good deal of routine clinical
prediction is going on all over the country in
which the data available, and the intensity of
clinical contact, are not materially different
from that in the published comparisons. It is
highly probable that current predictive methods
are costly to taxpayers and harmful to the wel-
fare of patients. (p. 272)

Meehl's conclusion from his review is that clinical
prediction is an expensive and inefficient substitute for
statistical predictions, and one that keeps the clinician
from being a better clinician and using his talents
constructively in clinical activity. Meehl's conclusion
has been validated by the findings of Dawes and Corrigan
(1974) and Goldberg (1970). In an attempt to improve
clinical inferences Goldberg (1970) commented that the use
of judgmental models is less costly than the use of human
judges. After a clinical expert has derived his model he
is free to perform other activities. A statistical model
of a clinician's own behavior usually will yield more valid
predictions than does the clinician himself and it will
reduce the influence of random procedural errors.

Holt (1958) expressed his concerns that clinicians

do have justified grievance against Meehl, and felt that

the evidence in favor of the statistical approach may be a

31

function of the experimental design, in which the clinician
is in a disadvantaged position. He suggested a comparative
study which would simultaneously predict the same criteria
for the same data by clinicians and statisticians who have
gone through the same preliminary steps.

In order to carry out Holt's suggestion and accept
Meehl's challenge, Grebstein (1963) studied the relative
accuracy of actuarial prediction. Three groups of judges
were categorized on the basis of their education and
experience as sophisticated, semi-sophisticated, and naive
judges in a clinical judgmental task of judging intelli-
gence quotients from Rorschach Psychograms. The following
findings are noted: there was no significant difference
between the judgments of the sophisticated group of judges
and the multiple regression equations; the accuracy of
clinical judgment was not demonstrated with increase in
clinical experience; and the average error rate was higher
among the judges than in the multiple regression equations.
One of the methodological drawbacks that might have counted
in favor of the sophisticated judges was that the judges
kept the psychograms for several days, and the judges had
been instructed that their findings would be compared with
the multiple regression equations. This might have moti-
vated the judges to take more time to predict than they
usually do to make judgments in the clinical setting.

To explore further the accuracy of the clinical

judges, Goldberg (1965) compared the diagnostic judgments

32

of 29 clinical judges and a series of linear regression

analyses in a neurotic-psychotic continuum from Minnesota

 

Multiphasic Personality Inventory profiles. Comparisons of

 

validity coefficients showed that the statistical formula
exceeded the best clinical judges and an empirically
derived linear combination of scores yielded the highest
validity coefficeint.

Educational and vocational counseling is future
oriented. Counselors do make judgments, decisions, or
predictions about their clients' probable success or
failure in different educational and vocational pursuits.
The accuracy of a counselor's decisions and predictions
contributes to effective counseling. Williamson (1939)
emphasized the clinical art of diagnosis with a view to
making a good prognosis of students' merits or abilities.
The inductive and deductive process of building a model of
the person, the decision maker from whom predictions are
made, is emphasized by Super, Crites, Hummel, Moser,
Overstreet, and Warnath (1957) and Goldman (1961).

Among other roles and functions, the counselor is
also a predictor. The question arises as to how efficient
the counselor's predictions are and whether counselors
should make predictions. Watley (1966a, 1966b, 1968a,
1968b) in a series of studies compared counselors'
predictions of educational outcome to predictions by
regression equations. The counselors varied greatly in

their skills in prediction; and their training and

33

experience made no difference. Counselors who lacked
confidence in prediction were more often correct than
counselors who typically expressed most confidence in their
judgment. This experimental evidence convincingly supports
the superiority of statistical methods.

Holt (1958) argued that no systematic study has
been done to show the forecasting skills of the clinicians
and that the clinician has not been given the same initial
validation experiences as enjoyed by the statistician or
the equation. The statistical method is first developed on
' the same kind of sample and against the same criterion that
is used in the comparative studies of predictive methods.
The clinicians, on the other hand, made their predictions
with such experience. Typically clinical judges made pre-
dictions without having had any planned validation experi-
ence with the criterion prior to the competitive studies.
McArthur (1954) and Allport (1962) suggested that the
training of the clinician may improve the predictability of
the clinician.

Watley (1968a, 1968b) conducted such a study to
test whether clinical judges could improve their predicta-
bility while he provided the group of judges (who partici-
pated in the earlier studies) with immediate feedback
training, the previous prediction results, the correlation
coefficient between the judges, and the outcome of the
criteria. No evidence was obtained to show that previous

prediction experience and the feedback information the

34

judges received aided in producing accurate judgments.
This finding does not support Holt's (1958) claim, McArthur
(1954) and Allport's (1962) assumption that the clinician
can effectively compare, integrate, and evaluate data to
produce meaningful associations useful for prediction.
Behavioral prediction requires a sound integration

and synthesis of a number of variables and their inter-
actions. The question is how it is humanly possible for a
counselor or a clinician to integrate and synthesize a
number of client variables--case data, psychometric scores,
variables of the labor market, and many other environmental
factors?

The process of weighting the fragmentary scores,

of confronting one bit of evidence with another

and finally synthesizing what one knows in order

to discover the personal pattern is exceedingly

complex. (Allport, 1937, p. 19)
Using better controlled procedures and more clinically
relevant criteria, Sawyer (1966) analyzed 45 prediction
studies which show an apparent superiority for mechanical
modes of both data collection and combination. He also
suggested that the clinician was more likely to contribute
to the field of prediction by observation than integration.

Clinical versus statistical prediction is an arti-

ficial rivalry, in that the two methods are complementary.
The statistician gets the basic information on which to
determine the relative weights in the regression equation

from the clinician. The clinical case study is superior

where satisfactory tests are unavailable. Also, the

35

clinical method is better suited than the statistical
method to processing idiosyncratic events whose frequency
is too low to allow the development of a statistical
formula of any value. In the arena of multi-dimensional
human behavior, different "rare events" occur which have
little substantial effect upon the en masse, and thus little
importance in statistical prediction. So, there remain a
vast multitude of clinical decisions for which no‘formulas
or equations have yet been developed (Anastasi,‘1982).
Meehl (1957) published an article under the title, ”When
Shall We Use Our Heads Instead of the Formula?” There is
not one answer to Meehl's question; rather one has to
shuttle back and forth on the continuum of two extremes as
the situation demands. The present state of the art in the
clinical versus the statistical approach was summarized by
Anastasi (1982):

The clinician (counselor) should utilize all the

objective test data, norms, regression equations,

and other actuarial strategies applicable to the

particular situation, while supplementing this

information with facts and inferences attainable

only through clinical methods. (p. 492) (Paren-

thesis is mine)

Upon reviewing 477 rehabilitation research reports

of 1954-1973, the importance of statistical prediction in

rehabilitation emphasized by Berkowitz, Englander, Rubin

and worrall (1975) in their book, An Evaluation of Policy

 

Related Rehabilitation Research: ”If we can forecast out-

 

come with 10% more accuracy than we are doing now, both the

36

savings in costs and the benefits to clients and society

would be substantial." (p. 125)

Prediction of Rehabilitation Outcome Using
Demographic Variables

 

 

Since the inception of the Vocational Rehabili-
tation Program, the task of the counselor and agency has
been to determine a reasonable expectation that the client
will benefit from vocational rehabilitation services and
that these services would achieve successful employment and
closure. The task is indeed complex for the counselor to
identify early in the process the client who would become a
successful rehabilitant in the rehabilitation process.
Rehabilitation research has attempted to find a means for
the counselor to predict rehabilitation outcome using
client characteristics in the biographic and demographic
domain.

DeMann (1963) used 20 biographic factors of closed
cases of rehabilitants and non-rehabilitants from the St.
Paul district office of the Minnesota Division of
Vocational Rehabilitation. The factors identified were
related to rehabilitation outcome by multivariate pre-
dictive analysis using linear discriminant functions.
Seven biographic factors found to be significantly related
to rehabilitants over non-rehabilitants were: age at
disablement, age at acceptance, type of disability, level

of education, source of referral, source of support, and

37

home ownership. More rehabilitants owned their home,
received no public or private relief as source of income,
were referred by educational facilities, were younger, had
a disability that occurred before 30, were high school
graduates and suffered from pulmonary tuberculosis.
Identification of the non-rehabilitants was possible with
65% accuracy in experimental and cross validation groups
and suggested the use of psychological or cognitive factors
for more accurate prediction.

Ehrle (1964) attempted to classify vocational
rehabilitation clients in terms of success-failure and to
construct an expectancy chart to indicate probability of
success of future rehabilitation clients based on bio-
graphical data and to devise an instrument. He randomly
selected an equal number of employed and unemployed clients
of the Missouri State Department of Vocational Rehabili-
tation. Biographic data were quantified and combined. A
cutting score was used to yield a maximum discrimination
between the employed and unemployed for both the criterion
and validation groups. The percentage of correct classifi-
cation and expectancies of vocational success were pre-
dicted better by the 20 variable key than the 86 variable
key. Results were reported that the variance between
criterion subgroups could be maximized for classification
purposes and that scores could be derived to classify
clients, and could be combined to establish expectancy

charts. The expectancy charts were useful to determine an

38

index of an individual's rehabilitation potential, to guide
in planning individual services, counseling, and to suggest
the probability of success.

The prediction studies described earlier used a
conglomerate of client variables and also used sophisti-
cated statistical procedures on small samples. Kunce (1971)
warned that this may lead to "statistical overkill" or
”statistical underkill." Statistical overkill occurs when
a sophisticated statistical procedure used on a small sample
yields highly significant but non-generalizable findings.
Conversely, statistical underkill is also possible: Wallis
and Bozarth (1971) attempted to develop and evaluate a
weighting system for outcomes using a large sample of 9,286
Illinois vocational rehabilitation clients composed of
status 26 closures and closures above status 07. Variables
were weighted and combined in two ways. System I utilized
four variables: age, education, type of disability, and
type of previous services. System II utilized a finer
discrimination of age and education. Results concluded
that this complex weighting system for rehabilitation was
not superior to an unweighted sytem.

As the complex weighting system could not predict
outcome, Kunce and Miller (1972) studied a simplified
prediction using a common sense approach based on an
earlier study by Kunce and WOrley (1970). Twelve variables
were used to determine an index score on a sample of 6,099

clients and cross validated on 3,935 clients who were

39

closed by two different Mid-western state agencies. The
index scores were found useful in describing caseload
difficulty, assessing client's strengths and limitations,
developing a rehabilitation plan, counseling, and identify-
ing barriers to employment. The variable clusters that
accounted for the three outcome criteria were the following:
(1) rehabilitation outcome - race, number of dependents,
employment at acceptance, and social security disability
status: (2) work status at closure - age, employment at
acceptance, welfare status, and social security disability
status; and (3) earnings at closure - age, education,
earnings at acceptance, and welfare status. Across the
three outcome dimensions the following findings are impor-
tant to note: public assistance, age, and earnings at
acceptance were the crucial elements that differentiated
the rehabilitants from the non-rehabilitants. These find-
ings are in agreement with the findings of DeMann (1963).
His study recommended the use of other assessment variables
(e.g., psychological tests), and the elimination of some
less effective biographical variables to obtain a more
accurate index score.

The studies reviewed showed that demographic and
biographic variables are useful in outcome prediction.
However, using biographic and demographic variables Aiduk
and Langmeyer (1972) could not predict employment success
of an emotionally disabled sample. Variables used in the

study were age, sex, education, source of referral, closure

4O

status, age at referral, and diagnostic (primary) disability
(psychosis, neurosis, alcoholism, behavior disorder, mental
retardation, and epilepsy). One of the drawbacks of the
study was the inclusion of status 08 (rejected) as one of
the three closure statuses. Of the samples under study,
94.7% of the population were closed at status 08 and most
of them were social security recipients at referral.
Omitting the social security cases and keeping the 08
status did not improve prediction. The inclusion of status
08 clients in Wallis and Bozarth (1971) may entail the same
drawback.

The Aiduk and Langmeyer (1972) study was in complete
disagreement with prior studies of an emotionally disabled
sample. The limitation of the study could be eradicated by
using personality measures, as they argued that Perlman and
Hylbert (1969) developed a prediction model using five

demographic variables and the Minnesota Multiphasic Person-

 

ality Inventory (MMPI) scales on an emotionally disabled

 

sample to identify potential dropouts in vocational train-
ing. Keep in mind that the outcome dimension was different.
One may conclude that biographic and demographic variables
are useful in predicting outcome for some sub-populations
but not for others. DeMann (1963), Ehrle (1964), Wallis

and Bozarth (1971), and Kunce and Miller (1972) used mixed
disability samples, whereas Aiduk and Langmeyer (1972) used

specific disability as the basis for their study.

41

Miller, Kunce, and Getsinger (1972) analyzed data
on hearing-impaired clients by three different methods:
clinical, statistical, and a combination of both in order
to improve predictability. A feasibility index was deter-
mined on 12 variables for each client by using a simplified
prediction method proposed by Kunce and Worley (1970).
Employability judgments made by counselors comprised the
clinical judgment measure. The feasibility index scores
were not related to employment judgments. However, employ-
ability judgments were related to all five long-term
employment outcome criteria. Improvement in predictability
occured in four categories of hearing-impaired clients when
the feasibility index scores and employability scores were
combined. Direct implications of clinical judgment
(employability) and statistical procedure (feasibility
index scores) for effective delivery of client services and
program management were emphasized. To improve predictive
power, use of personality tests to measure other behavior
dimensions and selective weighting of the most appropriate
variables were suggested.

Numerous studies were reported in the research lit-
erature on predicting the rehabilitation outcome of either
post-hospital employment or recidivism of psychiatric
patients (Fairweather, Simon, Gebhard, Weingerten, Holland,
Sanders, Stone, & Reahl, 1960; Fairweather, 1964; Lorei,
1967; Arthur, Ellsworth, & Kroeker, 1968; Anthony, Buell,

Sharratt, & Althoff, 1972). However, there was a lack in

42

identifying the factor(s) that contribute(s) to post-
hospital employment and recidivism. A deficiency in using
correlational statistics rather than testing specific
hypotheses was an additional issue. Buell and Anthony
(1973) tested specific hypotheses by multiple linear
regression analysis using 10 demographic characteristics of
78 discharged psychiatric patients. The 10 demographic
variables accounted for 53% of the variance (r=0.72,

p (.05) in post-hospital employment. When employment
history was deleted from the model, a major decrease in
variance occurred. This suggests that one variable,
employment history, accounted for the major amount of vari-
ance in post-hospital employment. Further analyses revealed
that those who became employed full-time (a non-schizo-
phrenic diagnosis) tended to be married, white, and more
skilled. Similarly, a significant portion of variance in
recidivism was accounted for by the demographic variables
attributed to one characteristic, number of previous
hospitalizations. Patients who had been hospitalized
before tended to repeat that history.

Lorei and Gurel (1973) predicted the post-hospital
employment and readmission of 957 schizophrenics from a
Veterans Administration Hospital using 20 background
demographic characteristics. It was hypothesized that
pre-admission work history, and patterns of hospitalization
be related to post-hospital work and re-admission within a

nine month follow-up period. Correlational analysis of a

43

20 predictor set yielded a multiple correlation of 0.52
with weeks of work (p <.01) and 0.23 (p (.01) with re-
admission. Use cf regression statistics indicated that
work experience five years prior to admission to hospital
was the most useful predictor of post-hospital employment
and re-admission. This finding is in agreement with the
findings of Buell and Anthony (1973).

Anthony and Buell (1974) replicated their previous
study on 79 psychiatric patients released from the same
hospital the following year. The original study, the
replication, and the study by Lorei and Gurel (1973)
consistently found the most useful single predictor of
post-hospital employment to be employment history. With
regard to recidivism, the results of the three studies
indicate that number of previous hospitalizations is the
single best predictor of recidivism at 6 months (Buell &
Anthony, 1973), 9 months (Lorei & Gurel, 1973) and 12
months (Buell & Anthony, 1974). However, multiple linear
regression analysis comparison of recidivism at 6, 9, and
12 months yielded a small amount of recidivism outcome
variance by the number of previous hospitalizations. So,
predicting recidivism from the number of previous hospital-
izations and other demographic data should be done with
great care.

Prediction of likelihood of success of rehabili-
tation clients using statistical techniques is too sophis-

ticated for counselors to use. Worral and Vandergoot

44

(1980, 1982) developed a table of positive and negative
regression weights representing various client character-
istics on 18 categories of disability types, four categories
of age grouping, three levels of education, and one level
each of sex, race, marital status, number of dependents,
public assistance, and work history. Regression weights
were estimates of the incremental contribution to the
probability of success. During the intake interview, the
counselor can add and subtract the regression weights in a
worksheet from the table as per the client characteristics
and arrive at an estimate of probability of successfully
achieving the outcome criteria by the client.

Wbrral and Vandergoot (1980) used 6,263 closed
cases of Oregon Vocational Rehabilitation clients during
the fiscal year 1978. The model used a multivariate design
to assign weights based on actual program data in line with
Miller and Barillas (1967), Lawlis and Bozarth (1971), and
Bolton (1972); but not in an arbitrary fashion suggested by
Kunce, Cope, Miller, and Lesowitz (1973), and Weikel and
Johnson (1974). A linear regression or a linear probability
model was used to forecast outcome criteria. Outcome was
correctly predicted for 63% of the cases, which is close to
the observed probability of success in the program (66%).
Also, the model predicted that 42% were likely to be closed
unsuccessfully, which is an increase in prediction over the
observed probability of 33%. It suggests the model can

predict the unsuccessful cases better than chance.

45

Prediction models have been often criticized due to
lack of cross validation over time in order to demonstrate
the consistency of the model and to determine improved
prediction over the base rate of the outcome criteria
(Berkowitz, Englander, Rubin, & Worral, 1975; Bolton, 1972;
Cook, 1980). Worral and Vandergoot in their original study
met the above requirement. They wanted to test the cross
validation over time (Wbrral & Vandergoot, 1982); They
have replicated the previous study on 6,224 subjects closed
during 1979 in the same agency and added three more pre-
dictor variables to the model, as the literature suggested:
referral source (Bolton, 1972; Bonge, 1975), time in certain
statuses (Cooper & Greenwood, 1975; Goldberg & Johnson,
1980), and training and other service provision factors
(Flynn, 1975; Kunce, Miller & Cope, 1974; Growick &
Stueland, 1979).

In the replicated study, the age range 26-34,
marital status, work history, and the disability type--
psychosis and drug abuse--were significant using a t value
of 1.96 at the .05 level. The cross validation results on
successful closure were 65% in 1979 compared to 63% in 1978;
and on unsuccessful closure 45% in 1979 compared to 42% in
1978. Adding further variables to the model did not improve
the predictive usefulness; however, regression analysis
indicated the following: referral from a hospital tended
to be unsuccessful; three of the four training variables

related to success were significant, with the strongest

46

being on-the-job training. Predictive power of the model
appears to be stable over a two-year period. The authors
suggested that psychological variables should be included
and that the model should be tested in another state in
order to improve the predictive validity. Hence, this
study will add the Locus of Control construct as a psycho-
logical variable to test the predictive ability of the
model in the states of Louisiana, Michigan and New York.
In summary, the predictive studies in rehabilitation
using demographic variables are useful. The variables
found significant in prediction were age, education, sources
of referral, sources of support at referral, marital status,
and employment history. Most of the studies used samples
composed of all disabililties (DeMann, 1963; Ehrle, 1964;
Wallis & Bozarth, 1971; Kunce & Miller, 1972). Goss (1968)
and Bolton (1972) suggested to partition the mixed disabil-
ity to categorize the disability type to improve the pre-
dictive accuracy of each sub-group sample. Accordingly,
sub-group samples were utilized in various studies:
emotionally disabled (Aiduk & Langmeyer, 1972); hearing-
impaired (Miller, Kunce, & Getsinger, 1972); psychiatric
(Buell & Anthony, 1973, 1974); schizophrenic (Lorei &
Gurel, 1973); finally Worral and Vandergoot (1980, 1982)
analyzed data using 18 categories of disabled samples.
Wallis and Bozarth (1971) failed to predict by
using a complex weighting system.‘ Similarly, Aiduk and

Langmeyer (1972) could not predict the outcome of an

47

emotionally disabled group. The significance of work
history on future vocational aspiration was equivocal as
confirmed by many studies (DeMann, 1963; Kunce & Miller,-
1972; Buell & Anthony, 1973; Anthony & Buell, 1974; Lorei &
Gurel, 1973; Worral & Vandergoot, 1982). Buell and Anthony
(1973) first used multiple regression analysis over
simplified correlational analysis to show the evidence of
unique variance contributed by each demographic variable.
Most of the studies recommended use of psychometric
measures of client behavior and stability in areas of
personality and adjustment as independent predictor
variables which might increase predictive ability in the

outcome domain.

Prediction of Rehabilitation Outcome Using
Psychological Variables

 

Predictive studies using demographic variables
recommended the use of psychological variables to increase
predictive efficacy. A linear combination of demographic
and psychological variables may have some incremental
effect in the outcome domain. However, the literature
review revealed that some studies depended only on the
psychological variable to predict outcome.

Drasgrow and Dreher (1965) used a psychometric
measure, the Minnesota Multiphasic Personality Inventory
(MMPI), to differentiate between successful and unsuccess-

ful emotionally disabled rehabilitation clients. They

48

found that the unsuccessful group had five times as many
"sick" as "passable" profiles and the successful group has
three times as many ”passable" as "sick" profiles.

Lowe (1967) wanted to predict the ability of
psychiatric patients to become gainfully employed after
hospitalization using the three most frequently used

psychological tests, the Wechsler Adult Intelligence Scale

 

(WAIS), the Minnesota Multiphasic Personality Inventory

(MMPI), and Rorschach Ink Blot Test. The three tests

 

measured three broad areas: ability, objective personality,
and subjective personality. No significant relationships
between psychometric scores and employment outcome were
found. He explained that the general psychological tests
of ability and degree of adjustment are important to voca-
tional adjustment, but the tests were not good predictors
of these psychological attributes. Whether or not one
secures employment upon leaving the hospital or mental
institution is determined by psychological differences in
motivation, temperament, degree of social responsibility,
and social maturity is yet to be seen.

The importance of diagnostic categories in evalu-
ating psychological data was emphasized by Goss (1968).
Subjects of the study were 98 male neuro-psychiatric
patients of a Veterans Administration Hospital accepted for
a vocational rehabilitation program. Data were compiled

from the Work Values Inventory (WVI) and comparisons were

 

49

made between success versus failure in obtaining employment
after discharge from the hospital. Predictive accuracy in
outcome criteria for psychiatric patients was found to vary
with each diagnostic category: alcoholics, anxiety-
depression, and schizophrenic. In each category prediction
was higher than the combined population prediction rate.

Goss (1969) followed up his earlier hypothesis and
investigated the utility of a personality inventory, the
Edwards Personal Preference Schedule (EPPS) need scores in
differentiating the 58 neuro-psychiatric patients with
vocational rehabilitation outcome criteria. The analysis
was performed on the total population in success-failure
criteria and for the following diagnostic sub-groups:
(a) alcoholics (ALC), (b) anxiety-depression (A-D),
(c) patients with physical disability (P-D), and (d) schizo-
phrenics (SCZ). The model predicted above the base rate of
success for all populations and the predictive accuracy
increased when the results were specifically related to
particular sub-group populations. It seems that Goss's
sub-grouping made each group of subjects more homogeneous.
The prediction gain was possible due to the relationship of
fairly homogeneous groups and the gain in behavior-specific
criterion information.

The limitation and usefulness of the EPPS in diag-
nosing or differentiating alcoholics from non-alcoholics in
the outcome domain was noted by Pryer and Distefano (1970).

Forty-five male alcoholics differed from 45 male

50

non-alcoholics (psychiatric) vocational rehabilitation
clients on only two of the EPPS variables, affiliation and
succorance. The EPPS need scores failed to discriminate
between the alcoholics who were successful in a vocational
rehabilitation program and those who were not.

Promise of the predictive model espoused by Goss
(1969) based on diagnostic grouping and a specially weighted
scoring technique with the EPPS was followed by Distefano
and Pryer (1970). They tested 47 male and 64 female psychi-
atric patients upon admission to a vocational rehabilita-
tion program at Central Louisiana State Hospital. Male-
female subjects were sub-grouped as schizophrenic, neurotic,
personality disorder, organic brain syndrome, and others.
Follow-up of these clients on subsequent success and failure
criteria between the outcome groups in either the male or
female sample was not statistically significant between any
of the EPPS scales and vocational success. Evaluation of
Goss's model using sub-group populations with the EPPS did
not improve predictive accuracy in forecasting the
vocational outcome.

Prediction of job success following heart attack
using the MMPI was reported by Gresset (1969). A signifi-
cant but negative correlation was found between the
Hypochondriacal scale and employment status four months
after heart attack, among a group of 40 male cardiac

patients.

51

Flynn and Salomone (1977) studied the ability of
the MMPI to predict rehabilitation outcome among multidis-
abled clients. Of these 128 successful and 128 unsuccessful
who were closed for more than one year were assigned random-
ly to each sample, and cross-validation was performed. The
MMPI data were obtained from client's case files. Results
provided no strong support for the routine use of the MMPI
in forecasting rehabilitation outcome with multidisabled
clients. It was suggested that the MMPI's contribution to
predictive accuracy, even if it is small, may be combined
with the other independent predictors.

Results of the predictive studies using only
psychometric measures are mixed. Lowe (1967) using three
psychometric measures failed to predict the outcome of
psychiatric patients. Goss's (1969) idea of partitioning
the population and analyzing the data for each sub-group to
reveal the degree of relationship with the outcome criteria
is an important one. Analysis of total sample may obscure
the unique difference or characteristics of the sub-group.
Analysis on sub-group population was emphasized by Bolton
(1972). The usefulness of the EPPS in diagnosing or
differentiating the alcoholics from the non-alcoholics in
terms of predicting outcome was questioned by Pryer and
Distefano (1970) and Distefano and Pryer (1970). Using the
MMPI, Lowe (1967) and Flynn and Salomone (1977) found no
strong support for the routine use of the MMPI or any other

psychometric measures. These studies suggest that psycho-

metric measures along with client characteristics on bio-
graphic and demographic variables may improve predictive

ability. A review in that direction follows.

Prediction of Rehabilitation Outcome Using
A Linear Combination of Demographic
and Psychological Variables

 

 

 

In reviewing prediction studies using demographic
characteristics in predicting outcome criteria, it was
suggested that the degree of predictability may improve
with the inclusion of some psychological measures.
Psychological measures can delineate the client's state of
psychological adjustment. The personal adjustment of the
disabled individual is to a large extent a function of
premorbid personality and integration with the new condi-
tion. Intrapersonal and interpersonal adjustment along
with acceptance of disability are the crucial elements of
success in vocational pursuits.

Ayer, Thoreson and Butler (1966) combined 14 MMPI
scales with 10 demographic variables and used multiple
regression analysis to predict success of 79 clients from a
division of Wisconsin Vocational Rehabilitation. The study
predicted three outcome measures: occupational level,
upward mobility, and closure status. The multiple correla-
tion coefficient for each of the three regression equations
approached 0.67 at the .05 level of significance. The age

at onset, age at time of application, type of disability,

53

and education were significant for one or more of the
criteria. Results indicated positive beta weights for age
at time of application, intelligence quotient scores, and
the masculinity-feminity (Mf), psychopathic-deviate (Pd),
and correction (K) scales of the MMPI. Marital adjustment
was found to be an indication of the level of personal
adequacy, dependability, interpersonal relationship ade-
quacy, willingness to assume responsibility, and motivation
to occupational advancement. Prediction of occupational
level suggested that physical disability tended to be from
a higher occupational level than emotional disability.
Higher occupational level jobs demand a higher degree of
individual adjustment and interpersonal relationship
adequacy. This corroborates the findings of Aiduk and
Langmeyer (1972) as they could not predict the success of
emotionally disabled populations. It seems likely that
emotional disability imposes a certain adjustment problem
in achieving outcome criteria. The review of the charac-
teristics of two client categories, psychiatric and schizo-
phrenic, will follow.

In prediction studies, a conglomerate of client
background variables are used. Heilbrun and Jordan (1968)
concluded that research "may ultimately have to consider a
substantial number of factors concomitantly before a real
breakthrough in the prediction of rehabilitation outcomes
can be effected." (p. 216) Perkins and Miller (1969) showed

a sound possibility of using multiple variables in predic-

54

tion. Multivariable studies using sophisticated statistical
procedures but inadequate measures may show no relationships
that are nominal, but consistent and pervasive (Kunce &
Worley, 1970). Gunderson and Arthur (1969) showed that
predictions based on simple counting diagnostic yes or no
answers on number of questions are as effective as complex
weighting system.

Using variables reported in the literature as being
related to outcomes, Kunce and WOrley (1970) used a
simplified prediction technique as proposed by Gunderson
and Arthur (1969) to predict occupational adjustment of
psychiatric patients. Twelve variables representing
personal history, maladjustment, intellectual functioning,
and personality were selected from the client data obtained

from the MMPI, the Circular Pencil Mazes and the WAIS.

 

Twelve questions were formulated from the variables and an
affirmative answer reflected the client's personal strength
on that variable. The employment outcome criteria were
positively related to education (phi=0.22), verbal I.Q.
(phi=0.25), performance I.Q. (phi=0.19) and mazes
(phi=0.20). Isolating single predictor variables showed
nominal relationships; however, a simple cumulative index
was positively related to employability. The applicability
of the simplified procedure and the validity of the
cumulative index procedure were tested on three different

groups: 118 disabled adults, 344 culturally disadvantaged,

55

and 590 workshop clients from three different parts of the
country; and comparable results were obtained.

The relationship of certain demographic variables
and personality variables to success in vocational rehabili-
tation was studied on an orthopaedic sample by Gilbert and
Lester (1970). They attempted to develop a personality-
based technique to assess rehabilitation potential of the
client. Two scales of the MMPI, Hypochondriasis (H3) and
Depression (D), were significantly related to the outcome.
Scores on the two scales indicated better adjustment for
the rehabilitation group. The degree of adjustment, both
intra—personal and inter-personal, to their own disability
perhaps may be the factor that reflects a level of
motivation on the part of the client which ultimately leads
to vocational success. Literature review substantiates
studies on client motivation (Barry & Malinovsky, 1965;
Rothney, 1970; Lane & Barry, 1970), but none studied the
interface relationship between client motivation for
rehabilitation services and rehabilitation outcome.

Salomone (1972) investigated client motivation as
determined by the counselors along with demographic vari-
ables and personality characteristics measured by the MMPI.
Clients (66 male and 52 female) had been closed by two
agencies, the Minneapolis Rehabilitation Center (MRC) and
the Division of Vocational Rehabilitation (DVR). Of these,
97 were closed by the latter as either employed or unem-

ployed. The sample consisted of 82% physical disabilities

56

and 18% diagnosed as having emotional or mental problems.
The relationship between client motivation for rehabilita-
tion services and subsequent outcome was significant only
with the DVR outcome criteria. The other part of the study
assessed the motivated and unmotivated group on the MMPI
profiles by a multiple discriminant analysis which yielded
a generalized Mahalanobis D2 statistic to test the signifi-
cance of the difference between the two group profiles.
The shapes of the profiles for both groups were similar,
but the distance between scale means was not significant.
Serious limitations of the study are apparent, e.g.,
sampling bias, unreliable measure of motivation by the
counselors, and difference in case closure criteria by the
two agencies. However, there is evidence to support that
clients who are motivated for rehabilitation services are
more likely to become rehabilitated than unmotivated
clients.

In summary, the psychometric measure of personality
used as a psychological variable was reviewed in 15
prediction studies. Twelve studies used the MMPI and 3
used the EPPS. The limitations of the EPPS in predicting
outcome domain were noted earlier. Use of the MMPI was
found useful in 9 studies, while findings of 3 studies
(Lowe, 1967; Salomone, 1972; Flynn & Salomone, 1977) could
not support the significance of using the MMPI. Salomone
(1972) suggested looking closely at the client motivation

dimension besides personality. Clients who are motivated

57

are likely to become more successful than unmotivated
clients in the labor market. Flynn and Salomone (1977)
advocated that "we believe that the most promising research
leads may be found from environmental contingencies that
support and maintain successful vocational adaptation."

(p. 14) The concept of the Locus of Control construct
derived from social learning theory was advanced by Rotter
(1954, 1966). Internal and external control attitudes of
an individual are attributed to the nature of reinforcement,
and perceived payoff is based on past experience with the
environmental contingencies. Some light on vocational
adaptation may be shed from the study of relationship of
Locus of Control (MacDonald & Hall, 1969; Tseng, 1970;
MacDonald, 1971c; MacDonald & Hall, 1971; and WOrral &
Vandergoot, 1982) and motivation to work (Benson &
Whittington, 1974; Andrisani, 1977; Bolton, 1980 and Worral

& Vandergoot, 1982).

Locus of Control

 

The concept of Locus of Control derives from
Rotter's social learning theory (Rotter 1954, 1955, 1960).
The Locus of Control construct first came into prominence
with the publication of a monograph by Rotter in 1966.
Since then Locus of Control has generated a substantial
amount of research supporting its validity as a psycholog-

ical construct. There are now five major reviews (Rotter,

58

1966; Lefcourt, 1966a, 1966b; Minton, 1967; Joe, 1971).
There are 13 measures of Locus of Control and five biblio-
graphies. Throop and MacDonald (1971) listed 339 published
and unpublished works through 1969; MacDonald (1972) and
MacDonald and Davis (1974) reported 135 and 273 references
in 1970 and 1971 respectively; Thronhill, Thronhill, and
Youngman (1975) computerized 1200 published and unpublished
references through 1972; and Prociuk and Lussier (1975)
reported 277 studies published in 1973 and 1974, of which
69% of the empirical studies used the Rotter Internal-
External scale. By now, the number of studies using the
Locus of Control construct may exceed 3,000.

Locus of Control (LOC) is defined as the degree to
which a person perceives desired reinforcement as contingent
upon his own behavior. Internal control refers to the per-
ception that positive or negative reinforcement following
some action or event is contingent upon one‘s own behavior
or one's relatively permanent characteristic. On the other
hand, external control refers to the perception that posi—
tive or negative reinforcement followed by some action or
event is not entirely contingent upon one's own action, but
is the result of luck, chance, fate; or reinforcement may
be perceived as under the control of powerful others and
unpredictable because of the complexity of forces surround-
ing the individual.

Rotter (1966) explained,

59

The effect of a reinforcement following some be-

havior ... is not a simple stamping-in process

but depends upon whether or not the person per-_

ceives a causal relationship between his own

behavior and the reward. (p. 1)
When the reinforcement is seen as contingent upon one's own
behavior, that occurrence will increase an expectancy for
the future in order to receive anticipated reinforcement
and this would be internally controlled. On the contrary,
when the reinforcement is seen as not contingent upon one's
own behavior then the expectancy of reward will not
increase, and motivation in this case would be externally
controlled. Therefore, one's expectations or beliefs that
reinforcement is dependent upon one's behavior will depend
upon an individual's previous reinforcement history
(Rotter, 1966). .

Locus of Control, a personality characteristic,
measures individual differences of generalized expectancy
across situations. The construct has been used in invest-
igating learning, creative thinking, achievement, aspira-
tions, adjustment in life, satisfaction in work and such
demographic variables as sex, socio-economic level, ethnic
identification, minority and disadvantaged status, diagnosis
and treatment of psychopathology.

In the area of achievement behavior, Coleman,
Campbell, Hobson, MacPortland, Mood, Weinfeld, and York
(1966) reported evidence indicating a relationship between

internal control and achievement amongst a variety of

minority groups. This finding was supported by McGhee and

60

Crandall (1968), Lessing (1969), and Hunt and Hardt (1969),
all of whom reported a positive association between
internality and achievement behavior. Hjelle (1970) could
not support evidence indicating LOC as a determinant of
academic achievement.

LOC was used to investigate ethnic and socio-
economic class difference. Rotter and Battle (1963) and
Shaw and Uhl (1969) demonstrated that children from lower
socio-economic levels have higher external orientation than
children from higher social class levels. Feather (1968)
found females score significantly higher in external con—
trol. Coleman et a1. (1966) and Lefcourt and Ladwig
(1965a, 1965b) found that blacks and other minority members
were more externally oriented than whites. This was sup-
ported by Scott and Phelan (1969) as the unemployed blacks
and Mexican-Americans were more external than unemployed
whites. Indians were the most external in attitude.

In the area of cognitive activity and LOC, internals,
seek more information relevant to their personal conditions
than externals. Seeman and Evans (1962) reported that among
tuberculosis patients, internals sought more information
about the disease and concomitant effects than externals.
Similarly, among reformatory inmates, internals exhibited
greater learning about the attainment of parole than had
externals (Seeman, 1963). Phares (1968) study compared
internals and externals in their tendencies to use informa-

tion for decision making. He concluded that internals are

61

more likely to make use of information, that externals are
equally aware of, and that, therefore, internals should
have a greater potential for effectiveness in their social
environment. A study focusing upon attentiveness,
Lefcourt, Lewis, and Silverman (1968) reported that
internals spend more time deliberating about the decisions
in skill-demanding tasks than chance-demanding tasks, while
externals exhibited the contrary.

Studies reporting the relationship between LOC and
adjustment have shown that externals score higher on anxiety
measures than internals (Platt & Eisenman, 1968; Hountras &
Scharf, 1970) and externals tend to be more debilitated by
anxiety (Finch & Nelson, 1974; Shriberg, 1974). Using the
level of aspiration paradigm, the relationship of I-E to
the coping of success and failure, Efran (1964) found that
internals tended to repress failures more than externals.
Internals blame themselves for their failures and try to do
something about it more than externals who are not likely

to blame themselves for their failures.

Locus of Control and Physically Disabled

Optimally, a rehabilitation client will take full
advantage of the numerous rehabilitation services and
utilize them to his/her best interest. In practice, the
client's degree of adjustment to the disability will
determine the effective utilization of rehabilitation

services in achieving the rehabilitation goal.

62

MacDonald and Hall (1969, 1971) investigated the
perception of disability by the non-disabled. Non-disabled
externals felt physical disabilities to be more debilitating
than internals. But in real-life situations it was dia-
metrically opposite. Using a perceptual defense paradigm
to measure denial of disability, Lipp, Kolstoe, James, and
Randall (1968) compared a group of disabled and non-disabled
in the perception of disability. Physically disabled
subjects demonstrated more extreme differences in perceiving
slides of disabled people than did normal subjects. Physi-
cally disabled externals had significantly lower recogni-
tion thresholds when presented with slides of other disabled
persons than more internal disabled subjects. The disabled
externals were less denying of disability than disabled
internals. These findings are in agreement with Efran
(1964) that disability is unacceptable to the disabled and
defended by the use of denial by internals. This study
offers a direction for vocational rehabilitation clients;
the less one denies one's disability, the more successful
the adjustment to the disability and in turn, the more
successful will be the rehabilitation outcome. Moderately
denying disabled persons are more rehabilitated than either
the low-denier or high-denier. Successful rehabilitation
is contingent upon acceptance of and adjuStment to the
disability.

Wedland (1973) tested a sample of musculoskeletally

impaired persons and found that individuals who had been

63

disabled less than one and a half years were significantly
more external than subjects disabled for three years or
longer. Individuals with recent onset of disability would
have significantly higher external scores than those with
long-term disability. Walls and Miller (1970) compared
perception of the disability by rehabilitation and welfare
clients. Rehabilitation subjects tended to perceive dis-
abling effects as more seriously debilitating than welfare
subjects. Perhaps the disabilities or rehabilitation sub-
jects were either emotional or internal in nature. Older
clients perceive some disabilities as more debilitating
than younger clients. The study recommended that rehabili-
tation counselors and social welfare workers should take
into account the client's LOC and perception of disabling
circumstances in order to formulate appropriate plans for
client services.

Houston (1972) investigated the differential
influence of internal—external beliefs in reaction to
stress. Glass and Singer (1972) emphasized the implication
of I-E beliefs to a number of psychological dimensions on
"perceived control." Glass (1977) studied the relevance of
I-E beliefs on Type A and Type B behavior pattern. Type A
individuals appeared to be internal but struggled and tried
to develop coping strategies for uncontrollable stress.
Those who continually attempt to master stress are prone to
coronary heart disease. Cromwell, Butterfield, Brayfield,

and Curry (1977) conducted a well controlled study of

personality characteristics in relation to stress, heart
attack and recovery. Coronary patients were more external
than the medical controls; internality was associated with
high anxiety and externality with undesirable physical
symptoms. Internals were more cooperative in response to
treatment demands, and left the hospital earlier than
externals. The findings of Glass (1977) and Cromwell et
al. (1977) suggested that I-E beliefs may have implications
of physiological adaptive response to stress within the
cardiovascular system (Strickland, 1978). Ledom (1978)
compared 20 subjects with significant coronary atheroscle-
rosis and 20 medical control subjects and found no signifi-
cant relationship between LOC and coronary prone behavior
scores. The mean LOC values were internal direction for
both groups and the groups did not differ in LOC values.
LOC orientation of a hemophilic group were studied
by Bruhn, Hampton, and Chandler (1971), who found that the
hemophilic group was more internal than normal controls.
However, within the hemophilic group, a borderline severe
hemophilic was significantly more external than either a
mild or severe hemophilic. This study suggested that the
borderline severe hemophilics tended to view their condi-
tions as unpredictable, hence externally oriented.
Goldstein (1976) compared a group of longéterm hemodialysis
patients with a group of non-hemodialysis patients. The
hemodialysis samples had higher denial and external scores

than the non-hemodialysis control sample. Poll and Kaplan

65

De-Nour (1980) investigated the relationship of LOC and
adjustment of a group of chronic hemodialysis patients in
Israel. Negative correlations of high significance were
obtained in LOC and compliance with the diet and acceptance
of disability. Internal patients adjusted and adapted
better than external patients. No correlation was found
between LOC and number of years on dialysis.

The foregoing reviewed studies attempted to inves-
tigate the relationship between LOC and the reaction to a
specific physical disorders or physical disabilities in
general. However, the severity of the disability, age at
onset, economic and social status of the disabled person,
and a host of other psychological and environmental vari-
ables all interact complexly on the disabled person.
Although physically disabled internal adults initially
respond to disability with anxiety, denial, and concern,
they tend to know more about their difficulties and exert
an influence to adjust and cope with the problem more than
the externals. Phares, Ritchie, and Davis (1968) commented
that even though the behaviors of the internals are dis-
ruptive, internals more often than externals take steps to
work on the problem posed by the negative personality
interpretation and in the long run internals are better off
in coping with their problems and achieving their rehabili-
tation goals. Lack of disturbance in behavior and less
anxiety on the part of the externals indicate a feeling of

being unconcerned or passive towards their disability which

in the long run may lead to more severe disability and

adjustment problems.

Locus of Control and Psycholggical Disorders

In the previous section, the relationship between
LOC and physical disability was established. The population
of clients served by the State-Federal Vocational Rehabili-
tation Program comprises physically disabled as well as
psychologically disordered, including mentally ill, mentally
retarded, emotionally impaired, psychiatric, neurotic, per-
sonality disorder, alcoholic, and drug addict. In this'
section, the relationship between LOC and psychological or
pathological disorder along with its implications for
successful rehabilitation will be reviewed.

Inferring from the past review it would seem logical
that psychologically disturbed individuals will have greater
external orientation. While this is the fact for some
psychiatric populations, it is a paradox for alcoholics and
drug addicts. Studies investigating maladaptive or dys-
functional populations show that there is a relationship
between externality and psychiatric patients (Cromwell,
Rosenthal, Shakow, & Zahn, 1961; Shybut, 1968; Harrow &
Ferrante, 1969; Smith, Pryer, & Distefano, 1971; Palmer,
1971; Duke & Mullins, 1973; Lefcourt, 1976). Lottman and
DeWolfe (1972) found that process schizophrenics were more
external than reactive schizophrenics. The fact that

process schizophrenics were more external may be due to

67

their poor premorbid adjustment patterns and a prior
history of failure and social inadequacy. In the process-
reactive dimension, Fontana, Klein, Lewis, and Levine
(1968) reported that schizophrenics who wished to impress
others were "healthy" and more internal than those who
wished to prove they were "sick.”

The LOC construct accounted for a large percentage
of the more extreme behaviors manifested in severe forms of
mental illness. The relationship between depression and
externality was supported by much empirical research
(Abramowitz, 1969; Goss & Morosko, 1970; Lefcourt, 1976).
External expectancies were more strongly related to a
measure of chronic depression than to temporary depressed
mood (Strickland & Hale, 1975). Williams and Nickels
(1969) indicated that externality was directly related to
suicidal behavior. Lambley and Silbowitz (1973) could not
predict the contemplation of suicide behavior by the Rotter
I-E scale.

So far, the review of the empirical research reports
a relationship between beliefs of external control and
severe psychological disorders; however, these findings are
contradictory for alcoholics and drug addicts. Surpris-
ingly, alcoholics score significantly more internally than
Rotter's (1966) general norms reported by Goss and Morosko
(1970). Oziel, Obitz, and Keyson (1972) replicated the
Goss and Morosko (1970) study and measured the generalized

perceived LOC and transformed specific LOC with regard to

68

drinking behavior. Results supported the earlier study and
found that alcoholics as a group perceive themselves as
being in control of their behavior in general and drinking
behavior in particular. Gozali and Sloan (1971) disputed
the findings of Goss and Morosko, who failed to find a
significant relationship between I-E scores and MMPI
subscales among alcoholics. O'Leary, Donovan, and Hague
(1974) replicated the Goss and Morosko as well as Gozali
and Sloan to clarify the ambiguity. Results could not
support Gozali and Sloan (1971) but validated the findings
of Goss and Morsoko (1970).

One may expect that drug addicts would be more
external. However, they were similar to alcoholics.
Berzins and Ross (1973); Calicchia (1974); Smithyman,
Plant, and Southern (1974) found that drug addicts are more
internal. It seems that the behavior of the alcoholics and
drug addicts is paradoxical in nature. Goss and Morosko
(1970) explained that alcoholics are aware of their control
expectancies and they use alcohol as a means of altering
feeling states. Another explanation forwarded by Phares
(1976) is that alcoholics have a history of being attracted
to group meetings and organizations, being quite glib and
articulate, which reinforce the arousal of confidence,
personal control and optimism.

Fontana et al. (1968) reported schizophrenics who
wished to impress others with their good adjustment tended

to score more internally than those who wished to create a

69

bad impression. Internal control response on the I-E scale
by alcoholics and addicts may resemble the same behavior
pattern. It seems both alcoholics and addicts have an
earnest desire to impress others. Berzins and Ross (1973)
suggested:
...that internal control can additionally be
conceptualized as a consequence or by-product
of substance abuse. Perhaps a term, such as,
”pseudo-internality” should be used to distin-

guish drug-engendered internality from its
conventional, socially learned counterpart.

(Po 90)

Changes in Locus of Control

LOC measures a generalized expectancy regarded as
influencing a wide range of human behaviors. Lefcourt
(1976) pointed out that an individual's LOC is inferred
from momentary expression of one's sense of causality and
it may be relatively stable/consistent at different points
of time. However, the expressions of causal expectation
are referrants of the LOC construct but not the construct
itself. The I-E scale is an instrument in social learning
theory to elicit expressions of belief which are indicative
of causality. As the LOC influences a wide range of behav-
ior, it is indeed necesary to understand the condition(s)
that affect the changes of internal and external orienta-
tion: under what circumstances and to what extent. Changes
in control expectancies could be achieved through natural,
accidental, or deliberately contrived events (Lefcourt,

1976; Phares, 1976).

70

The natural source of change in causality is asso-
ciated with advancing age. Wolk and Kurtz (1975) reported
a high positive relationship between internal control and
adjustment to aging. Palmore and Luikart (1972) found that
LOC is a determinant of life satisfaction. Internal and
external beliefs can be influenced by elements in an indi-
vidual's life which relate to variations in uncertainty,
unpredictability, and lack of control (Kiehlbauch, 1968;
MacArthur, 1970; Eisenmann, 1972). Kiehlbauch (1968) found
that reformatory inmates were more highly external upon
admission, and shortly before release, than during the
intermediate period of their stay. This represents a
curvilinear relationship with the length of stay in the
reformatory between the I—E scores and anxiety scores.

The initial period of helplessness with the new environment,
and the anxiety and uncertainty of coping with the outside
world immediately before their release made them more ex-
ternal. However, the intermediate period represents a
coping with the environment, abiding by the rules and regu-
lations of the prison, and a sense of belonging with other
prison inmates made them comfortable, hence internally
focused.

The goal of counseling and psychotherapy is to help
the client learn to effect a degree of control over one's
own life events and contingencies. A vast number of
research studies across a number of treatment modalities

where LOC has been used as a criterion outcome measure

71

indicate that individuals do become more internal as a
result of counseling or therapy (Smith, 1970; Gillis &
Jessor, 1970; Dua, 1970; Kilman & Howell, 1974; Eitzen,
1974; Lewis, Dawes, & Cheney, 1974; Schallow, 1975). Smith
(1970) found that LOC scores declined from the external to
internal significantly after five weeks of crisis inter-
vention therapy, as patients adopted more effective coping
techniques by restoring a sense of self-control. Similarly,
Dua (1970) reported an improvement of interpersonal skills
with the increase of internality from the pretreatment LOC
score in an action-oriented therapy.

Masters (1970) presented a case report where the
strategy was the reconstruction of causality by altering
the patient's perception of control. He suggested action
for exerting control over the patient's source of conflict.
It seems that a sense of personal control can be an integral
component of counseling or therapy. Parks, Becker,
Chamberlain, and Crandall (1975) eliminated self-defeating
behavior and change in LOC. Majumder, Greever, Holt, and
Friedland (1973) examined the use and expansion of an I-E
counseling technique on a group of disadvantaged youth and
showed a significant increase in internality after six
weeks compared to youths who were not counseled.

The effect of treatment experiences upon LOC of
alcoholics was examined by Oziel and Obitz (1975). The
clients entering a detoxification program were classified

in three groups according to their degree of prior

experience in treatment. Increased internality tended to
be associated with larger amounts of prior exposure to
treatment for alcohol abuse. O‘Leary, Donovan, Hague, and
Shea (1975) emphasized adaptive function in a six-week
treatment program for alcoholics to help identify contin-
gencies that contribute to and maintain their drinking
behavior. They were encouraged to make necessary changes
in personal and social areas to increase adaptive function-
ing. LOC was found to increase in internality after
therapeutic intervention. In a follow-up study, O'Leary,
Donovan, and O'Leary (1976) again found an increase in
internality as a function of treatment for alcoholism.
Marlatt and Marques (1976) increased internality and
decreased excessive drinking of 41 heavy social drinkers by
three different forms of relaxation treatment: meditation,
progresssive muscle relaxation and quiet reading periods.
Internals manifest greater resistance to persuasion
and conformity pressure (Gore, 1963; Crowne & Liverant,
1963; Strickland, 1965) while externals exhibit compliance
to and dependence on perceived authority, suggestibility,
attitude change and conformity (Ritchie & Phares, 1969;
Strickland, 1970; Biondo & MacDonald, 1971; Doctor, 1971).
This suggests that the internals may progress in therapeutic
intervention which is less directive (e.g., Rogerian, non-
directive style), minimal control and structure (Dua, 1970;
Kilmann, Albert, & Sotile, 1975), whereas the externals may

progress from structures (e.g., Ellis, directive style)

73

intervention within a controlled space-time format (Johnson
& Croft, 1975).

From the review of studies it is convincing that
LOC as a mediator of generalized expectancy of behavior in
different situations can be altered by various environ-
mental forces. An increase in internality orientation does
occur as a result of counseling, therapy, or training in—
tervention and establishes a relationship between increased
effectiveness and shifting perception of personal control,
and a greater potential for power and efficacy in mastering
the environment by controlling the environmental contin-
gencies. The rehabilitation counselor deals with various
types of physically disabled and psychologically disordered
clients. It is indeed imperative for the counselor to have
a close look at the usefulness of relatively simple and
easy to interpret personality constructs across types of
disabled clients in their adjustment toward their dis-.
abilities, in order to provide adequate and appropriate
information, counseling, treatment, training and inter-
vention. A sound adjustment to the disability and to the
environment is a prerequisite to successful rehabilitation.
the control orientations of vocational rehabilitation
clients give a direction not only to the client in diagnosis
but also to the counselors to plan appropriate client ser-
vices. The LOC construct can guide counselors in delineat-

ing the client's forces of orientation and dynamics in a

74

more positive direction in achieving goal directed behavior

by the client.

Generality—Specificity and Unidimensionality—
Multidimensionaligy

 

 

The Locus of Control (LOC) construct is a general-
ized expectancy and affects human behavior in a wide range
of psychological situations. An individual may manifest a
number of specific or circumscribed beliefs about LOC, each
of which may be more applicable to some specific situations
than to others. An individual's high score on an internal
direction for a particular aspect does not mean that he
will have the same high internal score in other situations.
For example, one may be highly internal in achievement
orientation but external in interpersonal relationships.

It seems that the Internal-External Locus of Control (I-E
LOC) does not possess complete generality like any other
variable. This means that "its effects on behavior are not
uniform and invariant across all situations." (Phares, 1976;
p. 45-46)

The general measure of Rotter's 29 item I-E scale
allows description of an individual's "average" LOC attri-
butes across situations. The construct may make a sound
prediction for an individual‘s general orientation, but may
fail to predict in any specific situation or specific ex-
pectancy. Therefore, the I-E scale is not unidimensional,

but rather multidimensional, in character.

75

Rotter (1966) recognized the multidimensionality of
the I-E scale as composed of one general factor and many
additional factors of only few items. The small amount of
variance associated with the additional factors was not
sufficiently reliable to construct sub-scales that can
hardly have any predictive validity.

Hersch and Scheibe (1967) noted that people who
scored higher on external often exhibit greater variance in
behavior than do people who scored strongly internal, and
suggested that the externality should further be differen-
tiated. Gurin et a1. (1969) studied race relations and the
I-E construct. The motivational dynamics of people dis-
advantaged by minority and/or economic status may have a
difference in significance in internal and external
direction. The assumption in internal belief represents a
positive affirmation when associated with success, a
feeling of competence. The internal orientation may have
negative implications when associated with failure and may
lead to self-blame and self-derogation. The relationship
between personal adjustment the I-E control is
curvilinear.

On the other hand, internal-external bases of
control are generally equated with skill vs. chance. This
may hold true for those who are in an advantaged position
in the social structure. The disadvantaged, disabled,
handicapped, low income group, and the minority group

experience obstacles that have nothing to do with chance

76

factors. For example, the circumstances of the labor
market, hiring or firing practices, and non-availablity of
transportation to conduct a job search, all reduce the
probability of getting a job. Similarly, racial discrimi-
nation and class-status obstacles may be perceived correct-
ly by the minorities as external but not a matter of
randomness, chance, or luck. Psychological and sociological
analyses of minority groups have made a distinction between
“individual blame” and "system blame.” It can have a
damaging and devastating effect on the minority group
members when they practice excessive self-blame or blame of
one's group by an internal orientation. Merton (1957)
pointed out the social dysfunctionality of such beliefs for
minority group members:

when people subordinated in a social system react

with invidious self-deprecation rather than against

the system, they accept a rationale for the exist-

ing system that serves to perpetuate their subor-

dinate position. (p. 34 in Gurin et a1., 1969)
Gurin et a1. (1969) factor analyzed Rotter's scale to deter-
mine the motivational dynamics of I-E control dimensions
and found evidence of two factors: Factor I, control
ideology--beliefs about how much control most people in our
society possess; and Factor II, personal control--the con-
trol that one can exert in his own life. In other words,
rejecting the notion that success follows from luck, chance,
fate, or accepting the traditional Protestant Work Ethic.

Joe and Jahn (1973) presented similar data with regard to

the personal control and social system control factors.

77

Varimax rotation of 23 item factor analyses yielded
two factors: Factor I--a belief concerning the felt mastery
over the course of one's life, Factor II--a belief concern-
ing the extent to which individuals can exert impact on
political situations (Mirels, 1970). Similar findings have
been reported by MacDonald and Tseng (1971) and Abrahamson
et al. (1973). A refinement of the meaning and measurement
of LOC through modification and addition of new items of
the independent factors of the I-E construct was attempted
by Reid and Ware (1973). The evidence of three factors of
I-E based on multiple regression analysis were presented.

Collins (1974) converted the 23 forced-choice items
to a 46-item Likert agree/disagree format and constructed
42 new items. In the modified version, an individual may
achieve an external score based on the following four
factors: (a) the world is difficult, (b) the world is
unjust, (c) the world is governed by luck, or (d) the world
is politically unresponsive. Similar findings have been
reported by Kaemmerer and Schwebel (1976) and Zuckerman and
Gerbasi (1977).

To focus upon multidimensionality within the LOC
measures, Reid and Ware (1974) followed-up their previous
study with others and found that the responses to items
related to beliefs about control of impulSes, drives, and
emotions were independent from either personal control or
social system control factors of the I-E scale. On the

other hand, the differences between items of control

78

attributed to self and to others were not found as reported
by Gurin et al. (1969). Some validity data for the use of
separate factors were reported which supported the
contention of multidimensionality and its utility.

Bar-Zohar and Nehari (1978) proposed a multidimen-
sional structure consisting of three dimensions: behavioral
outcome, control ideology, and situational contents, with
each dimension consisting of five factors: effort, ability,
objective obstacles, powerful others, and chance or luck
representing a continuum of internality to externality.
This Multidimensional Locus of Control Inventory shows
promising reliability and is only moderately correlated
with Rotter's scale. The linkage between the I—E continuum
and the multidimensional locus of control, and with their
differential weights could provide an integrated approach
to examine the perception of locus of control in a more

definitive way.

Locus of Control and Rehabilitation

 

The construct of LOC as an important rehabilitation
variable has been substantiated by Lipp et a1. (1968);
MacDonald and Hall (1969, 1971); Nemerofsky (1970); Tseng
(1970); MacDonald (1971c) and Andrisani (1977). Lipp et
a1. first uncovered the relationship of LOC in the percep-
tion of disability by the disabled as well as non—disabled
and substantiated that I-E orientation is an important

determinant of reaction to disability. A significant

79

relationship between I-E control dimension and impact of
disability was reported by Nemerofsky (1970).
MacDonald (1971c) first reported that I-E control
is a potentially important variable in rehabilitation by
investigating the attitude toward and reaction to disability
by three major disability classes: physical disability,
emotional disorder, and socially disadvantaged. He ex-
plained the contributions of an internal LOC to expectancy
for success:
Locus of control is not a motivational variable
but rather an expectancy variable. Findings that
persons do not try to improve their conditions
because of negative expectancies do not indicate
that those persons do not want to improve their
conditions. A large number of the victims in our
society are motivated to improve their living
conditions, but they have low expectancies for
success--often realistically. Motivation coupled
with positive expectancy equals optimism; motiva-
tion coupled with negative expectancy equals despair.
(p. 115)

MacDonald suggested the promotion of internal LOC by

various remedial programs.

Tseng (1970) investigated the relationship of I-E
LOC on a sample of 95 male and 45 female vocational reha-
bilitation clients and found that internally controlled
subjects achieved significantly higher levels of job pro-
ficiency, personal quality, training satisfaction and
employability. Poll and Kaplan De-Nour (1976) studied the
vocational rehabilitation of 40 chronic hemodialysis

patients in Israel. LOC correlated significantly with

vocational rehabilitation. It was found that 75% of the

80

internals were working compared with only 38% of the
externals.

Several studies using non-disabled samples
established the relationship between LOC and reactions of
employees to work characteristics (Kimmons & Greenhaus,
1976), task characteristics and work attitudes (Dailey,
1980; Knopp, 1981), occupational structure (Eichler, 1980),
job involvement and moderator of role perception/individual
outcome relationships (Batlis, 1980). Andrisani (1977) in
a National Longitudinal Survey for black and white young
men and middle-aged men provided considerable support that
I-E attitudes are strongly related to a number of aspects
of labor market experience for the disadvantaged.

Strickland (1978) in a review commented that
research on the I-E variable and psychological and/or
emotional difficulties is much more extensive than that on
the I—E variable and physical disability. Similarly, the
search of literature reveals only a few studies devoted to
investigation of this relationship with vocational reha-
bilitation clients. Since Tseng (1970), no study has
reported on the work attitude, work characteristics, work
satisfaction, and work behaviors of vocational rehabilita-
tion clients. Hence, this study will investigate the rela-
tionship of generalized I-E control expectancies as well as
specific expectancies of labor market experience of voca-
tional rehabilitation clients in the states of Louisiana,

Michigan, and New York.

81

Work Motivation

 

The predictive studies reviewed earlier appear to
be extremely valuable in attempting to isolate some of the
factors associated with successful rehabilitation. The
concept of ”motivation," "client motivation,” or more
precisely the client's "work motivation" is one of the most
essential factors of successful rehabilitation. If the
successful outcome is characteristic of the motivated
client, then the ability to predict outcome early in the
rehabilitation process is a necessary step towards
diagnosing the un-motivated, non-motivated, or high-risk
client. Rehabilitational efforts could then be directed
to develop motivation or to motivate the client towards
goal direction.

Motivation was defined by Lane and Barry (1970):

as the hypothetical process descriptive of the

interaction of all possible stimuli (variables,

experiences, etc.) which influence the differen-

tial energizing of responses, thus making some

responses dominant over other possible responses

in the same situation (p. 6)
Behavior then can be viewed as a function involving the
interaction of an individual and his environment. By
observing the changes in behavior, the presence or absence,
increase or decrease of motivation can be determined. At
any point in time, an individual's behaviOr is dependent
upon four interactive factors: (1) abilities, skills, and
knowledge; (2) inner personality variables; (3) physiologi-

cal condition; and (4) external situation-~environmental

82

factors. The first three can be termed intrinsic and the
last one as extrinsic factors influencing motivation. The
variables or factors that are composed of intrinsic and
extrinsic mode of motivational dynamics are not entirely
independent. They overlap as well as interact in a
sophisticated fashion to create a complex interaction of
organism-environmental factors (Pervin, 1968).

In concluding their review on client motivation in
rehabilitation, Barry and Malinovsky (1965) commented that
the concept of motivation is too broad, too complex, and
too vague a construct to be useful in rehabilitation
practice. In a survey of 280 rehabilitation counselors,
43% of them attributed client's failures to “lack of
motivation" or labelled difficult clients as ”unmotivated."
The need for understanding the motivation of rehabilitation
clients, especially their motivation to work, has been
emphasized as one of the most pressing problems faced by
rehabilitation counselors (Thoreson, Smits, Butler, &
Wright, 1968; Zandy & James, 1979). One of the major
reasons cited by counselors for unsuccessful closure of
vocational rehabilitation clients is lack of motivation.
However, nobody can be properly described as ”unmotivated.”
It may mean that the client's goals and aspirations are not
congruent with the counselor's. If there is congruence
between the goals of the client and the goals of the coun-
selor or of the rehabilitation process then the probability

is higher that the counselor will find the client to be

83

more motivated and willing to work with him (Tichenor,
Thomas, & Kravetz, 1975). Rehabilitation clients who were
judged as motivated by a panel of rehabilitation experts
were more likely to be closed ”employed" status 26 than
those judged unmotivated.

The concept of work motivation derives from the
concept of general motivation from psychology and is much
more complicated than a simple ordering of persons on a
motivated-unmotivated continuum. Cook (1982) reviewed
various work motivation models and suggested that the
models developed by industrial psychologists and rehabili-
tationists have more direct application to vocational
rehabilitation. Vinacke (1962) defines motivation as the
condition responsible for variations in the intensity,
quality and direction of on—going behavior. This defin-
ition is well accepted by the industrial psychologist and
suggests that work motivation consists of those things or
processes (intrinsic or extrinsic) that are independent of,
but interact with, a worker's level of aptitude, skill, and
understanding of job task (Campbell & Pritchard, 1976).
Individual differences (abilities, skills, motivation) and
environmental rewards (increasing or decreasing monetary
reward) do affect work motivation (Landy & Trumbo, 1980).

In rehabilitation, which deals with various types
of physically, mentally, visually and socially handicapped
persons, the problem is not motivation, but changing the

clients' perceptions of themselves and their environments.

34

Somatopsychologists maintain that when one limits the
importance of physique. holds a wide range of values, limits
disability to the impact of acutal impairment, and empha-
sizes personal intrinsic values while limiting external
comparative values (Wright, 1960), one has achieved an
optimal adjustment to disability which best leads to voca-
tional adjustment and goal directed work behavior. The
identification of realistic goals is an important motiva-
tional variable in vocational rehabilitation. Goals with
positive valence attract and goals with negative valence
repel. Goals can be enhanced by association with some
incentive, such as monetary reward.

McDaniel's (1976) Decision-Making Model, a motiva-
tional model, has direct application to rehabilitation.
According to this model, client participation in any
rehabilitation task depends upon three factors:

1. The client's subjective estimate of the
probability of a successful outcome or chance
of success.

2. The utility of personal value of task perfor-
mance and outcome.

3. The client's subjective estimate of the costs
or efforts involved.

He represented these three factors in an equation:

Motivation = P(Osé X U

Motivation is equal to the estimated probability of
successful outcome multiplied by task utility and divided

by perceived costs. Using this model, a counselor can

85

determine the likelihood of successful outcome for a client
and can estimate a client's level of motivation in various
rehabilitation tasks.

Work motivation can be understood by analyzing the
development of work personality as a result of interactions
with the environmental demands of job setting, such as
performance of some task, ability to get along with peers
and supervisor, conforming to certain work rules, and
maintaining a minimum standard of quantity and quality of
work. WOrk personality develops through a process of
internalization of cultural demands for activity, produc-
tivity and achievement (Neff, 1977).

Traditionally, motivation has been viewed as some
hidden, internal structure. The job of the rehabilitation
professional is to determine the amount of internalized
motivation and how it could be reorganized. Pordyce (1976)
criticized the traditional viewpoint as it imposes the
responsibility for rehabilitation on the client, not on the
treatment environment. It is the Behaviorist view that
motivating behavior can be changed, increased, decreased,
and/or taught, and that work-related behavior can be main—
tained by changing the environmental contingencies, for
example, by changing the philosophy, principles, and systems
of treatment and training facilities.

Walls, Masson, and Werner (1977) report that voca-
tional rehabilitation clients and/or their families are

eligible to receive one or more of 76 Federal assistance

86

programs. Vocational rehabilitation counselors find it
discouraging to work with public assistance recipients as
their success rate is lower. A primary reason for client
failure in rehabilitation is the work disincentive often
cited by rehabilitation counselors. The guaranteed benefit
and the loss-benefit rate are variables that influence the
choice to work or not to work. Money is a powerful motiva-
tor; however, the degree to which financial disincentives
act as motivational barriers to work depends upon the
amount of non-work-contingent income associated with
disability (Neff, 1977).

Just as financial aids act as disincentives or
barriers to work, functional limitations associated with
physical impairment and prejudicial attitudes of employers
also act as barriers. Therefore, the motivational dynamics
of a client could be understood by analyzing the importance
attached by the client to work-related goals and incentive
conditions that enhance or impede movement towards obtain-
ing and retaining a job. The concept of Job Readiness
Posture (JRP) advanced by Associates for Research in
Behavior, Inc. (1973) addressed these issues.

Job Readiness Posture is "... a term used to define
an individual's attitudes, perceptions and motivations as
they impact on his [or her] ability to obtain and maintain
a job." (Buros, 1978) In a broader sense, the Job Readi-
ness Posture is "the set of attitudes, motivations and

perceptions that permits an individual to accept and work

87

within the constraints established by a work environment."
(Transition to Work III, p. 2) The JRP helps in under-
standing the factors that facilitate or inhibit the
transition of a successful trainee from training status to
work status. The successful transition depends upon
obtaining and maintaining a regular job after a training
program and is based on the following three factors:

Adequate vocational skill training: A person must possess

 

sufficient skills for an entry level position in the labor
market.

Adequate placement opportunities: There must be a job
available in his locality utilizing the skill for which he
was trained. Training a person in an obsolete or non-
existent job or skill will end up in one's failing to get a
job.

Appropriate Job Readiness Posture: A person must have the
motivation, desire, and attitudinal skills to obtain and
retain a job. The success or failure of vocational rehabi-
litation clients in their vocational pursuits depends upon
an adequate presence or absence of these factors. The goal
of any training program is to improve and strengthen the
individual's JRP. When a person with poor JRP enters a
vocational skill training program, the training curricula
should be designed to facilitate development and internal-
ization of an appropriate JRP. The concept of JRP is

measured by the instrument called, the Vocational Opinion

 

Index (VOI).

88

The Vocational Opinion Index (VOI) is designed to
assess three psychological dimensions of the Job Readiness
Posture which consists of three factors:

I. Attractions to work - overall, benefits to

 

children, benefits to worker, better life style,
and independence;
II. Losses associated with work - overall, personal
freedom, time for family; and
III. Barriers to employment - medical, child care and
family, new situations and people, ability to get
and hold a job, transportation.
Measuring the three psychological dimensions generates a
profile and can be used to designate a trainee as either
I. A Worker
II. A Non-worker with predispositional problems, or
III. A Non-worker with attitudinal problems.
An analysis of the reasons or problems associated with a
non-worker JRP is made. From the detailed diagnosis of a
non-worker JRP, the counselor and the training staff can
develop a remedial prescription in order to help the
trainee to develop an appropriate JRP. If a non-worker JRP
is not addressed during the training program then there is
a probability that the individual will not obtain and
retain a job after training. The JRP score has been shown
to relate to work status after a vocational training
program more than 90% of the time (Benson & Whittington,

1974).

89

Upon failing to predict outcome, Lowe (1967) and
Flynn and Salomone (1977) suggested that future research
should look closely at the behavioral outcomes of the
client. Ability and degree of adjustment as a measure of
the client's behavioral outcome is a vital factor in
vocational adjustment (Fordyce, 1976). The degree of
adjustment reflects a level of motivation on the part of
the client which ultimately leads to vocational success.
Clients who are motivated for rehabilitation services are
more likely to be rehabilitated than unmotivated clients
(Salomone, 1972). For a rehabilitation client to be
successful in the labor market requires a certain standard
of appropriate attitude, perception and motivation. The
VOI measures these components of the client's behavioral
outcome in relation to work. Thus, this study will incor—
porate the concept of Job Readiness Posture as a measure of

the client's motivation for work.

Work History

 

The rehabilitation client's prior work history may
reveal certain factors, concerns, or issues associated with
adjustment to the world of work. Previous work history and
work experience may reflect the client's nature and type of
vocational development, vocational adjustment, work person-
ality, and commitment and motivation towards the develop-

ment of a stable career pattern. Characteristics such as

90

vocational development, vocational adjustment, work person-
ality, and career pattern should be viewed within a develop-
mental context, recognizing the fact that there is a process
which evolves over time and can be studied from one's
history of work. Roe's Personality Theory of Career Choice
(Roe, 1957) was formulated from the study of work history

of eminent scientists.

In predicting the psychiatric outcome of mentally
ill patients, Buell and Anthony (1973) found employment
history to be a significant predictor of post-hospital
employment and recidivism. When employment history was
deleted from the model, a major decrease in variance
occurred. It suggests that one variable, employment
history, accounted for the major amount of variance in
post-hospital employment. They concluded that the best
predictor of ex-psychiatric patients' future behavior is
their past behavior.

Lorei and Gurel (1973) predicted the post-hospital
employment and readmission of 957 schizophrenics from 20
demographic characteristics. Regression analysis revealed
that the extent of work experience in the preceding five
years was the most useful predictor of post-hospital
employment and recidivism. This is in agreement with the
findings of Buell and Anthony (1973).

Anthony and Buell (1974) replicated their previous
study. A significant amount of post-hospital employment

variance was accounted for by employment history. The

91

original study, the replication, and the study of Lorei and
Gurel (1973) consistently found the most useful single
predictor of post-hospital employment is employment
history. In their replicated study, Worral and Vandergoot
(1982) found a work history variable to be a primary
indicator of success of vocational rehabilitation clients.

The characteristic of work history is defined by
Tiedeman and O'Hara (1963) as:

a. the kinds of position held;

b. the sequence in which one holds these positions,
with reference to some "levels" factors such as
ability, responsibility, or socio-economic
status; and

c. The amount of time spent in each position.

Work history is a reflection of work adjustment. Important
factors include whether one holds a job for a long time or
many positions for a short time at the same level, moves
from lower to higher level jobs or higher to lower level
jobs, or is unemployed for sometime. Work history appears
to encompass multidimensional factors and its impact on a
future occupation is complexly determined.

A stable pattern of work history helps in building
an attitude of competence, self-reliance, and self-direction
for future vocational adjustment, aspiration and success in
employment. Mischel (1968) pointed out that "the best
prognostic index of future adjustment generally is previous

adjustment." (p. 38) However, the acutal impact of prior

work history on future success of vocational rehabilitation

92

clients may be not as simple as Mischel concluded. The
type of disability often imposes functional limitations
that may completely interrupt the history of career pattern
or work pattern. Also, the age of onset of disability is
another crucial factor. One who is disabled congenitally
or in early childhood is restricted in the exploration of
his environment with normal freedom. The disabled person
who has worked may differ significantly in the pursuit of
an occupation in comparison to the disabled person with no
work history.

The relationship of work history to post-hospital
employment of psychiatric patients have been demonstrated
(Fairweather et al., 1960; Fairweather, 1964; Buell &
Anthony, 1973; Lorei & Gurel, 1973; Anthony 6 Buell, 1974).
Also, the role of work history as a significant predictor
of vocational rehabilitation clients has been shown
(DeMann, 1963; Kunce & Miller, 1972; Worral & Vandergoot,
1982). The present study will explore this relationship a
step further as to how work history and experience before
disability relate to the work pattern and experience after
disability. Does the disability impose, enhance, or change
the occupational pattern? Furthermore, does receiving
various restorative, educative, and skill training from
vocational rehabilitation services have any effect in
maintaining an earlier work pattern? In other words, do

clients m0ve from lower level jobs to higher level jobs,

93

end vice-versa; or do they maintain the same level job

after receiving vocational rehabilitation services?

Summary

A client's probability of success in vocational
rehabilitation embraces a conglomerate of factors and their
interactions. Some of these are the client's own.charac-
teristics, while others are in the environment with which
he interacts to bring success in rehabilitation. Broadly
speaking, the following four dimensions independently and
interactively determine the outcome of the client's voca-
tional rehabilitation endeavor: (1) client variables,

(2) counselor variables, (3) process variables of vocational
rehabilitation, and (4) labor market variables. It would

be realistic to study all four facets together to determine
relative impact of component variables in the outcome
domain. However, in the scope of a doctoral dissertation

it was not feasible to consider all four facets. The major
limitation of the study is that it considered only the
client variables.

The extensive review of research delineates clearly
the impact of client characteristic variables in rehabili-
tation outcome. Each study showed the influence of some
variable that predicts outcome better than others. However,
it was established that demographic characteristics are

good predictors. Besides demographic characteristics, a

94

great many studies used psychological variables indepen-
dently and in combination with demographic variables. It
was found that adding psychological variables with demo-
graphic variables enhances the predictabilty of the outcome.
The use of various psychological instruments to assess the
clients' characteristics has been studied: The complexity
of administration and interpretation of the MMPI, the EPPS,
and other psychometrics rules out their use by rehabilita-
tion counselors. The Locus of Control construct, on the
other hand, measures individual personality differences,
and the LOC scale is easy to administer and interpret. It
provides a means for rehabilitation counselors to check
clients' control of orientation across types of disabled
clients at various points in time in the rehabilitation
process. The LOC construct also serves as a guide for
adjustment and improvement toward their disabilities by
providing appropriate information for counseling, treatment,
training, and other intervention. The LOC has been used
extensively with psychological and emotional disorders but
rarely with the physically disabled. No one other than
Tseng (1970) has used LOC to predict the outcome of reha-
bilitation clients which provides the rational for inclu-
sion of the LOC in this study.

Another psychological variable, work motivation or
motivation for work, has been shown to be related to

success of rehabilitation. Rehabilitation clients who are

95

motivated are more likely to be successfully rehabilitated
than unmotivated clients. Motivation is a complex phenom-
enon. However, the behavioral determinants of motivation
that are in the client's environmental contingencies were
suggested to be considered. Financial disincentives
resulting from various public assistance programs often act
as barriers to work. The concept of Job Readiness Posture,
a set of client's attitudes, perceptions and motivations
and its impact in the world of work, seems useful in
delineating a worker JRP from a non-worker JRP, by con-
sidering motivating factors based on individual perception
of relative pay-offs, losses and barriers associated with
working.

The best predictor of rehabilitation success is
past experience. The client's prior work history, experi-
ence, and level of satisfaction is a reflection of his work
attitudes, motivation and competence; vocational develop-
ment, adjustment and adaptation. The significance of work
history in rehabilitation outcome has been established.
However, the multidimensional impact of work history as to
the status of work done, depth of involvement, time spent,
complexity of task performed, wages received, requirements
for training or certificate/license and overall satisfaction
have not been studied. In this study, the multidimensional
factors of work history and its impact on rehabilitation
outcome after receiving vocational rehabilitation services

will be investigated.

96

Overall, it does appear that predictive accuracy
increases from the independent contributions made by the
predictor variables that are in the domain of client
characteristics. In the current study client character-
istics of demography, locus of control and motivation as
psychological variables, and patterns of work history will
be studied to determine their individual and combined
effect and interaction, and relative efficacy in deline-
ating the successful outcome from the unsuccessful. In
this context Berkowitz, Englander, Rubin, and WOrral (1975)
commented ”If we can forecast outcome with 10% more accuracy
than we are doing now both the savings in costs and the
benefits to client and society would be substantial."

(p. 125)

CHAPTER III

METHODOLOGY

Introduction

 

The previous chapters noted the need for further
research in developing a prediction model for vocational
rehabilitation outcome. It has also been suggested that the
inclusion of psychological variables would enhance the
predictive ability of the model. Theory and research on
internal-external locus of control, work initiative, and
work motivation as psychological variables of behavioral
determinants, along with the multidimensional impact of work
history have been reviewed. The potential positive effects
on outcome domain have been noted. Now, it seems reasonable
to study whether the introduction of psychological and work
history variables has any impact on the prediction model.

In this chapter the design and implementation pro-
cedure for the prediction model will be considered. The
sample selection, instrumentation, research procedure,
hypotheses, research design and method of analysis will

follow.

97

98

Sample

The subjects of this study were vocational rehabili-
tation clients who had completed all pre-placement services
and training. These clients were ready for employment and
already placed in Status 20 or soon would be. Clients who
met the following criteria were selected for the study:

a. Clients in Status 20, ready for employment, who
at the time of administration of the questionnaire were not
more than 60 days in Status 20; or

b. Clients who were at the end of their training
program, including on-the-job training, and would be placed
in Status 20, ready for employment; and

0. Clients who met one of the above criteria and

volunteered to participate in the study.

Rationale

 

The rationale for selecting the subjects as Status
20, ready for employment was two-fold. First, Status 20
clients were considered a unique baseline for data collec-
tion. A common criterion was that all clients received
pre-placement services appropriate to their handicapping
conditions, and according to the resources of vocational
rehabilitation agencies. Secondly, these clients were on
the verge of initiating their employment search, and this
was an appropriate time to determine their locus of control

orientation, work initiative and work motivation. Are they

99

job ready? Do they possess enough or appropriate skill
training, job seeking preparation and behavior? Could
factors be identified which could be related to their actual

success or failure in landing a job?

Procedure

 

The proposal of this research was developed in con-
sultation with the Deputy Director, Management Services and
Supervisor, Program Evaluations of Michigan Rehabilitation
Services (MRS) beginning January, 1982. A formal proposal
was submitted to MRS for review in June, 1982. After review
and several conferences a few modifications in selection of
sample and procedure were implemented at MRS's request to
make the study feasible. At the same time, the Human
Resources Center (HRC) in New York was also engaged in a
national study with similar research through a grant from
the National Institute of Handicapped Research. It was
planned that we would collaborate in our research to cross
validate each other's model in different states to improve
the predictive ability of the model (Appendix A). The HRC
had obtained approval of the Council of State Administrators
of Vocational Rehabilitation's (CSAVR) Committee on Research
for the project implementation in Michigan (Appendix A).
However, due to various constraints at MRS the project could
not be implemented in the State of Michigan as planned and

future prospects were in question.

100

The CSAVR approval cleared the way for other State
Directors to approve and implement the research project in
their States, should they choose to do so. The Research
Director of Research and Training Center, Stout Vocational
Rehabilitation Institute, University of Wisconsin-Stout,
Menomonie was contacted to explore the possibility of
implementing the project in the State of Wisconsin. He
forwarded the proposal to the Vocational Rehabilitation
Agency, State of Wisconsin (Appendix A). They had many
similar requests within the State that they could not
accommodate. Finally, the State of Louisiana approved the
project. The Executive Director, Division of Vbcational
Rehabilitation forwarded the administrative agreement to
utilize vocational rehabilitation services to implement the
research project (Appendix A). The Program Supervisor has
been assigned to coordinate the study with 10 district
offices and the facilities throughout the State.

A proposal of the research project was submitted to
the University Committee on Research Involving Human
Subjects, Michigan State University to meet the Federal
Policy on research with human subjects in order to protect
adequately the rights and welfare of the subjects in the

study. The approval of the Committee is in Appendix A.

Data Collection in Louisiana
A copy of the abstract and procedural guidelines

concerning client selection, client contact, testing room,

101

and possible problems for clients to respond to paper-
pencil tests was forwarded to the Program Supervisor for
review and transmission to the district supervisors and
counselors. The counselors contacted their eligible clients
and requested their voluntary participation in the study. A
schedule was prepared to visit the district offices in
October, 1982 to collect data. This had to be cancelled due
to an insufficient number of clients who had moved to Status
20 and who were less than 30 days in that status. Only 25
clients volunteered and four district offices either did not
have any client in Status 20 or were not willing to co-
operate. Therefore, the subject selection criterion was
changed by extending the allowable time in Status 20 from 30
days to 60 days, in order to add a significant number to
those who had already agreed to participate. In reality
that was not the case. Another schedule was made to collect
data in November, 1982 and data were collected accordingly.
On the first day of data collection at the New
Orleans district office, none of the clients who volunteered
and agreed to participate showed up for testing. If this
would be the case in other district offices then the study
would have to be aborted or the number of subjects would be
very small. Hence, it was decided to use a captive popula-
tion of vocational rehabilitation clients at the training
facilities. These clients were at the end of their training
program and would be looking for employment (Status 20). A

breakdown of participating clients from the district offices

102

and training facilities is shown in Table 3.1. Whereas it
was expected that there would be at least 45 clients, only 9
or 20% of the volunteered clients showed up in 7 district
offices; and the remaining 54 clients were from the various
facilities.

At each district office and facility, the researcher
met with the supervisors, counselors, training and placement
staff for 15 to 20 minutes to describe the research in
brief, to answer questions, and to encourage cooperation. A
standard procedure was followed for the Introduction to the
Counselors and Supervisors (Appendix-B). In view of the
smaller return rate at the district offices, a request was
made to contact the clients who failed to appear and other
eligible clients. The directions for administration of the
instrument were explained and a sufficient number of instru-
ments were provided. Three districts provided 15 subjects
of which four had to be rejected, since one was deceased and
others did not meet selection criteria.

The researcher met the clients and gave a brief
introduction about the research, emphasizing the importance
of their participation and cooperation. A standard pro-
cedure was followed for the Introduction to the Client
(Appendix B). The clients read and signed the Consent and
Release of Information Form (Appendix C) before the instru-
ments (Appendixes D Through I) were administered. The

instruments were checked individually for errors, omissions

103

TABLE 3.1.--Number of Clients Who Agreed to Participate,
Number Rejected and Sample from District Offices
and Training Facilities in Louisiana; and
Training Facilities in Michigan and New York

 

 

 

 

 

 

 

Louisiana
District Offices Participated Rejected Sample
New Orleans 0 0 0
Metairie 2 0 2
West Bank 3 +2* 0 5
Houma 1 0 1
Baton Rouge 1 0. 1
Hammond 1 +4* -2* 3
Shreveport 1 +9* -2* 8
Trainin Facilities
DeIgado Rehabilitation Center,
New Orleans 4 0 4
Goodwill Rehabilitation Center
New Orleans 5 -3 2
Holman Vbcational Center, New
Orleans 7 -1 6
Greenwell Springs Hospital
(Alcohol, Drug, T.B., and
Psychiatric) 7 -1 6
Southeast Louisiana Hospital,
Mandeville (Psychiatric) 7 -4 3
Veterans Administration Medical
Center, Shreveport (Alcohol) 9 0 9
Louisiana Technical Institute,
Monroe (Corrections) 14 -14 0
7 -27 50
Michigan
State Technical Institute a Re-
habilitation Center, Plainwell 29 -7 22
Professional Job Club Develop-
ment, Inc., Lansing 34 _ 0 34
New York
Human Resources Center, Long
Island 9 0 9
Total: 149 -34 115

 

* Tested by the counselors

104

or double responses. It took 45 minutes to 90 minutes to

respond to the instruments.

Data Collection in Michigan

 

In December 1982, MRS encouraged the researcher to
collect data under some restricted conditions from two
sources, the State Technical Institute and Rehabilitation
Center (STIRC) at Plainwell, Michigan and Professional Job
Club Development, Inc. in Lansing, Michigan.

The STIRC is operated by MRS and offers vocational
and technical training to handicapped adults of the State in
12 different trades. Fifty percent of the trainees repre-
sent severely disabled clients. The success rate of the
graduates of STIRC during 1981-82 was 62%. Out of 49
clients graduated in December, 1982, 28 participated in the
study two days prior to their graduation. The remaining 21
left the center for on-the-job training leading to a job or
already had a job in different parts of the state or the
country. These clients representing 43% of the graduates
were mailed a packet consisting of a letter from the Princi-
pal, Consent and Release of Information Form, the instru-
ments, and a self-addressed stamped envelope. Only one
responded. One of the major drawbacks was that due to
administrative difficulties at MRS, the counselors of these
clients could not be contacted to obtain some demographic
and follow—up data. This information could only be obtained

from the clients and records at the MRS state office.

105

Professional Job Club Development, Inc. is a private
agency engaged in intensive job search programs. The
counselors at the Lansing District Office refer their
clients for a two to four months training program to improve
job-seeking skills. These clients may not be representative
of the client population in the Lansing district office.
These clients lacked skills and/or behavior relevant to job-
seeking. Not all clients attend this special program. A
total of 34 clients were tested and four were rejected as

they did not meet the criteria of sample selection.

Data from New York

When it appeared that it would not be possible to
collect data from Michgian and Wisconsin, the Human
Resources Center (HRC) in New York collected some data for
this project. The HRC is a private training facility for
vocational rehabilitation clients in Long Island, New York.
It is also a regional Research and Training Center. For
their research HRC used the Demographic, Modified Locus of
Control Scale and the Vocational Opinion Index. However,
for this project they used all five instruments with 9
subjects. There were some missing data in demographic and
followhup variables as they used a different format on those

two instruments.

106

Follow—up Data

 

Follow-up data were collected after six months from
both the counselors and the clients to determine client
employment status, the dependent variable measure of the
study. In Louisiana, data were collected by telephone from
the State Office of Vocational Rehabilitation. Counselors
reported data on all 50 clients from the available informa-
tion on clients' case files; however, only 31 clients could
be contacted. A considerable attempt has been made to
locate the clients in different states based on available
forwarding telephone numbers. Some clients were called in
three different states and for some as many as 10 calls were
made.

In Michigan, data from the counselors of the 34 Job
Club clients were obtained, but similar data for the 22
STIRC clients could not be obtained as the MRS state office
did not permit the investigator to contact their counselors.
Among 34 Job Club clients, contacts were made with 22
clients, while two refused to talk and 10 had no phones.
Similarly, out of 22 clients of STIRC, 19 were contacted and
three had no phones. To clients who had no phones, a
follow-up letter (Appendix J) including a Follow-up Quest-
ionnaire-Client (Appendix I) with a stamped return-address
envelope was mailed. For 10 Job Club followbup mailings,
three clients responded, three letters were returned with no
forwarding address, and four did not respond. Of three

STIRC clients who were sent follow-up mailings, two

107

responded and one did not. No follow-up letters were sent
to clients in Louisiana who had no phones, because most of
these had left the-state without a forwarding address on
file.

For the nine clients in the New York sample, the HRC
provided the data from the client and no data from the
counselors. The New York data provided very minimal
information compared with that collected from Michigan and
Louisiana.

Some of the common problems encountered in collect-
ing follow-up data from the clients were: no phone, phone
disconnected with no further information, unpublished
number, counselors protected privacy of client, client
refused to talk, family member guarded client information,
family member made a barrier to talk to client, client moved
out of state, and client jailed for criminal conduct.
Collecting follow-up data from the counselors was no problem
at all. In only a few cases, changes in counselors, coun-
selor overload and client's lack of communication with coun-
selors prevented the researcher from obtaining up-dated

information on client's outcome.

Instrumentation

 

A package consisting of the following instruments
was used in collecting data: I. Demographic Questionnaire,

II. Locus of Control Scale, III. Modified Locus of Control

108

Scale, IV. Vocational Opinion Index, V. Work History

 

Questionnaire, and VI. Follow-up Questionnaire. The Locus

of Control Scale, Modified Locus of Control Scale and

 

Vocational Opinion Index are standardized instruments. The

 

Demographic Questionnaire, Work History Questionnaire and
Follow-up Questionnaire were devised for the purpose of this
study to elicit appropriate data. The items in these
questionnaires were selected on the basis of literature
reviewed and the variables that were found to be effective
in eliciting information in predicting the rehabilitation
outcomes. No formal evaluation of individual items was
made. In general, the information sought was factual in
nature. Each instrument will be reported separately in the

order in which it was used in the research.

I. Demogpaphic Questionnaire (Appendix D)

This questionnaire was devised to determine the
client characteristics of the predictor variables. The
items were chosen on the basis of literature reviewed in
Chapter II, which indicated that the following variables
have a differential effect in the outcome domain and are
good predictors for vocational rehabilitation clients: age,
sex, race, marital status, number of dependents, education,
disabilities, geographic location, sources of referral,
sources of support, public assistance. A part of the
information in the questionnaire was obtained from client

files when they were unable, or unwilling to provide

109

it. The clients' signed consent was obtained for release of

such information from case files.

II. Locus of Control Scale (Appendix E)
Locus of Control, a personality characteristic,
measures individual differences of generalized expectancy

across situations. Rotter's Internal-External Locus of

 

Control Scale (Rotter, 1966) contains 29 items. The scale

 

is a forced-choice self-report inventory. Internal and
external statements are paired that may appear equally
acceptable but differ in validity. The paired statements
may both be desirable or both be undesirable. It allows the
subjects to choose the one statement they believe strongly.
Out of 29 items, 6 are innocuous items to disguise the
purpose of the test. One point is given for each external
statement selected, yielding a score of 0 to 23. The lower
the score, the more internal the attitude, and the higher.
the score, the more external the attitude.

Internal consistency estimates of reliability of
the I-E Scale ranged from 0.65 to 0.79 with nearly all
correlations in the 0.70's (Rotter, 1966). Rotter contends
that this is a very high internal consistency for an
additive scale. The item and factor analyses support his
contention. Test-retest reliability for Several samples
varied from 0.49 to 0.83, depending upon the time interval
and sample involved (Rotter, 1966). These are similar to

the findings of Hersch and Scheibe (1967).

110

The forced—choice technique of the LOC scale allows
for control of social desirability. Studies regarding the
relationship between internal-external LOC and social
desirability have been contradictory. Non-significant

correlations between the I-E Scale and the Marlowe—Crowne

 

Social Desirabilitnycale (M-C 805) have been reported by

 

Strickland (1965), Rotter (1966), Tolor (1967), and Tolor
and Jalowiec (1968); while Feather (1967b), and Altrocchi,
Palmer, Hellman, and Davis (1976) found significant correla-
tions. Berzins, Ross, and Cohen (1970); and Cone (1971)
compared the I-E Scale scores and the Edward Social

Desirability Scale scores and reported significant correla-

 

tion. In Germany, Schreiber (1980) used a German version of
the I-E Scale and the M-C SDS on vocational rehabilitation
cleints and has been able to reduce the social desirability
and the I-E scores correlation to zero by using signal
detection theory.

The above findings indicate that the I-E Scale is
not free from social desirability as contended by Rotter
(1966). However, Rotter (1975) explained some problems and
misconceptions related to the construct of the I-E Scale:

Even though the forced-choice method allows some
control over social desirability, it is well-
known that such measures change in their rela-
tionship to social desirability under different
testing conditions. (p. 62)

It may be concluded that the I-E Scale is neither

entirely free from the effects of social desirability nor

seriously impaired. Rotter (1975) further commented

111

"... the I-E scale is subject, as are all personality
measures, to the conditions of testing and the known or
suspected purposes or nature of the examinee.” (p. 62) At
least a portion of the variance associated with the I-E
scale is attributable to social desirability. However, the
exact amount of variance will vary depending upon the
subject's perception of reinforcements from the specific
testing conditions or situations. 1
Construct validity of the I-E Scale is indicated
by the differences in behavior for persons above and below
the median of the scale or from correlations with behavior
criteria or mean differences in total scores between groups
(Stratoudakis, 1976). A substantial number of studies
reviewed in Chapter II on locus of control, its antecedents
and behavior correlates, offer strong evidence of the con-
struct validity of the I-E Scale. Discriminant validity is
indicated by the low relationships with variables such as
intelligence, social desirability, and political liberal-
ness. It can also be expected that the I—E Scale will de-
monstrate improved predictive validity with increased reli-
ability performance. The I-E Scale has been widely used
and a great deal of validity data is reported in Rotter
(1966, 1975), Joe (1971), and Robinson and Shaver (1973).

The significance of the Internal-External Locus of

 

Control Scale to predict job proficiency, employability, and
training satisfaction of vocational rehabilitation clients

was first shown by Tseng (1970). The LOC is a promising

112

rehabilitation variable for the physically handicapped,
emotionally disabled, and socially disadvantaged as empha-
sized by MacDonald (1971). Strickland's (1978) study sup-
ported the view of MacDonald (1971). Also, Flynn and
Salomone (1977) suggested the use of behavioral character-
istics that are in the client's environmental contingencies
which support and maintain successful vocational adaptation.
Hence, the present study utilized the concept of LOC as a
psychological variable of behavioral determinants on voca-
tional rehabilitation clients. The efficacy of the concept
of LOC was used in delineating the successful rehabilitants

from the unsuccessful.

III. Modified Locus of Control Scale (Appendix F)

Rotter's I-E Scale measures general expectancy
across situations. The work of Crandall et a1. (1965);
Hersch and Scheibe (1967); Gurin et al. (1969); Mirels
(1970); Reid and Ware (1973, 1974); Collins (1974);
Kaemmerer and Schwebel (1976); Zuckerman and Gerbasi (1977);
and Bar-Zohar and Nehari (1978) indicated that the I-E Scale
is multidimensional instead of unidimensional. As a result,
the attempt has been made to develop subscales by factor
analysis (Gurin et al., 1969; Mirels, 1970; MacDonald &
Tseng, 1971; Abrahamson et al., 1973) and by modifying
Rotter's original items or adding new items (Gurin et al.,
1969; Reid & Ware, 1973, 1974; Collins, 1974) to measure

expectancy to a specific situation. The revised scale may

113

turn into a more useful tool in the research of human
behavior, according to Zuckerman and Gerbasi (1977).

In an attempt to develop a sub-scale, Gurin et al.
(1969) found that four items of the I-E Scale have higher
correlation in predicting the I-E control dimension for
minority and disadvantaged populations. They demonstrated
that only four items captured the payoffs individuals
ascribed to their initiative when the items were phrased in
the first person.

This four item sub-scale, the investigator labelled
as the Modified Locus of Control Scale. Each item consists
of a forced-choice response to two attitudinal statements,
one reflecting an internal attitude and the other an
external attitude. The subject first chooses between the
two statements on each item. Then the subject indicates how
closely the forced-choiced response on each item represents
his/her point of view on the issue. Is this statement "much
closer” or ”slightly closer” to his/her opinion? Each item
bears a score from 1 to 4 on the basis of an increasingly
external attitude. The four scores possible for each item
are: 1 for the internal response ”much closer," 2 for
internal response "slightly closer,” 3 for external response
"slightly closer," and 4 for external response "much
closer.” The total score on the scale may range from 4 to
16. The lower the score, the more internal the attitude and
the higher the score, the more external the attitude. For

example, if one received a score of 1 on each of the 4 items

114

the total score would be 4. This score would suggest the
person to be internally oriented.

Andrisani (1977) used this Modified Locus of Control
Scale as part of the National Longitudinal Surveys and
showed that they are a significant predictor of the labor
market experience for young and middle-aged white and black
men. This modified version of the I-E Scale was also used
as part of the National Longitudinal Surveys by the Center
for Human Resources Research (1979). The Modified Locus of
Control Scale was administered to the subjects of the
present study to find the significance in predicting labor

market experience for vocational rehabilitation clients.

IV. Vocational Opinion Index (VOI) (Appendix G)

The VOI was developed by the Associates for Research
in Behavior, Inc. in 1973. It measures the construct of
motivation to work status after a vocational training
program. The index is a measure of Job Readiness Posture
(JRP) which is defined as a set of attitudes, motivations,
and perceptions and its impact on the ability to obtain and
maintain a job. The VOI delineates three psychological
dimensions: (1) Attractions, (2) Losses, and (3) Barriers
as perceived by an individual with regard to work.

The VOI consists of 55 multiple choice questions in
three sections. The Personal Data Questions section

consists of 13 items, of which numbers 1 through 4 are

115

concerned with demographic data, while the rest are related
to whether the trainee currently is participating or has
completed the training program. Form A is used for in-
program and Form B is used for out-program. Section I has
28 items about working or about things that can or might
happen when people work. The responses are scored in a
Likert—Type Scale format (Likert, 1932) in five categories:
strongly agree, somewhat agree, neither agree nor disagree,
somewhat disagree, strongly disagree; and a weight of 1 to 5
is assigned for each response, respectively. Section II
consists of 14 item statements about problems that might
make it difficult for some people to get a job or to keep a
job. The responses are scored in Likert-Type Scale format
in five categories: extremely, very, somewhat, a little,
not at all, and a weight of 1 to 5 is assigned to each
resopnse, respectively.

An Areas of Concern Sheet is prepared from the
scores of the three sections of the VOI representing the
three psychological dimensions of the JRP and their
following respective factors:

Attractions

 

A1 2 Overall

A2 = Benefits to Children
A3 8 Benefits to Worker
A4 = Better Life Style

A5 = Independence

116

Losses

L1 = Overall

L2 - Personal Freedom

L3 = Time to care for and be with family
Barriers

B1 a Medical

B2 8 Child Care and Family

B3 - New Situations and People

34 - Ability to Get and Hold a Job

85 a Transportation

Items 1 to 23 of Section I are used to calculate the
Areas of Concern factors in Attractions and Losses. All the
items in Section II and only item numbers 7 and 9 in
Personal Data Section are used to calculate the factors in
Barriers. The rest of the items in the VOI are for research
purposes and are not calculated for preparing Areas of
Concern.

The VOI could be administered on an individual or
group basis. A fifth grade reading level is required to
complete the VOI. It is available both in English and
Spanish, and in two forms, Form A for current trainees and
Form B for graduates of the training program. To derive
maximum benefit, the VOI should be administered to the
trainees at three points in time: (a) Intake, (b) Exit
from program, and (c) Follow-up. The initial administra-
tion of the V01 provides remedial training prescriptions to

assist a non-worker in developing a worker's JRP. At the

117

end of the vocational skill training program, the VOI is
readministered to evaluate the effectiveness of the remedial
prescription. The VOI is again administered to the trainees
13 weeks after they leave the training program as a measure
of validation of their expected work status as indicated by
both the end of program response VOI and their actual work
behavior. In the current study, however, the VOI was admin-
istered to the vocational rehabilitation clients at the end
of their training program to measure the clients' JRP. This
was considered a measure of client's motivation to seek,
obtain, and maintain employment, once they had achieved
readiness for placement (Status 20).

The VOI has been designed, tested and normed nation-
wide specifically for disadvantaged trainee populations.
The data were collected from 13 Manpower Development
Training Act (MDTA) Skills Centers, on 2,000 disadvantaged
trainees in 24 skill areas on the basis of availability of
various skills, distribution of culture (black, white, and
Spanish American) and geographical distribution. The
National Study indicated that the VOI surpassed all minimum
criteria of reliability and validity for a psychometric
instrument; see Table 3.2. The geographic region within the
United States (except Puerto Rican living in Puerto Rico),
sex and skill area had no significant influences on VOI
scores. This means that the VOI requires a single set of

norms applicable to the entire U.S. trainee population

118

TABLE 3.2.--Coefficients of Homogeneity for the Vocational
Opinion Index from Transition to Work III.

 

 

SCALE n ALPHA” FORM
English
Attraction 534 0.82 i A
Loss 534 0.76 A
Barrier 534 0.86 A
Attraction 494 0.79 B
Loss 494 0.76 B
Barrier 494 0.86 8
Spanish
Attraction 161 0.76 A
Loss 161 0.66 A
Barrier 161 0.81 A
Attraction 143 0.76 B
Loss 143 0.84 B
Barrier 143 0.84 B

 

* An Alpha of 0.60 or greater is deemed an acceptable level
of homogeneity (reliability).

119

(except Puerto Rico) and a robust measure of JRP (Associates
for Research in Behavior, Inc., 1973).

Three types of validity data were established for
the VOI. Of over 2,000 disadvantaged trainees who were
asked to complete the VOI, less than one percent refused to
answer. From this high response rate may be inferred the
face validity of the instrument. Factorial validity was
established from factor analysis of the data from the VOI,
which yielded three distinct factors defined as Attractions,
Losses and Barriers. Trainees' Job Readiness Posture on the
V01 correlated with their behavioral work status after
training over 90% of the time, an estimate of criterion-
related validity (Associates for Research in Behavior, Inc.,
1973).

The clients' motivation in rehabilitation, more
precisely, their work motivation can be found from environ-
mental contingencies of behavioral determinants that support
and maintain successful vocational adaptation (Fordyce,
1976; Flynn & Salomone, 1977). The ability and degree of
vocational adaptation and vocational adjustment reflect a
level of motivation which contributes to vocational success.
To be successful in the labor market, the rehabilitation
client must possess appropriate work motivation. The VOI
measures the JRP as motivation for work. 'The VOI was
administered to the subjects to delineate whether the level
of motivation could be utilized to differentiate between the

employed and the unemployed.

120

V. Work History Questionnaire (Appendix H)

 

This questionnaire was developed to determine how
previous work history relates to future employment success
after receiving vocational rehabilitation services. This
may or may not provide any significant difference for con-
genitally disabled persons and disabled persons with no
prior work history. However, for disabled persons who have
worked, the nature and type of work, level and depth of work
may affect their pursuit of a future occupation. The sig-
nificance of work history on future vocational aspirations
and its impact on future employment were equivocal and
confirmed by many studies (Fairweather et al., 1960; DeMann,
1963; Fairweather, 1964; Kunce & Miller, 1972; Buel &
Anthony, 1973; Lorei & Gurel, 1973; Anthony & Buel 1974).

The questionnaire is intended to assess various
aspects of the client's prior work history with respect to
relatedness and unrelatedness of previous employment and its
multidimensional impact such as level and depth of involve-
ment, time spent, training required, certificate or license
required, wages received, and job satisfactions. Also
included in the questionnaire are the kinds of jobs the
clients are looking for and perceptions on their degree of

success in each job.

VI. Follow-up Questionnaire (Appendix I)
The questionnaire was devised to determine the

dependent variable measure of the study, the employment

121

status at the end of six months after being placed in Status
20. Items were selected for the questionnarie, with a view
to the kinds of information obtained in the Work History
Questionnaire, so that a common theme of multidimensionality
of work history could be compared with employment status at
follow-up. For those who became employed, detailed charac-
teristics of job obtained were recorded. For those who were
unemployed, the reasons of unemployment and their future
plans to alleviate the unemployed conditions were sought. A
parallel form of the questionnaire was made, the Followbup
Questionnaire-Counselor and the Followbup Questionnaire-
Client to study the degree of agreement in reporting follow-
up data by the counselors and the clients in terms of
reliability, counselor-client confounding, and accuracy of
data. The follow—up data were gathered both from the
counselors and the clients by telephone interview by the

researcher.

Hypotheses

 

The following hypotheses were tested:

Hypothesis 1: The clients with an internal locus of
control will have more work motivation than the clients with
an external locus of control.

Hypothesis 2: The clients with an internal locus of
control will be employed and obtain employment earlier than

the clients with an external locus of control.

122

Hypothesis 3: There will be a positive relationship

 

between the clients' demographic characteristics and employ-
ment status in post VRS.

Hypothesis 4: There will be a positive relationship

 

between the clients' work motivation and employment status
in post VRS.

Hypothesis 5: There will be a positive relationship

 

between the clients' prior work history characteristics and
employment status in post VRS.

Hypothesis 6: A linear combination of predictor
variables, such as demographics, locus of control, work
motivation, and work history characteristics will be able to
differentiate the employed from the unemployed clients in
post VRS.

Hypothesis 7: There will be a positive relationship

 

between the clients' prior work characteristics and post VRS

work characteristics.

Research Design

 

The study was designed to find out the predictive
relationship of various client characteristics to the
outcome domain. These characteristics included demographic
variables, locus of control, work motivation, and work
history. "The outcome of any social model is a function of
its participants and its social situation." (Fairweather &
Tornatzky, 1977, p. 46) The outcome of a social model,

whether it is molar or molecular, is dependent upon the

123

individual participant and the social context in which it
operates. The outcomes for rehabilitation services are
perceived generally as multivariate and changing. However,
in this study, only one outcome criterion, employment
status was investigated. The participant variables were
client attributes, such as, age, sex, education, work
motivation, expectancies, attitude, etc. The social
situation variables may be classified as internal and
external. Internal social variables are those intrinsic to
the social model, such as, functional nature of the state
vocational rehabilitation system, ethics in serving the
handicapped, the role of the counselor and other process
characteristics. External social variables are those that
impinge upon the model by the interaction of the physical
and social environment; those perceived relevant to the
study include time, social climate, state of the economy,
and geographical location (Fairweather & Tornatzky, 1977).

This study was conducted in three different geo-
graphical locations, a Northern, a Southern and an Eastern
state when the national economy was ravaged by high infla-
tion and high unemployment. Therefore, the impact of the
external and internal social variables will have a multidi-
mensional effect on the participant variables as well as on
the outcome domain.

The main objective of the study was to predict the
success of vocational rehabilitation clients from client

demographic characteristics, locus of control, work

124

motivation, and work history variables. The design of the
study was a multivariate correlational predictive study. It
is a common design in field studies (Bolton, 1974). The
outcome of prediction studies provides the following three
types of information (Borg & Gall, 1976):

a. The extent to which a criterion behavior pattern
can be predicted;

b. Data for theory building about possible
determinants of the criterion behavior pattern;
and

c. Evidence regarding the predictive validity of the
test or tests that are correlated with the
criterion.

Correlational predictive studies require computing
correlations between a complex behavior pattern, the
criterion and the predictor variables thought to be related
to the criterion. The predictor variables (independent
variables) are measured sometime before the citerion
behavior (dependent variable) occurs. In this study, the
clients' demographics, locus of control, work motivation and
work history variables represent the independent variables
or predictor variables, and the employment status after six
months of the study represents the dependent variable or
criterion variable. 1

Correlations are useful to discover or clarify
relationships among variables and to explore possible causal

factors that can later be tested in an experimental design.

125

It permits the measurement and study of a great number of
variables and their interrelationships simultaneously. The
primary concern is to gain a better understanding of a com-
plex behavior pattern by studying the relationships between
these patterns and the variables to which they are hypothe-
sized to be related (Borg & Gall, 1976). An associative
technique of a systematic multivariate correlational method
looking for predictive relationships was useful in the

study.

Analysis

The basic form of data analysis for the study was to
correlate each predictor variable with the criteria. When a
study involving a large number of variables tries to deter-
mine their degree of predictive relationship or the degree
of association, a multivariate analysis is warranted. The
most appropriate multivariate correlational technique for
the study was the Discriminant Function Analysis and
Canonical Analysis. The Pearson Product-Moment Correlation
(a parametric) and Chi-square (a non-parametric statistic)
were also computed.

The Discriminant Analysis (DA) is a powerful tech-
nique to examine the difference between the two groups of
rehabilitation clients, employed or unemployed with respect
to several predictor variables simultaneously. The Dis-

criminant Analysis addresses characteristics, attributes or

126

psychometrics which can predict or differentiate the
rehabilitants from the non-rehabilitants.

The importance of the DA technique is two-fold in
the study: (1) to study the relationship among the vari-
ables of the given sample and (2) to classify and diagnose
for predictive purposes (Klecka, 1980). With multiple
independent variables and with two groups, the Discriminant
Function gives the ”best” prediction by calculating the
least-squares ”best” composite score to maximize the dis-
crimination between the groups. The Discriminant Function
is a regression equation with a dependent variable, employ-
ment status representing the predicted group membership.
The usefulness of DA has been commented upon by Kerlinger
(1973): “Although discriminant analysis has not been used
much in behavioral research, it has interesting potentials."
(p. 650) Hence, the major emphasis on the analyses of the
study using DA is to examine the underlying associative
make-up of the study variables.

Another multivariate correlational technique used
was Canonical Analysis (CA). When a research project is
confronted with a set of independent and a set of dependent
variables, CA is an appropriate statistic to use. CA uses
the least-squares principle and derives linear composites
for each of the sets of variables in such a way that the
correlation between the two sets of linear combinations is
at maximum. In other words, CA tries to seek maximum

weights for the independent and dependent variables to

127

determine which of the independent and dependent variables
are more closely associated or related. The degree of re-
lationship or association is expressed as Canonical Correla-
tion, Rc between these two composites. The square of the
Canonical Correlation, R3, is an estimate of the variance
shared by the two composites (Kerlinger & Pedhazur, 1973).
CA is the generalization of Multiple Regression Analysis
(MRA) to any number of dependent variables. MRA is a spe-
cial case of CA. CA was used to study the underlying asso-
ciative make-up of the multidimensional work characteristics
of rehabilitation clients at three different periods in
time: before disability, after disability and in post VRS.
The Statistical Package for Social Sciences,
SPSS-6000, Version 8.3 on CYBER 750 Computer at Michigan
State University was used for the statistical analysis of

the data.

Summary

Samples of rehabilitation clients from the states of
Louisiana, Michigan, and New York were used to study the
relationship between client characteristics (demographics,
locus of control, work motivation, and work history pat-
terns) and client success in employment after receiving
vocational rehabilitation services. These clients were
ready for employment (Status 20) or at the end of their

training program in training facilities and would be placed

128

in Status 20. All subjects volunteered for the study. The
data were collected from seven district offices, three
training centers, three hospitals in Louisana; one training
center and one Job Club in Michgian; and one training cen-
ter in New York.

A total of six instruments were used to measure
client characteristics. Three of these were standardized
instruments and three were developed specifically for the
study. The measures used in the study included: (1) Demo-
graphic Questionnaire to ascertain the clients variability
in demographic characteristics; (2) Locus of Control Scale
to measure the individual differences of generalized ex-
pectancy across situations; (3) Modified Locus of Control
Scale to measure the individual differences of specific
expectancy relevant to the initiative to work; (4) Vbca-
tional Opinion Index used to determine the Job Readiness
Posture as a measure of work motivation; (5) Work History
Questionnaire to study the pattern, level, and depth in-
volved in work and its relation to future employment; and
(6) Follow-up Questionnaire to determine the status of
employment and its characteristics after six months of the
study.

This was an exploratory predictive field study.
Discriminant function, a multivariate correlational pro-
cedure, was considered the most appropriate to examine the
underlying associative make-up of the study variables. The

hypotheses investigated whether the characteristics of the

outcome criteria could be differentiated on the basis of the
characteristics of the domains of predictors, individually
or in combination. The significance of different hypotheses
was explained by the Chi-square values, Correlation
Coefficients, Canonical Correlations between the sets of
variables, and the predictive accuracy of the discriminant

function with their respective level of significance.

CHAPTER IV

RESULTS

Introduction

 

This was an exploratory study to develop a predic-
tion model for rehabilitation clients, relating client
predictor variables (demographic, psychological, and work
history) to outcome criteria observed after Vocational
Rehabilitation Services (VRS).

Two major areas were addressed in the analysis of
data. The first area presented a descriptive summary of
client characteristic predictor variables, including demo-
graphics, two measures of Locus of Control, work motivation,
the multidimensional aspects of before and after disability
work history, and a detailed outcome measure collected from
the counselors and the clients.

The second area presented the formal testing of the
hypotheses which were explored in the study. The test
statistics and analyses used in examining the hypotheses
were Chi-square, Pearson Product-Moment Correlation, Dis-
criminant Analysis, Stepwise Discriminant Analysis, and
Canonical Correlation Analysis. The rationale for the
selection of test statistics and theories associated with it
are also discussed.

130

131

Description of Sample

 

Demographics

 

A total of 115 vocational rehabilitation clients
voluntarily participated in the study, of whom 50 were from
Louisiana, 56 from Michigan, and 9 from New York. Table 4.1
is a presentation of data on the clients' demographic char-
acteristics. The mean age of the sample is 30 with a median
age of 27. The age range varies from 17 through 59. The
sample appears to be largely composed of older adults. The
majority, 62%, are male, 76% white, and 61% never married.
About half of them, 51%, had education up to the high school
level. A little more than half, 58%, had some vocational or
technical training and 94% had some work experience prior to
seeking help from the VRS.

The primary cause of disability was physiological
for 49% and psychogenic for the other 51%. The most numer-
ous group in the sample were alcoholics, 18%, followed by
orthopaedics, 15%. Only 29% had a secondary disability, of
which, once again about half were physiological and the
other half of psychogenic origin. Mean age of occurrence of
primary disability varied from 14 to 19 years, with the
exception of learning disorder which, in most cases, were
congenital.

About half of the sample lived with their parents or
owned a home and the other half rented; 43% lived in urban

areas followed by 33% in rural, and 24% in semi-urban areas.

132

TABLE 4.1.--Demographic Characteristics of the Sample

 

 

 

 

Variable N % X SD MD
Age 115 100 30 10 27
Sex

Male 71 62

Female 44 38

Race

White 87 76

Non-white 28 24
Marital status

Married'at least one time 45 39

Never married 70 61

Number of dependents
(excluding self)

 

None 80 70
One 14 12
Two 11 9
Three to eight 10 9
Education

1st to 8th grade 11 10
9th to 12th grade 59 51
1 to 2 years of college 32 28
3 to 4 years of college 10 9
Graduate study 3 2

Vocational, technical or

trade school attendance

prior to VRS
Attended 67 58
Did not attend 48 42

 

 

Worked any time before and

after disability prior to VRS

Worked 108 94
Not worked 7 6

 

 

Primary Disability
I. PhysiologicaIIQisorder 5
. B indness, Deafness,
Speech or Hearing
2. Orthopaedic, Amputation
3. Back injury
4. Cardiovascular

 

m
b
\D

 

...—l
bleUl
_,|—u|
hOU'IJ-S

133

TABLE 4.1.-- Continued

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable N % if so MD
Primary Disabilipy
I. PhysiologiCal Disorder
5. Metabolic, Blood
Malignant, Diabetic 7 6
6. Neurological, Epileptic,
Cerebral Palsy 11 10
II. Psychogenic Disorder ‘gg ‘51
Learning Disorder
7. Learning disabled,
Slow learner 3 3
8. Mental retardation 10 9
Mengal Illness
9. Psychosis 8 7
10. Neurosis, emotional 9 8
11. Personality and conduct
disorder 3 3
Substance Abuse
12. Alcoholism 21 18
13. Drug 5 4
Secondary Disabilipy
Present 33 29
Absent 82 71
Physiological disorder 17 52
Psychogenic disorder 16 48
Age of Primary Disability
I. Physiological §§_ 42 17 16 15
Congenital 19 17
Adventitious (3-52) 37 32
II. Psychogenic .§2 .21 14 11 15
Congenital 17 15
Adventitious (5-35) 42 37
Learning disorder 13 11 1 2 1
Mental illness 20 17 16 11 10
Substance abuse 26 23 19 8 18
Living Condition
Own/Parents' home 55 53
Rented 48 47
Not reported 12 -

134

TABLE 4.1.-—Continued

 

 

 

 

 

Variable N % X SD
Location
Rural 35 33
Semi-urban 25 24
Urban 46 43
Not reported 9 -
Source of Referral
Self and family 38 36
Educational institutions 19 18
Private agencies 13 12
Public agencies 35 33
Not reported 10 -
Emplgyment Status at Acceptance
Employed 7 6
Unemployed 108 94

 

All percentages may not total to 100% due to rounding.

135

A plurality of the clients, 37% were self-referred, followed
by 33% referred by public agencies. At the time of

acceptance for VR services only 7 clients were working.

Psychological Characteristics

 

Locus of Control

 

Two instruments were used to measure the locus of
control orientation of the clients at the verge of their
embarking on employment-seeking activity. The mean score on

Rotter's Locus of Control Scale and Modified Locus of

 

Control Scale were 9.38 and 8.99 with standard deviations
(SD) of 4.14 and 3.18 (Table 4.2). The mean scores on both
scales were not significantly different; however, the SD's
differed by almost one point.

The mean on Rotter's (1966) original normative
sample of college students was 8.15 with a SD of 3.88 for
males and 8.42 with a SD of 4.06 for females. Cellini and
Kantorowski (1982) reported a generalized trend towards
externality for college students. The mean score increased
to 10.87 with a SD of 4.02 for male college students and
11.70 with a SD of 3.71 for females.

The sample of the current study was not college lev-
el and no normative data exist for rehabilitation clients.
One would expect that the rehabilitation clients would score
more externally, thus having higher mean scores. However,
the sample mean was in the internal direction. This may be

due to their optimistic perception about the world of work.

136

TABLE 4.2.--Means, Standard Deviations, Medians and Ranges
of the Locus of Control Scalp (LOC) and the
Modified Locus of Control Scale (MODLOC)

 

 

Scale X‘ SD MD Minimun Maximum

Locus of Control (LOC) 9.38 4.14 9.1 0 21

Modified Locus of
Control (MODLOC) 8.99 3.18 8.69 4 16

 

137

Work Motivation

 

The Vocational Opinion Index (VOI) assesses the Job

 

Readiness Posture (JRP) of clients' attitudes, perceptions,
motivations, and its impact on the ability to obtain and
maintain a job. An individual's JRP profile is composed of
three psychological dimensions including 13 factors (Table
4.3). The VOI diagnoses whether a trainee is a ”worker” or
a ”non-worker." Ameliorative interventions are implemented
to address the non-worker JRP during training. In the
A1=Overall or L1-Overall factor, if a trainee fell into the
"-' or '+' group, it indicated that he possessed a non-
worker JRP and his position in other factors in Attractions
or Losses was not calculated. For example, for A1=Overa11,
there are 59 subjects diagnosed as non-worker and 56 sub-
jects as workers. Only these 56 subjects were used to
ascertain their position in other factors of Attractions.
VOI criteria for classification may be appropriate for
diagnostic purposes but are not suitable for the purpose of
the study as a criterion measure of employment status of the
client on the basis of their work motivation. In the sam-
ple, 87 clients had jobs. According to VOI criteria, this
would mean that these 87 clients did not meet the criteria
for the "workers” group. This suggests they had insuffi-
cient motivation to work when in fact they were employed.
Therefore, the criteria of VOI to classify subjects on the
factors were ignored and every one was given a chance to be

classified in all of the 13 factors. In Attractions and

138

TABLE 4.3.--The Vocational Opinion Index (VOI)-Three
Dimensions and 13 Factors for the Sample

 

 

 

 

 

n_u "OK" n+u
Attraction
A1=Overall 29 56 30
A2=Benefits to Children 31 73 11
A3-Benefits to Worker 26 62 27
A4=Better Life Style 21 61 33
A5=Independence 37 43 35
Losses
L1-Overall 29 61 25
L2=Personal Freedom 23 62 30
L3=Time to Care for and be with
Family 60 23 55
Barriers
B1=Medical 46 69
Bz=Child Care and Family 82 33
B3=New Situations and People 60 55
B4=Ability to Get and Hold a Job 67 48
85=Transportation 95 20

 

139

Losses, the sample appeared to be normally distributed. In
five Barrier factors, the sample's barriers were either

present or absent.

Work Histopy

 

The multidimensional aspects of work history before
and after disability were recorded in the areas of complexi-
ty of the task performed (e.g., involving with data, people,
things), number of months worked, wages received, and job
satisfaction. Similar data were also collected at follow-
up. The number of jobs held by the client before and after
disability, and at followbup appear in Table 4.4. A maximum
of the 5 most recent jobs was recorded before and after
disability for the analysis.

While follow-up cases evidenced close agreement
between the counselors' and clients' reports, discrepancies
occurred in many instances. For example, a counselor
reported that a client was unemployed, while in fact the
client was employed, and vice versa. The differences in
counselors' and clients' reports were resolved by computing
the best of both reports, designated as ”best” values
throughout the study.

Out of 115 clients, 64 did not work before disabil-
ity whereas 24 did not work after disability. More clients
worked after disability, and held about twice the number of
jobs than before disability. This group may have been com-

prised of younger clients who became disabled at an early

 

140

pm a poupomom uoz

 

u u n u u u o. __ o m . m
u 1 a u u u o op o m o
I 1 u u u n s. cm o. __ m
o op as m m m m. .N o. m_ N
he es on «m me on mm mm o. a. P
om mm .m em mm on .N om om «o o
a boom w noooaao a ouoHoncooo a sopaaoonao w moadpoonpo
pound ouOMom moon «0 .oz

 

msuzoaaom

 

 

mos~m> umom pousmaou can mucofiau .muoHomcsou may we pouuomom
no oouzoadom on can .sopaaoonoo amour .sopdnoonno ooouom opoe noon do oooEozna.¢.o mamas

141

age and for whom the question of working before disability
does not arise. Follow-up data suggested that 77 had one
job and 10 had two jobs. Overall 76% had jobs after VRS.

A comparison of mean work characteristics of tasks
involving data, people, things, number of months worked,
wage and job satisfaction appears in Table 4.5. All of the
means on the six major work characteristics appeared similar
at the three different time periods: before disability,
after disability, and follow-up. Involvement with data,
people, or things together indicated the level of complexity
at which the worker performed. For simplicity, these
functions were studied separately. Data, people, and things
represent levels of complexity: data 7, people 9, and things
8 respectively. On the average, it appears that the reha-
bilitation clients worked more with things and data, and
were least involved with peOple during the three measured

periods of their work history.

Outcome

Details of the outcome measure for seven categories
in increasing order of preferred outcome criteria are shown
in Table 4.6. A summary of the employment status at post
VRS is in Table 4.7. The first three categories were com-
bined to represent the Unemployed, and four through seven to
represent the Employed. At the end of the six month follow-
up, 87 clients (76%) were employed and 28 clients (24%) were

unemployed.

142

 

 

 

mo.o oo.m so.m mo.m mo.m ooaooooonoon non
no.4 sm.o os.¢ oo.o mo.m oo>aooou moo: spuoom
sm.m m~.m ms.m os.m~ mm.mm ooxnos nooooe mo nongoz
mm.v -.q oo.m mm.o os.q noonnouoonoooou noxooz
Go.» o_.s om.o mo.n so.s oaoooouoopoooou noxnoz
mv.o so.o «4.4 ms.o no.4 ooooaoonoooou uoxoos
ummm mucmwau muouomcsoo uwuu< cucumm mofiumfiumuomumno xu03
mmwzoaaom

 

 

mosam> umom pousmsoo pct Q5130Haom up
.xuflawommfio pound .>ufiafiommwn phenom mowumfipmuomumcu xpoz coo: mo cOmfiummEooll.m.e mange

143

 

 

1 1 1 RN 1 o ooouooon ooz

mo as as am mm em ooomofio_o~ noooom .s
N. a, a a m. m_ ooaxnoz .o
o. o, a. m_ o. m. oaoooe who owns on ooxnoz .m
m o P _ N s cohooEoouco oo .oosoHoEm .4
a. o_ om .m a, m. nroooe who onoH on goo: ooo ono .m
m e .p P v e mcwcfimpu uozuusm ppm pawoo .N
v m w m «F m. poumoumucfi no: .pomonEocD .p
w omom w mucofiau m mpoHomcsoo oeooupo

 

 

can mucowao .mponmcsou on»

mosHm> ummm pousmsou
an pouuomom mm mslzoaaom up mopsmmoz oeoou501l.m.v mam<9

 

 

 

m. mm mm _ P ooouodom ooz
mm mp mp hh hm mocmumwmmm ofiaopm .m
NP NP NP 1 1 moccumwmmm awaosm +
Samson + oEoocH ucopppu .v
m. m. ~. 1 1 Ransom + oeoooH ooonuoo .m
as m. a, mm am madame .N
mm om —N e m oeoocfi acouusu ..
umom wucwfifiu wpoHomcsoo mcfiumoe Hmupomwm uuomasm mo moopsom
mousoflaom

 

 

144

mosam> umom pousmeou can mucowau .muoHomcsou
mcu mo poupommm mm mslzoaaom um .mcwumoe um .Hmupomom on upommsm mo moopsomul.m.v mqm<a

 

 

1 1 1 pm 1 m poupomou uoz
am mm _m am mm on im+~+.c oosoHoEooo
on so oo .o no «a As+o+m+oc oosopoeu
w umom m mucofiau w muoHomcsou msumum ucosho~m5m

 

 

mos~m> umom pousqsou

pan mucowau .mponmcsou can an poupoaom mo mausoHHom um msumum ucoshoHQEm11.h.v mange

145

Another way to index appreciable outcome criteria
would be to look closely at their sources of support at pre
and post VRS as shown in Table 4.8. At referral, half of
the sample depended on support from their family and the
other half received social security and public assistance.
At follow-up, more clients were living solely on their
current income, or in combination with either family
support or public assistance to supplement their living. A
majority of the clients were living on their current income

with fewer on Public Assistance.

Examination of Hypotheses

 

Hypothesis 1

 

Ho: The clients with internal locus of control
will have the same work motivation as the clients with
external locus of control.

Two instruments were used to measure locus of con-
trol. One was Rotter's (1966) Internal-External Locus of

Control Scale (LOC) and the other the Modified Locus of

 

 

Control Scale (MODLOC). The scores on both scales are con-

 

tinuous. Work motivation was measured by the Vocational

 

gpinion Index (VOI) and consisted of three psychological

 

dimensions: Attraction, Losses, and Barriers. The subjects
were grouped into three categories, viz., "-', "OK”, '+" in
each of the 5 factors of Attractions and 3 factors of

Losses. The group with "-" and ”+" indicates that these

146

people either have too low or too high expectations for
their jobs and they are not as good workers as the "OK"
group. For the five Barrier factors, the subject's
barriers are either present or absent. This stringent
criterion would virtually categorize everyone to be a non-
worker in any one of the 13 VOI factors listed. Ration-
ally, individuals with "-', "+' Attractions and Losses for
work do not possess the same problem. A positive attrac-
tion may be better than having a perception of negative
attraction. Similarly, negative loss is better than having
a perception of positive loss. The literature on the VOI
suggests that often "-' Attractions and '+" Losses appear
together on an individual's profile. Therefore, for the
analysis, "+" Attractions were combined with "OK” worker
group, and "-” Attractions with the non-worker group.
Similarly, "-" Losses were combined with "OK" worker group,
and "+" Losses with the non-worker group.

To test the hypothesis with the VOI as a categor-
ical dependent variable, the LOC and MODLOC was categorized
in three groups by applying the cutoff score of :1 standard
deviation from the mean. The clients who fell below one
standard deviation from the mean are termed as internal,
clients within :1 standard deviation are average, and those
above one standard deviation are external. The appropriate
test statistic for two categorical variables is a Chi-

square to determine whether a systematic relationship

147

exists between locus of control and work motivation. The

Chi-square formula is:

._ . 2
X2=2101EEJ>
i

where Oi is the observed frequency, and Bi is the ex-

pected frequency. The greater the discrepancies between the
expected and observed frequencies, the larger the Chi-square
becomes. The null hypothesis of no relationship or indepen-
dence may be rejected if the Chi-square value is higher than
the value expected by chance. The degrees of freedom for a
Chi-square test are: (1 - no. of rows)(1 - no. of columns),
i.e., (3 - 1)(2 - 1)=2. The null hypotheses were tested at
the .05 level of significance throughout the study. With 2
degrees of freedom a Chi-square of 5.99 is required at the
.05 level for a two-tailed test or at the .025 level for a
one-tailed test to reject the null hypothesis. Tables 4.9
and 4.10 illustrate the chi-square values, degrees of
freedom, and level of significance for the relationships of
LOC and MODLOC with each of the 13 factors of VOI.

There appears to be a significant relationship or
dependence among 3 different factors of VOI with the LOC and
MODLOC respectively. The other 10 factors of the VOI
appeared to be independent from LOC and MODLOC. Factors of
VOI that are related to the LOC are AzaBenefits to Child-
ren, L2-Personal Freedom, and B3=New Situation and
People; those related to MODLOC are A5=Independence, L1=
Overall Losses, and L2=Persona1 Freedom. The factor

L2=Personal Freedom was identified to be significant in

148

TABLE 4.9.--Chi-square Values Between the Locus of Control
(LOC) and the Vocational Opinion Index (VOI)

 

 

 

 

 

 

Contingency
x2 df p Coefficient

Attractions

A1=Overall 5.33 2 .06

A2-Benefits to children 6.26 2 .04* .23
A3-Benefits to worker 1.53 2 .46

A4-Better life style 1.74 2 .41
A5-Independence 2.61 2 .27

Losses

L1=Overall 0.71 2 .70

L2=Personal freedom 6.07 2 .04* .23
L3=Time for family 0.37 2 .82

Barriers

B1=Medical 1.02 2 .60

B2=Child care and family 1.93 2 .37

B3=New situations and

people 6.11 2 .04* .23
B4=Ability to get and
hold a job 4.50 2 .10

BsaTransportation 0.16 2 1.92

 

149

TABLE 4.10.--Chi-square Values Between the Modified Locus
_of Control (MODLOC) and the Vocational Qpinion

 

Index (VOI)

 

 

 

 

 

 

2 Contingency
x df p Coeff1c1ent

Attractions

A1=Overall 1.08 2 .58

Aszenefits to children 0.21 2 .90

A3=Benefits to worker 0.50 2 .78

A4=Better life style 0.47 2 .79
A5=Independence 7.77 2 .02* .25
Losses

L1=Overall 7.39 2 .02* .25
L2=Personal freedom 10.98 2 .004* .30
L3=Time for family 3.50 2 .17

Barriers

B1=Medical 2.34 2 .30

B2=Child care and family 1.55 2 .46

B3=New situations and

people 0.51 2 .77
B4=Ability to get and
hold a job 2.16 2 .34

85=Transportation 1.24 2 1.54

 

150

both LOC and MODLOC. It yielded the highest Chi-square
value of 10.98 at .004 level with MODLOC. The Contingency
Coefficient, a measure of association, provides an estimate
of the magnitude of the relationship between the variables
in a Chi-square table. The Contingency Coefficients for the
significant factors varied from .23 to .30, which is a

moderate relationship.

Hypothesis 2

 

Ho: The clients with an internal locus of control
will neither be employed nor obtain employment earlier than
the clients with an external locus of control.

The number of jobs obtained by the clients and the
number of clients who obtained employment at follow-up are
reported in Tables 4.4 and 4.7. The Chi-square statistic
was used to determine whether a systematic relationship
exists between locus of control and obtaining employment
post rehabilitation services. Table 4.11 illustrates the
Chi-square values of the three groups of LOC and MODLOC as
the independent variable compared with the dichotomous
categories of employed and unemployed as the dependent
variable. It appears that obtaining employment in post
rehabilitation services is independent of locus of control
orientation. In comparison with LOC, MODLOC yielded higher
Chi-square values. The first part of the null hypothesis
cannot be rejected. The clients' locus of control orienta-
tion as measured by the LOC and MODLOC is independent of

their employment status at post VRS.

151

 

 

 

TABLE 4.11.--Chi-square Values Between the LOC, the MODLOC
and Employment Status After VRS.
X2 df p
LOC
Employment (Counselors) 1.21 2 .54
Employment (Clients) 0.77 2 .67
Employment (Best) 1.51 2 .47
MODLOC
Employment (Counselors) 2.23 2 .32
Employment (Clients) 2.59 2 .27
Employment (Best) 4.56 2 .10

 

TABLE 4.12.--Pearson Product Moment Correlation Coeffi-
cients of the LOC and the MODLOC with Number

of Months Worked.

 

 

 

r N p
LOC
Mean Number of Months Worked (Counselors) -.26 S3 .02*
Mean Number of Months Worked (Clients) -.12 58 .19
Mean Number of Months Worked (Best) -.14 76 .11
MODLOC
Mean Number of Months Worked (Counselors) -.32 53 .009*
Mean Number of Months WOrked (Clients) -.28 58 .01*
Mean Number of Months Worked (Best) -.23 76 .02*

 

152

The second part of the hypothesis investigates
whether or not internals obtain employment earlier than
externals. One who has been working longer during the study
period of six months obtained employment earlier. Tables
4.4 and 4.7 show that 77 people had one job and 10 people
had two jobs in the last six months. To equate the two
groups, the mean number of months worked was computed. The
rationale is to retain consistency with the mean number of
months worked before and after disability. Mean number of
months worked is a continuous dependent variable. There-
fore, LOC and MODLOC were used here as continuous indepen-
dent variables. Pearson Product Moment Correlation Coeffi-
cients of these continuous variables appear in Table 4.12.

It was hypothesized that there would be a negative
relationship between the LOC and MODLOC, and number of
months worked, as the internals have lower scores in LOC and
MODLOC and worked more months than the externals. The
result of the test hypothesis indicates that the coeffi-
cients are in the predicted direction. The magnitude of the
correlations between the variables are of moderate size.

The MODLOC is a preferred measure to LOC as it yielded three
significant correlations in comparison to one with LOC; the
magnitudes are also relatively higher than LOC. A stronger
relationship could have been obtained if the followbup
period had not been restricted to six months. The second

part of the null hypothesis is rejected.

153

Discriminant Analysis

Hypotheses 3,4,5 and 6 examined whether or not the
demographic, work motivation, and work history predictor 1
variables have any relationship to the dependent variable,
employment status. Furthermore, the discriminant analysis
explored how accurately the relationship can classify and
determine the probabilities of group membership (employed or
unemployed) and the similarity of cases within each group.
Discriminant analysis (DA) is a powerful statistical tech-
nique to study the differences between two or more groups of
objects with respect to multiple variables simultaneously.

A brief discussion will follow on the elements of DA and
then each hypothesis will be discussed based on the DA
technique.

The objective of discriminant analysis is to weigh
and linearly combine the discriminating variables so that
the separation of the groups is maximized. The mathematical

equation obtained is called a discriminant function and it

 

is of the form:
Di = dilzl + dizzz + ...... + dipr Eq. 1

where D1 is the score on discriminant function i, d's are
empirical weighting coefficients, and 2's are standardized
values of the discriminating variables (Nie, Hull, Jenkins,
Steinbrenner, & Bent, 1975).

The maximum number of unique discriminant functions

that can be derived is equal to the number of groups minus

154

one or equal to the number of discriminating variables,
whichever is smaller (Klecka, 1980). This study is
restricted to two groups of rehabilitation clients, employed
or unemployed. One would expect to have only one
discriminant function. The derived function would be useful
in interpreting the data and classifying the subjects.

Discriminant Function: Discriminant analysis tries
to seek a set of weights, v, by which to weight the scores
of each individual so that the ratio of B (between-groups
Sum Squares and Cross Products, SSCP) to W (pooled within-
groups SSCP) is maximized, thereby maximizing the

discrimination between the groups.

 

A=v'Bv Eq. 2
v'Wv
where v' and v are row and column vectors of weights, A is
referred to as the discriminant criterion. The 1 can be
derived using the following determinantal equation:

w-ls - AI = 0 Eq. 3

where W"1 is the inverse of W, I is an identity matrix,

and A is the largest eigenvalue, or characteristic root of

 

the matrix whose determinant is equal to zero. If the
determinant of the matrix is zero, then it establishes the
fact that the matrix contains at least one linear dependency
and is referred to as being a singular matrix (Pedhazur,
1982). One eigenvalue is expected for the two groups in the

study. The eigenvalue is an index for judging the

155

importance of the discriminant function. The solutions of
these homogenous equations result in two types of coeffi-
cients: standardized coefficients and raw or unstandardized
coefficients.

Standardized Coefficients: The relative magnitude

 

of the standardized discriminant function coefficients can
be used as an index of the relative contribution or impor-
tance of the dependent variable to the discrimination be—
tween the groups (Employed versus Unemployed). The sign
associated with the coefficients merely indicates whether
the variables are making positive or negative contributions.
These coefficients are equivalent to the dij's in Eq. 1.
The standardized value of each discriminating variable

(2 score) is multiplied by its standardized discriminant
coefficient and the resulting products are then added
together to obtain the discriminant score.

Raw or Unstandardized Coefficients are not useful in

 

comparing the relative importance of the variables since the
original variables are not standardized. However,
unstandardized coefficients are used in deriving the
discriminant function:

Di 3 B1X1 + BzXz + ...... + Bpo + C Eq. 4
where C is the constant and B's are the raw coefficients,
X's are the values of the original discriminating variables.
This function can be used to calculate the discriminant
scores by adding the products of the raw coefficients with

the raw values of the corresponding variables and together

156

with the constant C (to adjust for the grand mean): the
score obtained is equivalent to the standardized one.
Centroid: The discriminant function is used to
calculate the discriminant score of each subject in the
groups under study. The mean discriminant score for a

particular group is referred to as the Group Centroid.

 

There will be two group centroids in the study, one for the
Employed group and the other for the Unemployed group. The
larger the separation between the groups, the better the
classification, thus reducing the number of misclassifi-
cations.

Measures of Association: Wilks' A (lambda) is used
to measure the association between the independent and
dependent variables in the multivariate analysis. The
lambda is a ratio of the determinant of two matrices:

=1w1 Eq. 5

A le

 

!w| =Pooled within-groups sscp and |T| =Total sscp. The A
indicates the proportion of variance of the dependent
variable not accounted for by the independent variables,
i.e., the proportion of error variance, which may vary from
0 to 1. When A=0, it indicates that there is a perfect
association between the independent and dependent variables.
In other words, when A approaches 0, the group centroids are
greatly separated and higher discrimination is possible.
When A21, it indicates that there is no association between

the independent and dependent variables; the proportion of

157

error variance is maximal. The independent variables do not
account for any portion of the variance of the dependent
variable. In other words, when the group centroids are
identical, there is no difference between the groups.

1-A=n2 is a generalization of the correlation ratio in

both univariate analysis and multivariate analysis (R3).

The interrelationships between Wilks' Lambda, Correlation
ratio (equivalently Canonical Correlation) and the

Eigenvalue can be expressed as (Pedhazur, 1982):

_ 22 _ A
1 A-Rc -17—A quG

Test of Significance: Wilks' Lambda is converted to
a chi-square to test the statistical significance of the

derived discriminant function

2
7‘="lln----'>(q+g)-1111n11k Eq.7

which is distributed as a x2 with (q-k)(g-k—l) degrees of
freedom, where n=the total sum of weights, q=number of
discriminating variables, gsnumber of groups, ln=natural
logarithm, and Ak=measure of association for the cases in
group k.

In summary, there are a few indices that can be used
to judge the importance of the derived discriminant function
in the study.

a. Eigenvalue provides a measure of the relative
importance of each discriminant function (if there are more

than one).

158

b. Canonical Correlation is a measure of association
which indicates the function's ability to discriminate among
the groups.

c. The square of the canonical correlation indicates
the proportion of the variance in the discriminant function
explained by the groups.

d. Wilks' Lambda represents the proportion of
variance of the dependent variable not accounted for by the
set of independent variables: it is the proportion of the
error variance.

e. Chi-square provides the statistical significance
of the derived discriminant function.

Classification of opjects: The discriminant
function derived in this manner can be used to classify
each of the rehabilitation clients into the employed or
unemployed group based upon the probability of group;
membership as determined by each client's‘values on the set
of discriminating variables. Classifications are performed
by deriving classification functions for each group;
multiplying the classification coefficients with the raw

variable values, and adding them together with a constant:

Ci = Ci1V1 + C12V2 + ...... + Cipr + C10 Eq. 8

where Ciaclassification score for group i, cijz classi-
fication coefficients with Cio being the constant, and
V'saraw score on the discriminating variables. Under the

assumption of a multivariate normal distribution the

159

classification scores are converted into probabilities of
group membership (Nie et al., 1975).

Stepwise Procedure: In this exploratory predictive

 

study, data were collected on variables which appear to be
good discriminators on the basis of the literature reviewed.
One or more of these variables individually may be a good
discriminator, but when combined with other variables,

may share the same discriminating information. The Stepwise
procedure selects the best discriminating variables among
the set of variables in the analysis whereas the Direct
method allows all the variables to enter into the analysis
regardless of their discriminating power. The stepwise
procedure sequentially selects ”the best" discriminator at
each step of the analysis by choosing the variable which has
the highest F ratio among those variables not in the model.
A variable entered at the beginning of the analysis may be
eliminated at a later step or vice versa, if in combination
with other variables it loses or gains discriminating power.
A reduced set of variables may predict as well as the full
set. In examining hypotheses 3,4,5 and 6, initially the

direct method and then the stepwise procedure was used.

Hypothesis 3

 

Ho: There will be no relationship between the
clients' demographic characteristics and employment status

at post VRS.

160

A list of clients' demographic characteristics and
their descriptive statistics appear in Table 4.1. To study
the difference between the employed and unemployed at post
VRS on a set of 23 demographic independent predictor vari-
ables, a Discriminant Analysis (DA) was performed. The
results of the analysis are in Table 4.13. The initial
analysis was performed by the direct method, allowing the 23
variables to enter into the analysis regardless of their
discriminating power. The tolerance level for variable
inclusion in DA eliminated 5 variables. The discriminant
function derived was based on the remaining 18 variables for
the 30 subjects who had a complete set of data on those
variables. After the elimination of these 5 variables, the
derived function was able to classify those 30 subjects with
96.67% accuracy based on their predicted group membership as
employed or unemployed. The function yielded a canonical
correlation (RC) of .80. The square of the canonical
correlation indicated that 64% of the variation between the
groups was explained by the function. The chi-square test
for Wilks' Lambda was not significant. The magnitude of the
eigenvalue indicates the weakness of the discriminant
function, and as a result the group centroids are not widely
separated. The null hypothesis cannot be rejected based on
a limited sample and with a limited set of data.

The magnitude of the standardized coefficients
indicated the relative importance of the variables in the

function. Number of dependents, marital status and

161

TABLE 4.13.-—Discriminant Analysis of Demographics by
Direct Method

 

 

WILKS' LAMBDA CHI-SQUARE DF SIGNIFICANCE
.36 19.16 18 .38
EIGENVALUE CANONICAL CORRELATION

1.74 .80

CANONICAL DISCRIMINANT FUNCTION COEFFICIENTS
STANDARDIZED UNSTANDARDIZED

 

 

 

 

Age .40 .04
Sex -.42 -.85
Race .63 1.52
Marital level 1.27 2.56
Dependents 1.31 1.56
Education .97 .92
Technical training .14 .28
Primary disability

Physiological .20 .40
Age physiological -.18 f.57
Age Psychogenic -.19 -.09
Learning disorder -.52 -1.48
Age learning disorder -.71 -2.28
Mental illness -.08 -.44
Secondary disability

Physiological -.70 -1.63
Living Conditions -.27 -.52
Geographical location -.71 -.82
Source of referral .73 -.53
Sources of support at referral .25 -.54

Constant -5.58

GROUP CENTROIDS Employed=-.70 Unemployed=2.31

 

CLASSIFICATION RESULTS

 

PREDICTED GROUP MEMBERSHIP

 

 

 

 

ACTUAL GROUP NO. OF CASES EMPLOYED UNEMPLOYED
Employed 23 22 1
95.7 4.3
Unemployed 7 0 7
0 100.0

PERCENT OF GROUPED CASES CORRECTLY CLASSIFIED=96.67

 

162

education made the greatest contribution in discriminating
between the groups. Other variables which proved to be
useful included sources of referral, geographical location,
age at the primary disability learning disorder and presence
of physiological secondary disability.

The stepwise method was used on the same variables
to allow for the selection of independent predictor vari-
ables on the basis of their discriminating power.' Wilks'
stepwise procedure was used for selecting variables which
yield the smallest Wilks' Lambda or the largest overall
multivariate F. The results of the stepwise selection are
shown in Table 4.14. Four variables were selected which had
some of the highest standardized coefficients in the model
created by the direct method: race, number of dependents,
secondary disability physiological, and sources of support
at referral. The discriminant function composed of these 4
variables was based on 30 clients. The canonical correla-
tion was .68. Thus the proportion of variance between the
groups accounted for by the set of 4 variables in the dis-
criminant function was 46%. The chi-square test of Lambda
was significant at the .003 level. Hence, the null hypothe-
sis is rejected and it may be concluded that there is a
relationship between the demographic characteristics of the
clients in differentiating the employed from the unemployed
at post VRS.

During the classification phase, the function

classified 33 subjects with 85% accuracy. The relative

163

TABLE 4.14.--Stepwise Discriminant Analysis of Demographics

 

 

WILKS' LAMBDA CHI-SQUARE DF SIGNIFICANCE
.54 15.87 4 .003
EIGENVALUE CANONICAL CORRELATION

.84 .58

CANONICAL DISCRIMINANT FUNCTION COEFFICIENTS
STANDARDIZED UNSTANDARDIZED

 

Race .34 .81
Dependents .45 .54
Secondaryjdisability

physiological —.65 -1.50
Sources of support at referral .43 .90

Constant-1.35

GROUP CENTROIDS Employed=-.49 Unemployed=1.61

 

CLASSIFICATION RESULTS
PREDICTED GROUP MEMBERSHIP

 

 

 

 

ACTUAL GROUP NO. OF CASES EMPLOYED UNEMPLOYED
Employed 24 20 4
83.3 16.7
Unemployed 9 1 8
11.1 88.9

PERCENT OF GROUPED CASES CORRECTLY CLASSIFIED=84.85

 

164

importance of the discriminators was somewhat different from
that obtained earlier, as this was a completely different
linear combination of the predictors: having a physiologi-
cal secondary disability appears to be the most important in
discriminating between the groups, followed by number of
dependents, sources of support at referral, and race of the
clients.

Although the first function was predicting with 97%
accuracy, it was not significant. The second function has
14 fewer variables than the first one. Generally, one
chooses the function with the lowest number of variables for
prediction. For two groups, one may by chance predict with
50% accuracy. The second function can predict 35% more
accurately than mere chance for the set of variables and
given the limited number of subjects who had a complete set
of data. No systematic difference was found among the
clients from the three states, as both the DA and cluster

analysis rejected the "state" variables.

Hypothesis 4

 

H0: There will be no relationship between the
clients' work motivation and employment status in post VRS.

To study the difference between the employed and
unemployed at post VRS on a set of 13 factors of VOI which
provides client's JRP as a measure of work motivation, a
discriminant analysis was performed. The 13 factors were
treated as independent predictor variables and employment

status as the dependent variable. Initially, the direct

165

method was used, and the results are presented in Table
4.15. Despite the absence of any missing data among the 115
cases, the discriminant function derived using 13 factors
was not significant. The canonical correlation was .28;
thus, only 8% of the variation between the groups was
explained by the function. Wilks' Lambda was very high,
.91, approaching the maximum possible remaining proportion
of error variance and therefore no relationship existed
between the independent and dependent variables. The group
centroids were almost identical and the separation between
the groups was not significant.

Next, Wilks' stepwise procedure was used and the
results are shown in Table 4.16. The stepwise procedure
selected 3 factors out of 13. The canonical correlation was
.23, or only 5% of the variation between the groups was
explained by the function. The proportion of error variance
was high. The level of chi-square for Lambda was noticeably
higher than the direct method, but still non-significant.

Both functions predicted the group membership with
60% accuracy, which was little better than chance. The
function was not significant based on full set of data for
the entire sample. Thus the VOI may have good diagnostic
ability but definitely did not have predictive ability in
this study. The null hypothesis cannot be rejected. And it
is concluded that the clients' work motivation as measured
by the VOI is independent of their employment status at post

VRS. An alternate grouping of clients on the VOI scales was

166

TABLE 4.15.-~Discriminant Analysis of 13 Factors of the VOI
by Direct Method

 

 

WILKS' LAMBDA CHI-SQUARE DF SIGNIFICANCE

.91 9.06 13 .76
EIGENVALUE CANONICAL CORRELATION
.08 .28

CANONICAL DISCRIMINANT FUNCTION COEFFICIENTS
STANDARDIZED UNSTANDARDIZED

 

 

A1=Overall .52 1.72
A2=Benefits to children .80 1.38
A3cBenefits to worker -.84 -1.24
A4-Better life style -.17 -.25
A5-Independence -.28 -.36
L1-Overall -.11 -.17'
Lz-Personal freedom -.71 1.04
L3-Time to care for and be

with family -.34 -.49
B1=Medical .03 .07
82=Child care and family -.49 -1.09
B3=New situations and people .14 .27
B4=Ability to get and hold a job .25 .51
BS=Transportation .10 .28

Constant .02

GROUP CENTROIDS Employed=.16 Unemployed=-.52

 

CLASSIFICATION RESULTS

 

PREDICTED GROUP MEMBERSHIP

 

 

 

 

ACTUAL GROUP NO. OF CASES EMPLOYED UNEMPLOYED
Employed 87 52 35
59.8 40.2
Unemployed 28 11 17
39.3 60.7

PERCENT OF GROUPED CASES CORRECTLY CLASSIFIED=60.00

 

167

TABLE 4.16.--Stepwise Discriminant Analysis of 13 Factors

 

 

of the VOI
WILKS' LAMBDA CHI-SQUARE DF SIGNIFICANCE
.94 6.34 3 .09
EIGENVALUE CANONICAL CORRELATION
.05 .23

CANONICAL DISCRIMINANT FUNCTION COEFFICIENTS
STANDARDIZED UNSTANDARDIZED

A1=Overall 1.00 1.72
Az-Benefits to children -.85 -1.25
A3-Benefits to worker .54 .80

Constant .26

GROUP CENTROIDS Employed=.13 Unemployed=-.42

 

CLASSIFICATION RESULTS

 

PREDICTED GROUP MEMBERSHIP

 

 

 

ACTUAL GROUP NO. OF CASES EMPLOYED UNEMPLOYED
Employed 87 51 36
58.6 41.4
Unemployed 28 9 19
32.1 67.9

PERCENT OF GROUPED CASES CORRECTLY CLASSIFIED=60.87

 

168

tested but did not yield any significance. It would appear
that the method of grouping did not influence the VOI as a

significant measure of work motivation.

Hypothesis 5

 

Ho: There will be no relationship between the
clients' prior work history characteristics and employment
status at post VRS.

To study the difference between the employed and
unemployed at post VRS on a set of 24 predictor variables
consisting of 12 before disability (hereafter referred to as
”before') and 12 after disability (hereafter referred to as
"after”) work characteristics, a Discriminant Analysis was
performed.

'Among the number of jobs held by the client either
before or after, a maximum of the 5 most recent jobs and
their multidimensional work characteristics were recorded
during data collection. These multidimensional aspects
were: number of jobs held; hierarchy of data, people, and
things; number of months worked; full-time or part-time;
training requirement; certificate or license; wages re-
ceived; job satisfaction; and reasons for leaving. The mean
scores or percentages were calculated for each character-
istic for both before and after.

The direct method yielded a canonical correlation
that was high and a value of Lambda that was low, indicating

that 90% of the variation between the groups was explained

169

by the function; the predicted group membership for the 23
employed and 4 unemployed was classified with 100% accuracy.
Despite the favorable statistics, the discriminant function
was non-significant, which may be an artifact of the data
resulting from either a small sample size, where the number
of variables was almost equal to the size of the sample. It
may also be that one or more variables which were highly
related contributed the same information in the linear
combination, cancelling each other's effect.

The results of the Wilks' stepwise procedure are
shown in Table 4.17. The stepwise Procedure selected the 7
most important variables for the discriminant function,
which was significant at .004 level. ‘The canonical correla-
tion was .78. Its square, or one minus Lambda, indicated
that 62% of the variation between the groups was explained
by the discriminant function. In other words, 62% of the
variation in group membership was accounted for by the 7
selected variables of the function.

The important variables that differentiated between
the groups were: mean worker function data (after) and the
mean number of months worked (before), followed by the per-
centage of full-time jobs (before), and mean worker function
people (after). Two before and five after disability work
characteristics were the components of the discriminant
function. The after disability work characteristics contri-
buted the most. An explanation would be that recency of

work activity and behavior prior to VRS leads to success in

170

TABLE 4.17.--Stepwise Discriminant Analysis of Before and
After Disability Work Characteristics

 

 

WILKS' LAMBDA CHI-SQUARE DF SIGNIFICANCE
.38 20.47 7 .004
EIGENVALUE CANONICAL CORRELATION

1.59 .78

CANONICAL DISCRIMINANT FUNCTION COEFFICIENTS

STANDARDIZED UNSTANDARDIZED

 

Before Disabilipy

 

 

Mean length 1.01 .31
Percent of full-time -.87 -.26
After Disabilipy

Number of jobs -.52 -.40
Mean worker function-data 1.36 .86
Mean worker function-people -.76 -.79
Percent of full-time .66 .02
Mean job satisfaction -.38 -.37

Constant 3.66
GROUP CENTROIDS Employed=-.50 Unemployed=2.91

CLASSIFICATION RESULTS

 

PREDICTED GROUP MEMBERSHIP

 

 

 

ACTUAL GROUP NO. OF CASES EMPLOYED UNEMPLOYED
Employed 29 27 2
93.1 6.9
Unemployed 5 0 5
0 100.0

PERCENT OF GROUPED CASES CORRECTLY CLASSIFIED=94.12

 

171

in obtaining employment at post VRS. Also the impact of the
before disability work characteristics, by the same token,
was no less important. The longer subjects worked and the
more full-time jobs they had, that is the depth of work
involvement, were the second and third most important dis-
criminators in the function.

The discriminant function derived was based on 27
clients who had a complete set of data for all 24 variables.
As variables were eliminated during the analysis in the
Stepwise procedure, more clients became eligible for classi-
fication. The classification function predicted group mem-
bership with 94% accuracy for the 34 clients who had com-
plete data on the reduced set of variables. Of the 29 who
were predicted to be employed, 27 (93%) were classified
accurately; 2 (7%) were misclassified. These two clients
were predicted to be employed but were in fact unemployed.
The 5 (100%) Clients predicted to be unemployed were unem-
ployed. The prediction rate obviously is very optimistic,
as it was based on only 30% of the sample and has not been
cross validated. The null hypothesis is rejected and it is
concluded that there is a positive relationship between the
clients' prior work history characteristics and employment
status at post VRS.

The above analysis was based on a limited sample who
have worked before and after disability; the discriminant
function used 27 clients and the classification function

grouped 34 clients. WOuld this discriminant function be

172

able to predict the employment status of those clients who
either worked before or after but not both? Accordingly,
further analyses were performed separately on 12 before work
variables and 12 after work variables.

For the 12 before variables, both the direct and
stepwise procedures did not produce any significant discrim-
inant function. The function derived by the direct method
accounted for only 12%, and the stepwise function comprising
2 variables accounted for 9% of the variation between the
groups. The former analyzed and classified 46 clients while
the latter analyzed 46 clients and classified 50 clients;
both predicted with 60% accuracy.

For the 12 after variables, the direct method
function accounted for 19% of the variation between the
groups using 84 clients in both analysis and classification,
and predicting with 71% accuracy. However, the function was
not significant. The stepwise function accounted for 16% of
the variation between the groups using seven selected vari-
ables, only 3% less than the direct method. It used 84
clients in the analysis and classified 86 clients with 71%
accuracy; the function was significant at the .05 level.

The results are shown in Table 4.18.

In summary, while the before disability work vari-
ables could not predict employment status significantly, the
after variables could, with the combination of variables
(before and after) accounting for the best overall predic-

tion. The 7 combined variables accounted for 62% of the

173

TABLE 4.18.-—Stepwise Discriminant Analysis of After
Disability Work Characteristics

 

 

WILKS' LAMBDA CHI-SQUARE DF SIGNIFICANCE
.84 13.68 7 .05
EIGENVALUE CANONICAL CORRELATION
.19 .40

CANONICAL DISCRIMINANT FUNCTION COEFFICIENTS
STANDARDIZED UNSTANDARDIZED

 

 

Mean worker function-data -.74 -.53
Mean worker function-people .54 .49
Mean worker function-things .34 .18
Percentages of training .53 .12
Mean wage .46 .19
Percentages of jobs left

involuntarily .52 .12
Voluntarily .78 .19

Constant -4.44

GROUP CENTROIDS Employed=.22 Unemployed=-.82

 

CLASSIFICATION RESULTS

 

PREDICTED GROUP MEMBERSHIP

 

 

 

 

ACTUAL GROUP NO. OF CASES EMPLOYED UNEMPLOYED
Employed 67 48 19
71.6 28.4
Unemployed 19 6 13
31.6 68.4

PERCENT OF GROUPED CASES CORRECTLY CLASSIFIED=70.93

 

174

variance whereas the 7 after variables accounted for only
16%. The former predicted 94% for 34 clients versus the
latter's 71% for 86 clients. As more of the sample was
used, more misclassifications occurred. Rehabilitation
counselors or practitioners in the field may choose either
function based on what types of work characteristics or

attributes a client possesses.

Hypothesis 6

Ho: A linear combination of predictor variables,
including demographics, locus of control, work motivation,
and work history characteristics will not be able to
differentiate the employed from the unemployed clients at
post VRS.

The earlier hypotheses tested the differences be-
tween the employed and the unemployed at post VRS on dif-
ferent domains of client characteristics. For this hypothe-
sis, discriminant analysis was performed on 5 different com-
binations of variable domains to find the best prediction
equation, that is, the discriminant function with a set of
optimal predictors which is meaningful and statistically
significant. The results of the 5 stages of linear combina-
tions and their summary statistics are shown in Table 4.19.

In Stage I, a set consisting of 43 (V1) variables
representing demographic, locus of control, work motivation,
before and after work variables was analyzed. As per the

rule, in Direct method, the number of variables included in

175

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178

the analysis is two less than the number of valid cases.
There were only 25 (N1) clients who had a complete set of
data; thus, the analysis could use only 23 (V2) variables.
The other 20 variables could not be used as they failed the
tolerance test. The classification function used 102 cases
and predicted group membership with 66.67% accuracy. The
stepwise procedure analyzed 25 (N1) clients and Classified
only 30 (N2) clients with 96.67% accuracy. Both discrimi-
nant functions were significant, the proportion of variance
accounted for by both functions was high, the Lambdas were
low, and the centroids were wide apart. However, the first
function seems not to predict as well as the second one.

In Stage II, a set of 30 variables was used, drop-
ping the 13 factors of the VOI used in the previous analy-
sis. In the direct method 7 variables failed the tolerance
test. The discriminant function used 23 variables, based on
25 clients, and classified 29 clients with 93% accuracy.
The stepwise procedure selected 19 variables, 1 more than
the previous stepwise procedure and predicted with 6% less
accuracy for almost the same number of clients; however, the
analysis began with 13 fewer variables.

The Stage III analysis was performed on a set of 41
variables, dropping locus of control. The direct and step-
wise procedures yielded significant results. The Stage IV
analysis was performed on a set of 28 variables after drop—
ping both the locus of control and the VOI. Finally, Stage

V analysis was done on a set of 36 variables after dropping

179

the before work variables; it showed a significant gain in
sample size and was non-significant in both direct and
stepwise procedures. The stepwise procedure in Stages IV
and V selected only 3 and 6 variables respectively which
were meaningless for all practical purposes.

A comparison between Stages I, II, and III revealed
that the presence or absence of the VOI or the Locus of Con-
trol Scales did not make much difference in the discriminant
function. When both the locus of control scales and the VOI
were dropped from the model in Stage IV only 3 variables
were selected in the stepwise procedure, which were redun-
dant. Also in Stage V, the absence of before work variables
jeopardized the usefulness of the function. The Stage II
analysis appeared to represent a moderate number of appro-
priate variables from all the given domains of predictors
after Stepwise selection. Hence, the Stage II analysis will
be discussed in detail: the standardized and unstandardized
coefficients obtained by the direct and stepwise procedures
are shown in Table 4.20 and Table 4.21, respectively.

The discriminant function derived by both procedures
was significant, with canonical correlations of .97 and
.99. Therefore the proportions of variance accounted for
between the groups in the functions were 96% and 99% respec-
tively. The stepwise procedure selected 19 variables repre-
senting 8 demographic, 1 locus of control, 5 before work and
5 after work variables. The group centroids were far apart

and the eigenvalue was high. The classification function

TABLE 4.20—-Discriminant Function Coefficients for the Stage

180

II Linear Combination by Direct Method

 

 

 

 

 

 

 

yariables Standardized Unstandardized

Demographic

Age -5.36 -.59
Sex 2.41 5.20
Race -2.13 -4.96
Marital -3.28 -6.48
Number of dependents -2.50 -1.41
Education -0.74 ' -.73
Technical training -2.59 -5.10
Primary disability
physiological -10.27 -19.74
mental illness -6.68 -17.80
Living conditions 5.07 10.45
Geographic location -6.68 -7.56
Sources of referral 1.97 -1.50
Sources of support at referral -1.50 -3.13

Locus of Control

LOC 3.53 1.06.
MODLOC 3.13 .91

Before Disability

Number of jobs 1.90 1.76
Mean worker function-data -8.38 -5”56
Mean worker function-people 2.62 2.58
Mean worker function-things 7.09 3.77
Mean number of months worked 11.55 .39
Mean wages received 3.45 2.05
Mean job satisfaction -1.87 -1.81

After Disability

Mean worker function-data 11.16 6.95

Constant 28.20

 

181

TABLE 4.21--Stepwise Discriminant Function Coefficients for
Stage II Linear Combination

 

 

 

 

 

 

 

yariables Standardized Unstandardized
Demographic
Age 8.43 .92
Sex 5.79 12.48
Number of dependents -3.96 -2.23
Technical training -2.25 -4.44
Primary disability
Mental illness 8.71 23.22
Substance abuse -1.94 -4.05
Geographic location 10.72 12.12
Sources of support at referral -2.90 -6.09
Locus of Control
LOC -12.84 -3.85
Before Disability
Mean worker function-data 14.11 9.36
Mean worker function-people -3.89 -3.84
Mean worker function-things -13.75 -7.32
Mean number of months worked -31.52 -1.01
Mean wages received 22.19 13.16
After Disability
Number of jobs 8.72 6.62
Mean worker function-data -28.30 -17.65
Mean worker function-people 9.59 9.77
Mean worker function-things 8.82 4.84
Mean number of months worked 3.36 .05

Constant -77.37

 

182

predicted accurately for the unemployed, but for the employ-
ed group 3 clients predicted to be employed were unemployed.
The three most important variables in discriminating between
the groups were the number of months worked (before), worker
function data (after) and mean wages received (before).
These were followed in order of decreasing importance by:
worker function data and things (before), locus of control
(LOC), and geographic location. The remaining variables
were of lesser importance. The null hypothesis is rejected
and it is concluded that a linear combination of predictors,
including demographic, locus of control and work history
variables were able to discriminate the employed from the

unemployed at post VRS.

Hypothesis 7

 

Ho: There will be no relationship between the
clients' prior work characteristics with post VRS work
characteristics.

This hypothesis was tested by Canonical Analysis
(CA). CA is a method designed to study the underlying rela-
tions between two sets of variables. The objective of CA is
to derive a linear combination from each of the sets of
variables by differentially weighting so that the correla-
tion between the two linear combinations is maximized by
extracting the largest source of variance. The correlation
between the two linear combinations is the canonical cor-

relation, Rc- The square of the canonical correlation

183

R3, is an estimate of the variance shared by the two
Canonical Variates. R3 is not an estimate of the variance
shared within one set of variables but of that between the
linear combinations for two sets of variables, which is
represented by eigenvalues, and each pair of linear
combinations derived are called Canonical Variates.

The number of canonical correlations and canonical
variates obtainable in a given set of data is equal to the
number of variables in the smaller of the two sets of vari-
ables (Pedhazur, 1982). In this analysis, there are 12
variables in set I representing before and after disability
work variables (6 each). Set II is comprised of 6 similar
variables based on client present occupations. One would
expect to have 6 canonical correlations and six sets of
canonical variates. Table 4.22 shows the order of the
magnitude of the canonical correlations. The square of the
canonical correlation (Rg) indicates the proportion of
variance shared by a pair of canonical variates or its
eigenvalues. The first pair of canonical variates shared
about 99% of the variance; its eigenvalue was RC1 = A1.

The second pair shared 94% of the variance (Rc2 = A2)
and so on.

When there is more than one eigenvalue, the contri-
butions of the eigenvalues can be compared by converting the
eigenvalues into relative percentages which indicate the
strengths or weaknesses of the canonical variates relative

to each other. From the table, it is apparent that the

184

TABLE 4.22.--Canonical Correlational Analysis of Before and
After Disability WCrk Variables With Post VRS
Work Variables.

 

#-

 

EIGEN- RELATIVE CANONICAL WILKS' CHI-

NO. VALUE % CORRELATION LAMBDA SQUARE DF P
1 .995 26.30 .997 .000 122.583 72 .000
2 .947. 25.03 .973 .003 62.776 55 .220
3 .707 18.67 .840 .063 30.267 40 .868
4 .577 15.25 .759 .217 16.760 27 .937
5 .341 9.02 .584 .515 7.286 16 .967
6 .216 5.73 .465 .783 2.688 7 .912

Standardized Coefficients for Canonical Variables of the
First Set

 

 

 

 

Canonical
Before and After Variate 1
Worker function-data (after) 1.25
Worker function-data (before) -.88
Job satisfaction (after) .85
Job satisfaction (before) -.82
Number of months (before) .64
Worker function-things (after) -.43
Worker function-things (before) .33
Wage (after) -.30
Number of months worked (after) -.29
Worker function-people (before) -.28
Worker function-people (after) -.16
Wage (before) .01

Standardized Coefficients for Canonical Variables of the
Second Set

 

 

 

 

 

Canonical
Post VRS Best Variate 1
Wage .54
Worker function-people -.52
Number of months worked ‘ ' -.44
Worker function-things -.35
Job satisfaction .23

Worker function-data .15

 

185

first eigenvalue is almost five times larger than the last
one. The first two are about equal but the second one is
not significant.

The first pair of canonical variates are derived so
as to have the highest intercorrelation possible by extract-
ing the largest source of variance given the particular
variables involved. The second set of canonical variates is
derived to account for a maximum amount of the relationship
between the two sets of variables left unaccounted for by
the first canonical variates and so on. After the first
canonical variates, the remaining sets account for residual
variance, or the variance for which the preceding components
or sets of canonical variates could not account. The re-
maining variates and their linear combinations are in-
dependent and uncorrelated.

The test of significance in canonical analysis is
Bartlett's (1947) test of Wilks' Lambda (A).

A=<l-R%1)(1-R§2) ..... (i-jo) Eq. 8

Or, equivalently,

A=(l-Al)(1-A2) ..... (l-Aj) Eq. 9
The test of significance of A is the Chi-square statistic

x2 a -[N - 1 - .5(p + q + 1)]lnA Eq. 10
where Nanumber of subjects; panumber of variables in the
first set; q-number of variables in the second set; 1n=
natural logarithm. The degrees of freedom associated with
this Chi-square test are pq.

Bartlett's test indicates the number of canonical

variables necessary to express the dependency between the

186

two sets of variables. The necessary number of canonical
variables is the smallest number of eigenvalues such that
the test of the remaining eigenvalues is non-significant. In
other words, the overall test of the null hypothesis assumes
that all six eigenvalues are equal to zero (Rg= A = 0) and
then determines which of the eigenvalues obtainable from a
given set of data are statistically significant (Pedhazur,
1982).

The first canonical correlation is significant at
the .000 level with a chi-square of 112.58. The probability
of obtaining this large a chi-square value is less than one
chance in 1,000. The overall Lambda (A) was tested and the
null hypothesis was rejected. It may be concluded that at
least the first canonical correlation is statistically
significant and that there is a relationship between the
clients' prior work characteristics and post VRS work
characteristics. Then the subsequent canonical correlation
is tested removing the preceding canonical correlation. The
degrees of freedom for the chi-square test for the first
canonical correlation is the product of the number of
variables in both sets pq, 12x6=72. The degrees of freedom
for the second canonical correlation, after removing the
first Rcr is (p-1)(q-1) or 11x5=55, and so on.

The first set of the canonical variates is important
to this study. The loadings of the individual variables are
presented in descending magnitude. These loadings or canon-

ical weights are standardized canonical variate coefficients

187

(EEO, SD81) for canonical variables in each set and an index
of relative importance or contribution of the variables with
which they are associated. It is not the sign associated
with the weights which is important, but the magnitude of
the weights. After disability work characteristics
involving data were more important than working with data
before disability, while working with the data was the least
important in post rehabilitation work characteristics.
Combined work characteristics involving people were least
important, whereas people became most important in post VRS.
It seems that the disability imposed restrictions in working
with data and things for rehabilitants in post VRS.

Mean job satisfaction after and before disability
were about equal, whereas in post rehabilitation it was
relatively less important. It is conceivable that in com-
bining before and after disability, they worked for a longer
period of time than in current post rehabilitation employ-
ment. The analysis was based on 21 subjects who had a com-
plete set of data for those 18 variables. In other words,
the subjects were those who had a work history before and
after disability, and were employed after VRS and had a
complete set of data on these variables.

In the foregoing analysis before and after disa-
bility work variables were combined, and established a
strong positive relationship with the work variables in post
VRS based on the limited sample size. Before and after work

variables were also studied separately with post VRS work

188

variables. Before disability and post VRS work character-
istics did not yield any significant canonical variables.
However, after disability and post VRS work characteristics
yielded two canonical correlations using 52 clients. Table
4.23 shows the order of the magnitude of the Canonical Cor-
relations. The first pair of canonical variates share 70%
of the variance, the second pair 37% of the variance and
both are significant. However, the first pair of variates
is about two times stronger than the second pair as indicat-
ed by the relative percentages. The first pair of Canonical
Variates significant to this study are listed in descending
magnitude. In comparing the combined analysis with post,
and after with post, it is apparent that the order of the
loadings in post remained almost identical. The significant
findings of the canonical analysis were similar to the

Cluster dimension obtained in BC TRY Cluster Analysis.

Summary of the Hypotheses Tested

The seven hypotheses tested in the study using
various subsets of variables, the statistics used to test
these hypotheses, the results of each hypothesis and the

levels of significance are shown in Table 4.24.

Summary of Results

1. The relationship between the clients' locus of
control, as measured by the Locus of Control Scale (LOC) and

Modified Locus of Control Scale (MODLOC), and work

189

TABLE 4.23.—-Canonical Correlational Analysis of After
Disability Work Variables With Post VRS Work

 

 

 

Variables
EIGEN- RELATIVE CANONICAL WILKS' CHI-
NO . VALUE PERCENTAGE CORRELATION LAMBDA SQUARE OF P
1 .707 46.87 .840 .114 97.482 36 .000
2 .371 24.60 .609 .391 42.215 25 .017
3 .233 15.50 .483 .622 21.338 16 .166
4 .128 8.52 .358 .812 9.356‘ 9 .405
5 .067 4.50 .260 .931 3.169 4 .530
6 .000 .01 .016 .999 .011 1 .914

Standardized Coefficients for Canonical Variables of the
First Set

 

 

 

Canonical

After Variate‘T
Wbrker function-data ‘.41
Worker function-things .37
Wage -.35
Number of months worked -.25
Worker function-people -.02
Job satisfaction -.01

Standardized Coefficients for Canonical Variables of the
Second Set

 

Canonical
Post VRS Best Variate 1

 

 

 

Number of months worked -.24
Worker function-people -.23
Worker function-things .15
WOrker function-data .08

Job satisfaction .04

 

190

 

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191

 

 

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192

motivation, as measured by the Vocational Opinion Index
(VOI) was not established.

2. There was no difference in locus of control
orientation, a personality characteristic measured by the
Locus of Control Scale and Modified Locus of Control Scale,
with respect to employment status as reported by the
Counselors, Clients, and Computed Best Values.

3. Clients who were internal found jobs earlier
than those who had an external locus of control orientation.
The MODLOC established 3 significant relationships with the
Counselors' report, Clients' report and the Computed Best
Values, whereas the LOC established relations only with the
Counselors' report.

4. Significant differences were found between the
clients' demographic characteristics in differentiating the
employed from the unemployed.

5. There was no difference between the clients'
work motivation, as measured by the VOI, in differentiating
the employed from the unemployed.

6. Significant differences were found between the
clients' combined work experience before and after
disability and their current employement status (Post VRS).
Before disability work experience alone could not
differentiate the clients' employment status whereas after
disability work experience alone could.

7. Significant differences were found in the

prediction accuracy of linear combinations using subsets of

193

clients' demographic, locus of control, before disability
work experience, and after disability work experience
variables. The presence or absence of the VOI, LOC and
MODLOC made no difference in the linear model. However, the
absence of before disability work experience made the linear
model non-significant.

8. Significant relationships were found between the
client's combined before and after disability multidimen-
sional work characteristics and post VRS work character-
istics. Also, a significant relationship could not be
established between before disability work characteristics
and post VRS work characteristics, but was established
between after disability work characteristics and post VRS

work characteristics.

CHAPTER V

DISCUSSION

Introduction

This chapter will begin by discussing and inter-
preting the results obtained by testing the hypotheses in
Chapter IV. Then, the limitations of the study will be,
discussed. Finally, conclusions and some implications for

future research will be presented.

Discussion

 

The first hypothesis tested the relationship between
the clients' locus of control orientation as measured by the
LOC and MODLOC, and work motivation as measured by the VOI.
The LOC, a personality instrument, measured the clients'
individual differences of generalized expectancy across
situations, and the MODLOC measured the clients' individual
differences of specific expectancy related to work. The LOC
has been used extensively in the research literature and has
established its usefulness in predicting academic achieve-
ment, job success, intra- and interpersonal adjustment,
satisfaction in life and jobs with children, adults, ethnic
minorities, disadvantaged, and psychiatric patients. The

194

195

MODLOC is a short, modified version of the LOC found to be
useful in predicting work related behavior specifically with
minorities and disadvantaged populations. The literature
suggested that the "internals” are those who can control
their reinforcement; they are more motivated to change their
circumstances than those externally oriented. If, in fact,
the “internals” are more motivated than the ”externals" then
the measures of locus of control may be related to another
measure of motivation, such as the VOI. The results indi-
cated that out of the 13 V01 factors, 3 different factors
were significantly related to the LOC and the MODLOC. The
degree of relationship, represented by the contingency co-
efficients between these factors and the locus of control
variables, was moderate. In comparison, the MODLOC, which
measures specific expectancy related to work initiative was
more highly related to work motivation than the LOC. Based
on the only 3 significant factors, the overall significance
of 13 VOI factors could not be generalized. The 3 signifi-
cant factors may be just an artifact of the data or the
statistical analysis. Perhaps the locus of control orienta-
tion and the VOI measure two different aspects of the
motivational dynamics of rehabilitation clients. After a
series of studies investigating the attitudes toward and
reaction to disability by the disabled and non-disabled,
MacDonald (1971c) concluded that the internal-external

control dimension is a promising rehabilitation variable;

196

however, "locus of control is not a motivational variable
but rather an expectancy variable.” (p. 115)

The first part of the second hypothesis tested was
that clients with an internal locus of control would be
employed at six months follow-up and the externals would be
unemployed. The literature on locus of control orientation
suggested that the behaviors of the internals are achieve-
ment oriented. They seek more information, use the infor-
mation gathered in making decisions, and spend more time in
deliberating about the decisions in skill-demanding tasks
than chance-demanding tasks. So, the internals should have
a greater potential for effectiveness in their employment
seeking behavior and activity in the world of work. The
chi-square statistics between the LOC, MODLOC, and employ-
ment status as reported by the counselors, clients and
computed best values were non-significant. This means that
the clients' locus of control orientations are independent
of their employment status for the sample of this study,
which does not support the expected behavior of the inter-
nals. These opposing facts may be viewed from the stand-
point that Rotter's 29 item LOC scale scores may manifest a
number of specific or circumscribed beliefs about LOC each
of which may be more applicable to some specific situation
than others. Phares (1976) commented that "its effects on
behavior are not uniform and invariant across all situa-

tions.” (p. 45-46)

197

It seems that the locus of control orientation may
make a sound prediction in some situations, but may fail to
predict in a specific situation or specific expectancy.
Table 4.2 showed that the mean scores for the sample on both
the LOC and MODLOC were lower than the generalized trends
reported by Cellini and Kantorowski (1982) for college
students. There was no comparable norm group available for
rehabilitation clients. The sample mean was in the internal
direction. Three conclusions can be drawn from the fore-
going facts: first, that the rehabilitation clients were
more optimistic than expected in their perception about the
world of work in terms of the benefits one can get from a
job; second, the internality and externality may mean
completely different dynamics in locus of control orienta-
tion for rehabilitation clients which require further
investigation with controlled studies; and finally, the
heterogeneity of the sample (115 clients from 3 different
states representing 13 categories of disabilities) may have
over-shadowed the effects of the internality and externality
dimensions both in the first and the second hypotheses.
Among these 13 categories of the disabled sample, more than
25% were alcoholics and drug addicts, and their paradoxical
behavior on Rotter's scale has been observed by many. Goss
and Morosko (1970) reported that surprisingly alcoholics
score significantly more internally than Rotter's (1966)
general norms. Oziel, Obitz and Keyson (1972) developed a

specific LOC to measure the specific expectancy of

198

alcoholics. The substance abuse—engendered internality was
distinguished as pseudo-internality by Berzins and Ross
(1973). The foregoing discussions supported the notion of
the findings of the first two hypotheses.

The second part of the second hypothesis indicated
that internality was related to obtaining employment earlier
than externality. The Pearson Product-Moment Correlation
Coefficient was significant with the MODLOC and the mean
number of months worked as reported by the counselors,
clients and computed best values, whereas the LOC was sig-
nificant only with the counselor's report. Once again, it
appeared that the MODLOC was a preferred measure as it
yielded three significant relationships whereas the LOC
yielded only one. The magnitudes of the correlations
between the variables were of moderate size, though magni-
tudes of correlations obtained with the MODLOC were rela-
tively larger than one obtained with the LOC. However, the
overall magnitudes of the correlations might have been
higher and stronger if the follow-up period had not been
restricted to six months. For many, it takes longer than
six months to obtain a job in post VRS given the unemploy-
ment rate and other intervening variables of the labor
market.

In this analysis, the mean number of months worked
after VRS was used instead of the total number of months
worked. This was done in order to use the same variables

to represent length of employment period before and after

199

disability. The mean number of months worked in post VRS
does in fact represent the total number of months for all
subjects with the exception of those who had 2 jobs during
the six month follow-up. The numbers of clients who had one
job as reported by the counselors, clients and computed best
values were 69, 52, 77; and two jobs were 3, 9, and 10
respectively (Table 4.4). The total number of clients
employed were 72, 61, and 87 whereas in the analysis 53, 58,
and 76 were used as reported by the counselors, clients and
computed best values respectively. These differences in
sample size were due to the individuals who were working but
for whom no detailed information was available, and those
closed in Status 26 as ”home maker".

We have seen that the relationship of locus of con-
trol orientation to work motivation was not significant in
the first hypothesis or with employment status in the first
part of the second hypothesis. How is it then possible that
the internals obtained employment significantly earlier than
the externals? One of the explanations may be due to treat-
ing the scores on the two measures of locus of control
orientation as continuous scores rather than categorical
scores as had been done earlier. The continuous scores were
more powerful in finding differences in the data than the
categorical scores, as parametric statistics are more
powerful than non-parametric.

From the foregoing analysis it appeared that the

MODLOC is a superior measure of locus of control orientation

200

as it measures work initiative or specific work expectancy
rather than the generalized expectancies across situations
measured by the LOC. Generally, the internal-external
orientation is equated with skill vs. Chance. In the case
of the disadvantaged and disabled, the handicapped experi-
ence obstacles which have nothing to do with chance, but
rather depend solely upon the nature and type of disability
and the restrictions it imposes, the conditions of the
present labor market, geographical location, transportation,
employer hiring and firing practices and many other inter-
vening variables in the clients' environment. Andrisani
(1977) used this modified version of the LOC and reported
that the internal attitudes were strongly related to the
number of labor market experiences for the disadvantaged.
Also, this modified version (MODLOC) had a moderate
relationship with the variables of the rehabilitation
clients in this study. A stronger relationship could have
been obtained if the samples had been more homogeneous in
terms of their disabilities.

In hypothesis three, the initial Discriminant Analy-
sis (DA) on a set of 23 demographic predictors on 30 cleints
failed to discriminate significantly on the outcome criter-
ion between the employed and unemployed. The variables
which discriminated the groups with higher loadings were the
number of dependents, marital status, education, sources of
referral, geographical location and secondary physiological

disability. The Discriminant function derived using 18

201

variables on 30 clients predicted with 97% accuracy. Sixty-
four percent of the variation was accounted for by the set
of demographics in discriminating the groups. However the
function was not significant. The variable-to-subject ratio
may explain the non-significant result. However, the
stepwise DA yielded a significant result, selecting 4
variables based on 30 clients and classifying 33 clients
with 85% accuracy. The 4 variable set accounted for 46% of
the variation. The reason the linear model was significant
may be that the variable-to—subject ratio is much better
than in the initial analysis.

The variables selected in stepwise DA and the
variables with higher loadings obtained in the direct method
were similar to the findings of DeMann (1963), Kunce and
Miller (1972), and Worral and Vandergoot (1980, 1982). It
may be concluded that the 4 variable set discriminated the
group significantly. Whether such variables as the presence
or absence of any secondary disability due to physiological
reasons, single person with no dependents or married with
dependents, sources of support at referral from family or
public assistance or social security, whites or non-whites
would affect the probability of being employed versus
unemployed needs further investigation. Also, the lack of
cross validation is a major drawback in any predictive
study. The absence of a complete set of data on a set of
variables among the cases reduced the chance for cross

validation by partitioning the sample.

202

The Job Readiness Posture (JRP), a measure of
clients' attitudes, perceptions and motivation as measured
by the VOI, and their impact on the ability to obtain and
maintain a job at post VRS failed to differentiate the
employed from the unemployed in hypothesis four. Despite
having a complete set of data on the 13 VOI factors for all
115 cases, the discriminant functions derived by both the
direct and stepwise procedures were not significant. This
indicates either that the clients' attitudes, perceptions
and motivations did not relate to their employment status or
that the instrument measure something else which was un-
related to employment for the rehabilitation clients.
Follow-up reports indicated that the number of clients
employed were 72, 61 and 87 as reported by the counselors,
clients and computed best values (Table 4.7). From this we
may conclude that the JRP measured by the VOI is unrelated
to employment status for rehabilitation clients.

The next question would be, what does the VOI really
measure? The VOI claims that the JRP profiles are cor-
related 90% of the time with actual behavioral work status
after training. It seems that the JRP is a good measure for
diagnosing workers from non-workers who have attitudinal or
predispositional problems. A remedial prescription is made
for a non-worker to resolve those problems or barriers
during the training program, to develop appropriate worker

postures.

203

The inability of the VOI to relate with the locus of
control construct earlier and now with motivation to work
may be in part due to a methodological drawback in the
study. The VOI is typically administered at the beginning
of a training program to identify a potential non-worker, at
the completion of the training program to evaluate the
effectiveness of the remedial prescription, and at 13 weeks
after a trainee leaves the program to validate the expected
work status and actual work behavior. In this study the VOI
was administered only once, at the end of client training or
prior to his or her embarking on job seeking activity, to
measure the client's JRP once the cleint achieved readiness
for placement at Status 20. The first five of seven cate-
gories of the outcome measure (see Table 4.6) raised some
doubts about the job readiness of Status 20 clients given
the uncertainty of the labor market and other intervening
variables. At six month followhup, this group included
those who were not interested in employment, were going for
further training, did not work in last six months, were
employed but no further information available, and were
employed in last six months but did not want to talk about
what they had been doing. The motivational dynamics of
these clients are indeed very complex in the presence of
financial disincentives and non-work-contingent income asso-
ciated with disability (Neff, 1977). Such disincentives act
as motivational barriers to work for rehabilitation clients.

This corroborates the concluding remarks made by Barry and

204

Malinovsky (1965) in their review of client motivation in
rehabilitation. They concluded that the concept of motiva-
tion is too broad, too complex, and too vague a construct to
be useful in rehabilitation practice. However, research
should not and cannot put an end to the investigation of a
construct due to its complexity, but rather a more sys-
tematic attempt has to be made. This study was an attempt
in that direction.

The VOI was designed, tested and normed specifically
for the disadvantaged population and no norms or JRP profile
are available for rehabilitation clients. Perhaps this is
the first time an attempt has been made to determine how and
to what extent the concept of JRP would be useful to reha-
bilitation clients. Concurrently, the Human Resources
Center is conducting a National Study using the VOI. The
efficacy of the VOI as a diagnostic tool in rehabilitation
can be judged only after performing controlled studies
following its methodological and procedural guidelines. It
may be concluded that the VOI might be a good diagnostic
instrument as it claims to be but surely cannot be used for
predictive purposes.

Work motivation can be understood more realistically
by analyzing the work personality in a developmental con-
text, rather than from psychometric scores of paper and
pencil tests. The work personality is the result of inter—
actions with the environmental demands of the worksetting,

performance of some task, ability to get along with

205

co-workers and supervisors, and the quality and quantity of
work produced. In this study, work personality was also
investigated from a different perspective by studying the
various components of work characteristics such as: the
complexity of tasks performed in terms of the hierarchy of
data, people and things; the depth of involvment; require-
ments for training, certification of licensing to perform
the job; wage; job satisfaction and reasons for leaving
their jobs before and after disability prior to VRS. The
extent to which a client's rehabilitation outcome can be
predicted from their previous work history and experience
was the subject matter of investigation in hypothesis five.
The clients' before and after disability work char-
acteristics combined, were able to discriminate signifi-
cantly the employed from the unemployed. Further explora-
tion has been made to determine how the before and after
work history independently can differentiate the employed
from the unemployed. The before disability work character-
istics independently were unable to achieve significance
whereas the after disability work characteristics were able
to distinguish the groups significantly. The 7 after
variables selected with a stepwise procedure accounted for
only 16% of the variance for 84 clients with a Canonical
Correlation of .40 whereas the 7 combined variables from
before and after accounted for 62% of the variance for 27

clients with a Canonical Correlation of .78. The former

206

predicted group membership for 86 (75%) clients with 71%
accuracy and the latter for 34 (30%) clients with 94%
accuracy.

The linear model failed to predict significantly
with only before variables, and the after variables
predicted with moderate significance; however the combined
variables predicted well. Although the before variables
independently were not significant, when combined with after
variables they contributed the two most important variables,
namely, number of months worked and percentages of full-time
jobs held before disability as depth of work involvement in
the combined linear model. The after variables independent-
ly were signficant. The importance of the after variables
may be due to the recency of the work activity and the
ability of the clients to cope with and adjust to their
disability to function as effectively as possible prior to
seeking help from VRS.

How do rehabilitation counselors or practitioners in
the field choose a linear discriminant function model to
predict the employment status of those clients who either
worked before or after but not both or who possess both the
before and after attributes? Schematic diagrams depicting
the groups of clients and the type of prediction equation
one may use, are presented in Figure 5.1 and 5.2. Group I
has a stable work history both before and after disability.
These clients were older adults and recently the disability

had imposed restrictions on their full functioning; as a

207

WORKED AFTER DISABILITY

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

T j YES NO
WORKED 5 YES ! lGroup I Group II
BEFORE 1 N827 N819 46
DISABILITY % NO Group III Group IV
g N857 N812 69
84 31
Figure 5.1--Schematic diagram of client groups.
VOCATIONAL REHABILITATION SERVICES INTERVENTION
Group I Group II Group III
Total N 27 46 84
iDISCRIMINANT“ Employed 23 36 66
i FUNCTION Unemployed 4 10 E 18
1 EPrediction 1 1
i__ :Accuracy 100% 60% 71%
E Total N 34 50 86
iCLASSIFI- Employed 29 39 67
1 CATION Unemployed 5 11 19
:FUNCTION Prediction
1 Accuracy 94% 60% 71%
l

 

 

 

 

 

Figure 5.2.--Schematic Diagram of Client Groups.and Their

Prediction Rate Based on Work History.

 

208

result they were recently unemployed and sought VR services.
These clients were highly motivated and probably did not
receive public assistance. The prediction of rehabilitation
outcome would be high for this group. These clients pre-
sented 30% of the sample and predicted with 94% accuracy.

Group II are those adults who worked before but not
after disability. Prediction for this group was difficult
and dependent upon: the depth of work activity before
disability, the type of disability, work motivation, the
waiting period in seeking VR services, and the status of
public assistance. These clients represented 43% of the
sample and were predicted 60% accurately, which was non-
significant.

Group III included individuals who became disabled
at an early age, and the question of working before disabil-
ity did not arise. Either disability did not impose much
restriction initially or they adjusted to it and worked for
a while. But recently, the disability had become severe,
resulting in unemployment and the cleints sought VR ser-
vices. These clients represented 75% of the sample. Pre-
diction was possible with an accuracy of 71% for this group
but this was not as good as for Group I. The outcome for
Group III was predicted better than that for Group II due to
the recency of their work activity. I

Group IV were the younger disabled without any work
experience. Accordingly, prediction could not be made for

this group using work variables; however, other domains of

209

predictor variables such as demographic or psychometrics may
be used.

The impact of before disability work history and
experience may not be obvious from the statistical signifi-
cance; however, it has an underlying contribution in the
building of appropriate attitudes, competence, self-reliance
and self-direction for future vocational adjustment. After
disability work experience provides a testing ground for
coping mechanisms related to disability and the ability to
mobilize the maximum residual abilities. The combined work
history and experience seems to serve as building blocks of
a pyramidal occupational pattern for some rehabilitation
clients.

The foregoing discussion centered around the pre-
diction of rehabilitation outcome using a specific domain of
clients characteristics whose ability to contribute inde-
pendently in differentiating between the groups has been
noted. In the sixth hypothesis all the domains or predictor
variables were linearly combined in five different stages to
derive the best linear prediction model. The goal was to
generate a linear discriminant function with optimal pre-
dictors which was both statistically significant and pragma-
tically meaningful, thus to improve the efficiency of the
linear model by reducing the irrelevant and non-contributing
predictors without losing the predictive power of the model.

Throughout the combinations, demographics were kept

constant as these are relatively permanent characteristics

210

or attributes of the Clients. Psychological attributes,
such as LOC, MODLOC and VOI were not always included in
different stages to note their relative contributions. The
absence or presence of the LOC, MODLOC or the VOI did not
affect the model. When all were deleted, the remaining
error variance was high, the model was statistically
significant but practically it was not meaningful.
Psychometric scores tested earlier in the study were also
non-significant in discriminating the groups which may
account for the situational or time specific attributes of
the client. The work variables were also kept constant
along with the demographics until the last stage, as they
were found useful in an earlier hypothesis. Since more
clients worked and held more jobs after disability than
before disability (Table 4.4), the before disability work
variables were deleted in Stage V. The remaining error
variance was at its maximum, 85%. Similar findings were
reported by Buell and Anthony (1973) with alcoholics: when
the employment history was deleted from the model, a major
decrease in variance explained occurred. In this study,
employment history indicated two different time periods,
before and after disability. One may argue that higher
error variance in the model was due to the increase of
sample size as a result of deleting the before work
variables. Nevertheless, the Stage I and III models were
significant with almost the total sample. Overall, the

Stage II linear combination appeared to be the best in this

211

exploratory study based on a limited sample size (22%) who
had a complete set of attributes across the domains of pre-
dictors in the model and in the absence of cross validation.
Finally, the last hypothesis was not concerned with
differentiating the groups, but rather examined whether or
not a relationship existed between the nature and types of
work performed prior to the VRS with work performance at
post VRS. The canonical analysis of before and after
disability work variables with post VRS work variables
extracted the largest source of variance, 99%. This first
pair of canonical variates was significant while the
remaining pairs of variates were not significant. The
before work variables independently could not yield any
canonical variable. The after work variables yielded two
significant canonical variates. The first one accounted for
almost double the variance of the second one. The pro-
portion of variance accounted for by the first canonical
variates derived from the combined work variables was
greater than that accounted for by the first variates based
solely on after work variables. The combined variables
indicated that the rehabilitants were working more with
people, less with things, and least with data whereas prior
to VRS they worked more with things and data, and least with
people. It seems that the disability may have imposed
restrictions on the rehabilitants to work with data and

things.

212

In summary, it appears that the work history and
experience variables were the most important predictors in
differentiating the rehabilitation clients in terms of
employment followed by the demographics. The psychometric
measures were expected to contribute in the prediction

model; however, that was not the case in this study.

Limitations

The sample of the study consisted of 115 rehabilita-
tion clients from three different states, seven district
offices, five training facilities, three hospitals and one
job club. The sample represented 13 categories of disabili-
ties. The heterogeneity of the handicapping condition may
have obscured finding relationships among the locus of
control orientation, and work motivation and status of
employment.

The subjects of the study were all rehabilitation
clients who volunteered to participate. A major source of
systematic bias would be the variation between the voluntary
and non-voluntary rehabilitation clients. In Louisiana,
when the first scheduled testing was changed due to a small
sample size, although the clients agreed to participate in
the second testing data, many of them did not show up in the
district offices. As a result, the strategy of data collec-
tion was changed to include training facilities. This may

have caused a source of bias.

213

The data on the predictor variables were collected
during the end of November through the middle of December
1982. The clients who completed their training were placed
in Status 20 just before Christmas, 1982, when the hiring
for the holiday season was almost over. Generally, the
clients who graduate from a training program in the spring
or before the holiday season had a better chance of finding
a job than those who graduated in December. Over and above,
in Michigan and New York, cold and snow inhibit the clients'
search for a job, and Louisiana was hit by floods in
different parts of the state in early spring of 1983.

The study was conducted at a time when the national
economy was ravaged by high inflation, recession and high
unemployment. Michigan had the highest unemployment rate in
the country. A six-month follow up did not provide enough
time for some clients to find a job, given the recessionary
economy and conditions of the labor market.

The dependent variable measure of the study was the
clients' employment status and its associated character-
istics. The follow-up data were crucial in this predictive
study. Hence, the follow-up data were collected both from
the counselors and clients. The follow-up data could not be
collected from 27 clients themselves, and on 9 clients from
the counselors, a source of limitation.

During the Discriminant Analysis, the major problem
encountered was the absaence for many subjects of a complete

set of data on the predictor variables being studied. As a

214

result, in the absence of a single value or data on the set
of predictors, the case could not be used in the analysis.
The missing data in most cases meant that the Client did not
possess that attribute. This caused a poor subject-to-
variable ratio which is difficult on statistics and means
that some of the underlying assumptions of the analysis were
not met, thus limiting generalizability.

The major drawback of the predictive study was the
lack of cross validation of the present model on another
sample. With a total sample of only 115 and in the presence
of missing data, it was almost impossible to have cross
validation, even with partitioning of the sample. Hence the
limitation of the exploratory predictive study. However, no
attempt has been made to analyze the data by ignoring the
missing value indicators or replacing the missing values by

estimation in the computer.

Conclusions

 

This study attempted to develop a prediction model
for rehabilitation clients on vocational rehabilitation
outcome using client characteristic predictor variables of
demographics, locus of control, work motivation, and prior
work history and experience. The demographics were signifi-
cantly related to outcome, differentiating the employed from
the unemployed. The contribution of the locus of control
orientation and the Job Readiness Posture remains question-

able. The data support the notion that the prior work

215

history and experiences prior to VRS significantly affect
the outcome in post VRS. The before and after disability
work variables in combination make the greatest contribution
both in the independent and linear model, followed by the
after disability work variables. The before disability work
characteristics independently were found not to contribute;
however, their underlying contributions to the outcome were
obvious in three hypotheses. '

The data support the fact that the multidimensional
aspects of work attributes were the most important client
characteristics in determining the likelihood of success in
rehabilitation. Rehabilitation counselors should look
closely at the client's work history, experience, and
related activities and behavior. Those clients who possess
both the before and after disability experience would more
likely be successful in obtaining employment after vocation-
al rehabilitation services than those who have only after
disability work experience. For those clients who have no
work attributes, the odds are that they will be tough cases
for the counselors to handle.

The significance of this exploratory predictive
model should be interpreted cautiously as the study used
voluntary rehabilitation clients and heterogeneous dis-
ability groups. Due to a small sample size, the presence of
missing values in the set of predictors in the discriminant
analysis and lack of cross validation make the generaliza-

tion of the model to the population of rehabilitation

clients are even more restricted. However, Klecka (1975)
comments that
The statistical theory of discriminant analysis
assumes that the discriminating variables have a
multivariate normal distribution and that they
have equal variance-covariance matrices within
each group. In practice, the technique is very
robust and these assumptions need not be strongly
adhered to. (In Nie et al., p. 435)

The special features of this predictive study are
the following: it used active rehabilitation clients; it
used four different domains of clients' characteristic pre-
dictors which were studied independently and in linear com-
binations; work history, experience and multidimensional
work characteristics were studied extensively in three dif—
ferent time periods; follow-up data were collected from both
counselors and clients; and the best values were computed to
obtain increased reliability of the follow-up data.

The study showed promise for future investigations
to determine the usefulness and validation of the construct
of locus of control orientation and Job Readiness Posture
for vocational rehabilitation clients. Another line of
inquiry may be directed to develop a prediction model on
specific disability groups. Finally, a holistic study for
rehabilitation would be to develop a model that can accommo-
date the conglomerate domains of predictors including the
client, the counselor, the process and the labor market
variables simultaneously. We all can look forward for that
kind of complex although ideal study to alleviate the suf-

ferings of a significant portion of humanity to whatever

extent possible.

217

Implications for Future Research

There are several approaches which future studies
might take with respect to determining the usefulness of
psychometric measures on rehabilitation clients, and
developing a prediction model based on client variables and
other significant intervening variables that affect the
rehabilitation of the disabled.

The LOC and MODLOC are simple, easy to administer,
score and interpret for the counselors. The usefulness of
the construct of locus of control in rehabilitation needs
further validation. The MODLOC appears to be a promising
instrument. These two instruments need to be used in
controlled studies with homogeneous clientele to determine
their effectiveness with rehabilitation clients.

The JRP profiles diagnosis by the VOI claims to be
successful 90% of the time for disadvantaged populations.
It might have some usefulness for rehabilitation clients.
Controlled studies are necessary following the procedural
methodology of the VOI to diagnose rehabilitation clients as
a worker or a non-worker early in the rehabilitation pro-
cess. Accordingly, the counselors can intervene and imple-
‘ment the remedial prescription during counseling and with
the training staff at training facilities.

A systematic investigation may be made to build up a

composite score or profile, using the VOI which consists of

218

three psychological dimensions derived from 13 factors, a
diagnostic score, and another score for predictive purposes.

In order to obtain accurate and reliable follow-up
data, they must be collected from as many sources as
possible, keeping in mind their usefulness and relevancy.
Otherwise, it provides unbalanced or biased information.
When the data were collected from more than one source, such
as in this study from the counselors and clients, the
agreement of the counselors' and clients' report can be
investigated. This would delineate the areas of agreement
and disagreement. The areas of incongruency can be
addressed during counseling to minimize the differences,
where congruency is the goal acting as a catalytic agent
which facilitates counseling to achieve goal direction.

The clients' work history variables were found to be
most useful in predicting outcome in the study. However,
the labor market variables play the major role in finding a
job, rather than any other rehabilitation intervention
variables. To determine the effect of disability on skill
level, occupational pattern before and after disability, and
job after VRS the McCroskey Vocational Quotient and the
Vocational Diagnosis and Assessment of Residual Employ-
ability (VDARE) may be used.

The vocational rehabilitation program serves
clientele with any number of disabilities or handicapping
conditions. Each disability demands some unique services

and needs, but there are also common needs across the types

219

of disability which require attention in order to be
rehabilitated successfully. To develop a prediction model
for rehabilitation clients, future studies should focus on a
specific disability group with a large sample size, making
the group more homogeneous and thus improving the
predictability of the model for that group. The predictor
variables that are found to be similar with various
disabilty groups may be combined to develop a single model
that can have useful predictive purposes for all types of
rehabilitation clients. An alternative approach would be to
study all disability types together having a large sample in
each category and (a) complete set(s) of predictor data.
This then allows the disability type to vary in the model,
keeping the predictors constant. This would also delineate
the contributions of the predictors for a disability group.
The large sample size would facilitate the cross validation
within and between the disability group.

The outcome of rehabilitation is complexly
determined by a number of factors other than the factors of
the clients. The counselor, the process, labor market and
client variables all play their own roles and interact
complexly: the resultant vectors determine the ultimate
outcome. A study that can address all the client, the
counselor, VR process and labor market variables to develop
a significant prediction model would be another step in the

process of solving the puzzle of rehabilitation.

APPENDICES

APPENDIX A

CORRESPONDENCE

220

5‘5}: :7 4r 1'3" STATE or ARKANSAS
'. 13..- ,:‘,‘ .
{NSF—9’: [Department of flame): 6&1.ku

DIVISION 0‘ REHABILITATION SEIWCES

mam: mm: 1401 aaooxwooo DRIVE 9 "”3““- “:7“
govt-won P 0. BOX 3781
um: nocx ARKANSAS m
aavscorv .
omicma PHONE - 371-2411

July 9, 1982

Dave Vandergoot, Research Specialist
Human Resources Center
Albertson, Long Island, NY 11507

Dear Dr. Vandergoot:

Thanks very much for your clarification of the research
project on work readiness. I am very sorry I have caused
some delay and suppose I will now cause an additional
delay.

As I interpret your last letter, you will only be adding

the states of New York, Connecticut, and Michigan. Our

policy states that if four states, or fewer, are involved

then the request should go only to the states. I am,
therefore, asking that these three states contact you directly
as soon as possible to advise you of their participation in
your study.

Finally, if you have additional questions, they should be
directed to the new chairman of the CSAVR Committee on
Research, Mr. Claude A. Myer. Mr. Myer is the Director

of the North Carolina Division of Vocational Rehabilitation.
His address is 620 North West Street, P. O. Box 26053,
Raleigh, NC 27611. His phone number is 919-733-3364.

Very truly yours,

Wig-«4w;

E. Russell Baxter, Chairman
CSAVR COMMITTEE ON RESEARCH

ERB/gkc

cc: Mr. Joseph H. Owens
Mr. George Engstrom
Mr. Claude A. Myer
Mr. Basil Scott
Mr. Joseph R. Galatti
Mr. Peter P. Griswold

"The Anunsas Department 01 Human SaMces is in compliance with T111- VI and V1101 the Chi Rights Ac: and is
operated. managed and claim some“ vmhout regard to age. M19100. handicap. sax. race. color or national odoin."

221

  

11......" Lon:1.1..no.N.1 11507 ‘516 747.5400

July 14, 1982

Mr. Peter Griswald

Director

Michigan Rehabilitation Services
bepartment of Education

Box 30010

Lansing, MI 48909

Dear Pete:

I thought I'd better clarify for you a copy of a letter
which you might have received from Russell Baxter, Chairman
of the CSAVR Committee on Research. He sent this letter to
me regarding approval for our Work Readiness Research, con-
ducted under the auspices of our Placement Research and
Training Center. Michigan was included in our requesr.

Since a doctoral student of Jim Engelkes' at nichigan State
Univers;:y is attempting to involve Micnigan DVR clients in

his disertation research. His proposed research parallels

our own research and we hope to include it in our overall

sample. We are supporting his research to some extent,

basically supplying him with the instruments that he needs.

I underscand that Bob Struthers has been discussing this
possibility with the student, Mr. Madan Kundu. If either you

or he would like further clarification from me, please let me know

I hope things are turning around for you folks in Michigan.
I have heard about the horror stories associated with the poor
economic condition you have there. I understand the difficulties
of undertaking research at a time like this, but I appreciate
your willingness to consider it as far as you have.

Sincerely,
Dave Vandergoot

Research Specialist

DV:kp
cc: Medan Kundu'//

-5--!TIES INC. - HUMAN RESOURCES SCHOC. ' HUMAN RESOURCES RESEARCH a TRAINING INSTITUTE

 

RESEABGH AM] manna 0mm :22:

e Sela-comm:

J. a. a: STOUT VOCATIONAL REHABILITATION INSTITUTE. SCHOOL OF EDUCATION 0 Winner:
UNIVERSITY OF WISCONSIN-STOUT e MENOMONIE. WISCONSIN 54751

 

 

Damec mums. PhD Charm c. Com. Rh 0, Darren 0 Cat?" Eco,
Cantu Director Research Director framing UII’OCW
-7‘SI232-1389 (715)232-2236 (7isI232-i370

July 22, 1982

Olaf Brekke

Hisconsin Division of Voc. Rehabilitation
131 Rest Wilson Street

P.0. Box 7852, 7th Floor

Madison, HI 53707

Dear Ole.

Attached is a short description of a research project on prediction
of rehabilitation success using certain variables.

The tentative research design is being utilized by Madan Kundu who is
a doctoral student at Michigan State's University Rehabilitation Education
Program. Madan is part of Stout's long-term relationship with MSU in
funding doctoral level research, keying on vocational rehabilitation
critical issues. This study is also being supported by David Vandergoot
at Human Resource Center through their Research and Training Center.

we have attempted to make the design as unobtrusive as possible, but
need your advice on the possible problems. Then, we would like to submit
it through your formal process for approval.

It is planned that Madan concentrate on the Milwaukee - Racine -
Kenosha - Madison - Janesville areas to develop a pool of 150 status 20
clients. It may be necessary to pay the clients a certain amount for in-
centive purposes and travel costs. Madan may have to make two trips; 1) to
set up the research procedures and explain them to those offices involved and
2) to conduct the testing and debriefing until 150 subjects have been tested.

He would appreciate your assistance on the technical problems associated
with approval of a research project. your evaluation of the research design
and logistical concerns.

Please call me July 26-30 if you can. I hope that the re-organization
is going smooth enough for all. Hisconsin seems to be faring better than
other states in the region during these trying economic conditions.

Sincerely,

C‘l at
Charles C. Coker. Ph.D.
Director of Research

CCC/ma cc: Madan Kundu David Vandergoot
Encl Jim Englekes Don Galvin

UNIVERSITY OF WISCONSIN-STOUT IS AN EQUAL OPPORTUNITY AND AFFIRMATIVE ACTION UNIVERSITY.

 

223

‘SMaqufIEnuflfiaua
OEPAMNT OF HEALTH AND HUMAN RESOURCES
Olen:IlluuuuUIoavmuuuweuw
OIVIsIOIv or VOCATIONAL REHABILITATION
DAVID c. mm P. O aox «371 1755 FLORIDA BOULEVARD Roger P . Gui ssinger

Govunoa BATON E LOUISIANA Rune“
(504) 342-2285

 

September 22. l982

Mr. Madan M. Kundu
809 #8 Cherry Lane
East Lansing, Michigan

Dear Mr. Kundu:

This is to confirm our earlier conversation regarding your
research project entitled "Predicting the Probability of Success
of Vocational Rehabilitation Clients Using Demographic, Locus of
Control, Hork Motivation, and Work History Variables by Cluster
Analysis."

As discussed in our conversation, I am approving your request
to utilize the Louisiana Vocational Rehabilitation Program to assist
in the proposed research project. Our staff has reviewed your
project and found that it would be of benefit to us in our program.
Mr. Everett Wright, Program Supervisor in Staff Services has
volunteered to coordinate the activities of the project.

If I can be of further assistance, please let me know.
Sincerely,

immwa

Lester E. Soileau
Executive Director

LES:afl

cc: Everett wright

2224

MICHIGAN STATE UNIVERSITY

 

uxn'enslrv comm-res 0s essences m'OLVL-vo MST LANSING - ancmom - «an
HUMAN SUBJECTS IucnmsI
m AoummATon auuomc

an, ”’4‘“ NOVCMber 2, i982

Mr. Medan M. Kundu
809 #8 Cherry Lane
East Lansing, Michigan A8823

Dear Mr. Kundu:

Subject: Proposal Entitled, "Prediction of Success or Non-Success
of Vocational Rehabilitation Clients Using Demographic,
Locus of Control, Work Motivation 5 Work History
Variables by Cluster Analysis“

 

UCRIHS review of the above referenced project has now been completed. I am
pleased to advise that the rights and welfare of the human subjects appear

to be adequately protected and the Committee, therefore, approved this project
at its meeting on November 1.1982 .

You are reminded that UCRIHS approval is valid for one calendar year. If you
plan to continue this project beyond one year, please make provisions for
obtaining appropriate UCRIHS approval prior to the anniversary date noted above.

Any changes in procedures involving human subjects must be reviewed by the
UCRIHS prior to initiation of the change. UCRIHS must also be notified
promptly of any problems (unexpected side effects, complaints, etc.) involving
human subjects during the course cf the wcrk.

Thank you for bringing this project to our attention. If we can be of any
future help, please do not hesitate to let us know.

Sincerely,

'_ ) §fl€u4agtk-

Henry E. Bredeck
Chairman, UCRIHS

HEB/jms

cc: Dr. Engelkes

APPENDIX B

INTRODUCTION TO THE COUNSELORS AND SUPERVISORS

INTRODUCTION TO THE CLIENTS

1225

INTRODUCTION TO THE COUNSELORS AND SUPERVISORS

Hello, Good Morning/ Good Afternoon.

You heard about me either from Mr. Soileau or from
Mr. Wright as I will be conducting a study using your clients.
I would like to briefly tell youuwho am.I and what I will be
doing with the study. I have been involved with rehabilitation
work for the visually handicapped children and adults since my
college days in 1963. I was involved in two international
rehabilitation research projects with the Social and Rehabilita-
tion Service as Rehabilitation Counselor. I worked with
Louisiana State University, Baton Rouge campus to help develop
graduate programs in rehabilitation counseling and special
education. My wife was a client of the Vocational Rehabilita-
tion Program at Baton.Rouge district office. At this point in
time, I come from.Michigan as a doctoral student at Michigan
State University.

The study I am about to conduct with all your help is
being sponsored by the Louisiana Vocational Rehabilitation
Program. Mr. Soileau and his staff reveiewed the study and felt
that it would be of benefit to the Louisiana Vocational Rehabili-
tation. This study will help me complete my doctoral dissertation
requirement at Michigan State University. Also this study is
part of a National Study being conducted by the National Institute
of Handicapped Research at Human Resources Center, New York.

The purpose of the study is to determine those factors
and events which contribute to the employment potential of peOple
with disabilities. The National Data from Rehabilitation
Services Administration for over a decade indicates that approxi-
mately two-third of the clients who have been accepted to
receive services are being successfully rehabilitated and the

.226

remaining one-third are closed as unsuccessful in status 28 and
30. Furthermore, nationally there is a gradual declining rate
of successful rehabilitation, 77.2% in 1970 to 64.5% in 1980.

In 1980, the lowest rehabilitation rate by state was for New
York 43.7%, followed by New Mexico 48.zz.and Michigan 48.42.

The effectiveness of the counselor, and the agency are
always in question in terms of accountability, effective utiliza-
ation of service, and wasted potential of the client. However,
you all are aware of the problem of accountability with the
major criterion being the 26 closure. It does not take into
account the counselor's effort, time, creativity, genuineness
and the rehabilitation gain by the client for those who are closed
at 28 and 30. The criteria of accountability is in question too.
Research favors to replace the present system with weighted case
closure or a point system of rehabilitation gain by the client.

At the present time, the 26 closure remains the major
criterion of accountability for the rehabilitation system. This
Study attempts to deveIOp a model that may be helpful for the
counselor and the agency to increase 26 closures.

Using demographic characteristics of client, rehabilitation
research predicted the probability of rehabilitation outcome
better than chance with average correlation 0.40's. These
Studies recommended the use of Psychological variables of
client behavior, personality stability, and adjustment as
independent predictor variables along with demographic variables
which may increase the predictive ability to around 0.60's.

Most of the research used MMPI, Rorschach, and the Edward Personal
Preference Schedule. The use of MMPI or similar personality
measures require a highly specialized skill in administration,
interpretation and is too costly to be of any practical use for
most vocational rehabilitation counselors. Rotter’s (1966)

Locus of Control Construct will be used to measure psychological
variables of the client.

£227

The present study attempts to develop a prediction model
Utilizing a linear combination of client characteristics of
biographic, demographic, locus of control, work motivation, and
past work history varibales to determine client's probability of
rehabilitation. It will be a simple, inexpensive model, easy
to administer and interpret by the counselor, to predict the °
client's probability of rehabilitation success. The model will
be able to achieve the following consequential objectives:

a) to diagnose the probable high-risk non-rehabilitants
early in the rehabilitation process;

b) to identify clients who may need additional help or
may benefit by increased or novel intervention and treatment
strategies;

c) to alert the counselor to the idiosyncratic needs,
capacities, and interests of these exceptional clients, and
facilitate some creative intervention:

d) to enhance the employment potential and reduce the
Wasted potential of the client;

e) to increase the rehabilitation rate of successful
Closure in status 26 and decrease the unsuccessful closure in
status 28 and 30;

f) to enhance effective utilization of vocational
rehabilitation resources and thereby to reduce the cost/benefit
ratio; and

g) to meet the legislative mandate of accountability
standards for the counselor as well as the agency.

I want to thank each one of you (counselor) as you have
spent a considerable amount of your time contacting the client,
explaining to them the purpose of the study, and persuading them
to participate. Without your effort, there would not be any study.
I have to seek your cooperation once again, six months from now,
for follow-up on some client characteristics.

All data collected this morning] afternoon from the clients
Will be analyzed by cluster analysis to determine what factors
or combination of factors can predict the success of rehabili—
tation. When the study is completed, I will send you a brief
summary of the major findings.

I thank you once again for your kindness shown to me
already and the previlage of meeting you. I shall be happy to
respond to any question you.may have.

228

INTRODUCTION TO THE CLIENTS

 

Hello, Good Morning/ Good Afternoon.

I am Madan Kundu. I will be collecting some infor-
mation this morning from you. I want to thank each one of
you individually for the trouble you took to come to the
district office. I really appreciate your decision to
volunteer for the study.

I have been involved with rehabilitation work for the

visually handicapped and physically handicapped children and
.adults since my college days in 1963. I worked at Louisiana
State University, Baton Rouge campus to develop a graduate
program in rehabilitation counseling and special education.
My wife was a client of the Vocational Rehabilitation
Program at Baton Rouge district office. At this point in
time, I come from Michigan as I am a doctoral student at
Michigan State University.

This study is being sponsored by Louisiana Vocational
Rehabilitation Program as they see it would benefit the
program here. Also this study is part of a National Study
being conducted by the National Institute Of Handicapped
Research at Human Resources Center, New York.

The purpose of the study is to determine those

factors and events which contribute to the employment

229

potential of people with disabilities. I will collect some
information from you this morning/afternoon on some bio-
graphic and demographic characteristics, such as, your age,
education, training, number of dependents you have, where
you live, then on some psychological aspects as to how you
perceive or believe yourself, and the society at large; your
opinion about work and the world of work; and what kind of
job you have done in the past. There is no right Or wrong
answer to these questions. It is just a matter of opinion.
So, please feel free to respond as you really feel or
believe, no matter what others might think or believe.
Whatever your responses may be, they all are confidential.
Nobody sees it. All information will be coded, analyzed and
interpreted by a computer. You can rest assured that no one
will know about your responses, not even your counselors.

One of the important aspects to remember is to please
respond to each question. If you find it difficult to
understand the question, please feel free to get my atten-
tion. If you do not answer or forget to answer one ques-
tion in a questionnaire, it will be no use for the study. I
will check your responses to make sure everything is done
the way it should be. This may take somewhere between one
to one and a half hours to complete.

How does this study affect you and what are your
benefits? There are no immediate benefits to anyone.
However, your participation will be of help for the

Louisiana Vocational Rehabilitation Program to improve

230

service delivery and future clients will benefit. At
National level, National Institute of Handicapped Research
may help change some policies or legislation that may have
some effect on all the clients.

Six months from now, I will contact you again to find
out how you are doing with respect to a job. This will be
done by one of the following ways: either by a one-page mail
questionnaire or by a 10 minute telephone interview.

Once again I thank you personally, each one of you,

for your participation to a good cause.

APPENDIX C

CONSENT AND RELEASE OF INFORMATION FORM

231

DIVISION OF VOCATIONAL REHABILITATION

STATE OF LOUISIANA

CONSENT AND RELEASE OF INFORMATION FORM

I, ,give my consent
to participate voluntarily in the research study of
predicting the probability of success of vocational
rehabilitation clients sponsored by Louisiana Vocational
Rehabilitation Program and to be conducted by Mr. Madan M.
Kundu, M.A., C.R.C. The purpose of the study is to determine
those factors and events which contribute to the employment
potential of people with disabilities. The data gathered
will be utilized in Mr. Kundu's doctoral dissertation at
Michigan State University and in a National Study being
conducted by the Institute of Handicapped Research at Human
Resources Center, Long Island, New York.

I give permission to Louisiana Vocational Rehabilita-
tion Program to allow the researcher to obtain information
from my case file. I also consent to be recontacted for the
purpose of follow-up on my progress in employment either by
letter or by telephone interview which may last approximately
10 to 15 minutes. I give this consent with the understanding
that my name or other identifying information or relevant
data will not be revealed by the researcher.

I understand that I may withdraw anytime from the
study without penalty, and that I may receive a report of
the findings upon request.

This consent is valid through December, 1983.

Signature of the client
or his/her legal representative

 

Date

 

APPENDIX D

DEMOGRAPHIC QUESTIONNAIRE

2233

DEMOGRAPHIC QUESTIONNAIRE

 

 

 

 

 

 

 

 

 

 

 

 

 

Name Date of Birth -
Address Age v
Sex: Male Female
Zip
Race: Black
Phone White
Native Indian
Marital Status:______flarried Hispanic
Separated Puerto Rican
Divorced Asian American
Widowed

Other

 

Never Married
Number of Dependents (including

self)

 

Education: 1 to 8th Grade completed ’Have you attended any

V +~
9 to 12th Grade completed or ogcgiiggaicggggg‘”al
equivalent to High School '

l to 2 years of College ______yes no
If yes, name of the
trade you learned

 

 

3 to h years of College
Graduate study

 

 

Have you worked before ? yes no

Disability : Primary Secondary
At birth Acquired At what age

 

Living conditions: Own home Rural
Rented Semi—Urban
Hospital _____prban

PLEASE DO NOT WRITE BELOW THIS LINE

 

 

 

 

 

 

Source of referral Age at referral
Date of acceptance

Employment at acceptance Earnings

Source of support:at referral now

 

 

 

Public Assistance:received before now receiving

APPENDIX E

LOCUS OF CONTROL SCALE

1234

LOCUS OF CONTROL SCALE

This is a questionnaire to find out the way in which
certain important events in our society affect different
people. Each item consists of a pair of alternatives letter
§_or 2, Please select the one statement of each pair (and
ggly gag) which you more strongly BELIEVE to be the case as far
as you're concerned. Be sure to select the one you actually
beleive to be more true rahter than one you think you should
choose or the one you would like to be true. This is a
measure of personal belief; obviously, there are no right or
wrong answers.

Please answer these items carefully but do not spend too
much time on any one item, Be sure to find an answer for every
Choice. For each numbered question make an g on the line
beside either the ‘3 or b, whichever you choose as the statement
most true.

In some instances, you may discover that you believe both
statements or neither one. In such cases, be sure to select the
One you more strongly believe to be the case as far as you're
concerned. Also, try to respond to each item independently when
making your choice; do not be influenced by your previous choices.

Items begin on the next page. Remember, select an
alternative you PERSONALLY BELIEVE TO BE MORE TRUE.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

l. a
b.

2 a.
b.

3. a.
b.

b. a.
b.

5. a.
b.

6 a.
b.

7 a.
b.

8. a.
b.

9. a.
b.

10. a.
b.

 

235

Children get into trouble because their parents punish
them too much.

The trauble with most children nowadays is that their
parents are too easy with them.

Many of the unhappy things in people's lives are partly
due to bad luck.

People's misfortmes result from the mistakes they make.

One of the major reasons why we have were is because
people don't take enough interest in politics.

There will always be wars, no matter how hard people
try to prevent them.

In the long run people get the respect they deserve in this
world.

Unfortunately. an individual's worth often passes unrec-
ognised no matter how hard he tries.

The idea that teachers are unfair to students is nonsense.

Host students don't realise the extent to which their
grades are influenced by accidental happenings.

without the right breaks one cannot be an effective leader.

Capable peeple who fail to become leaders have not taken
advantage of their opportunities.

No matter how hard you try some people just don't like you.

People who can't get others to like them don't understand
how to get along with others.

Heredity plays the major role in determining one's person-
ality.

It is one's experiences in life which determine what they're
like.

I have often found that what is going to happen will happen.

Trusting to fate have never turned out as well for me as
making a decision to take a definite course of action.

In the case of the well prepared student there is rarely
if ever such a thing as an unfair test.

Many times exam questions tend to be so unrelated to course
work that studying is really useless.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

11. a.
b.
12. a.
b.
13. a.
b.
lb. a.
b.
15. a.
b.
16. a.
b.
17. a
b.
18. a.
b.
19. a.
b.
20. a.
b.

 

236

Becoming a success is a matter of hard work, luck has
little or nothing to do with it.

Getting a good job depends mainly on being in the right
place at the right time.

The average citizen can have an influence in government
decisions.

This world is run by the few people in power, and there
is not much the little guy can do about it.

when I make plans. I am almost certain that I can make
themiwork.

It is not always wise to plan too far ahead because many
things turn out to be a matter of good or bad fortune
anyhow.

There are certain people who are Just no good.

There is some good in everybody.

In my case getting what I want has little or nothing to
do with luck.

Many time we might just as well decide what to do by
flipping a coin.

Who gets to be the boss often depends on who was lucky
enough to be in the right place first.

Getting people to do the right thing depends upon ability;
luck has little or nothing to do with it.

As far as world affairs are concerned. most of us are the
victims of forces we can neither understand. nor control.

By taking an active part in political and social affairs
the people can control world events.

Most people can't realize the extent to which their lives
are controlled by accidental happenings.

There is really no such thing as "luck."

One should always be willing to admit his mistakes.
It is usually best to cover up one's mistakes.

It is hard to know whether or not a person really likes yor.

Bow many friends you have depends upon how nice a person
you are.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

21. a.
b.
22. a.
b.
23. a.
b.
24. n.
b.
25. a.
b.
26. a.
b.
27. n
b.
28. a.
b.
29. a.
b.

 

237

In the long run the bad things that happen to us are
balanced by the good ones.

Most misfortunes are the result of lack of ability, ignor-
ance, laziness. or all three.

with enough effort we can wipe out political corruption.

It is difficult for people to have much control over the
things politicians do in office.

Sometimes I can't understand how teachers arrive at the
grades they give .

There was a direct connection between how hard I studied
and the grades I got.

A good leader expects people to decide for themselves what
they should do.

A good leader makes it clear to everybody what their jobs
are.

Many times I feel that I have little influence over the
things that happen to me.

It is impossible for me to believe that chance or luck
plays an important role in my life.

People are lonely because they don't try to be friendly.

There's not much use in trying too hard to please people,
if they like you. they like you.

There is too much emphasis on athletics in high school.
Team sports are an excellent way to build character.
what happens to me is my own doing.

Sometimes I feel that I don't have enough control over
the direction my life is taking.

Most of the time I can't understand why politicians behave
the way they do.

In the long run the people are responsible for bad govern-
ment on a national as well as on a local level.

APPENDIX F

MODIFIED LOCUS OF CONTROL SCALE

238

MODIFIED LOCUS OF CONTROL SCALE

Name

 

Directions

There are four items on this page. Each item has a pair of
statements, A and 8. Circle one statement that you believe most.
Then check with an x if the statement is much closer or slightly
closer to your point of view.

1. A.
B.
2. A.
B.
3. A.
B.
4. A.
B.

What happens to me is my own doing.

Sometimes I feel that I don't have enough control
over the direction my life is taking.

much closer slightly closer
When I make plans, I am almost certain I can make them work.
It is not always wise to plan too far ahead, because many
things turn out to be a metter of good or bad fortune anyhow.

much closer slightly closer

In my case, getting what I want has little or nothing
to do with luck.

Many times we might just as well decide what to do
by flipping a coin.

much closer slightly closer
Many times I feel that I have little influence
over the things that happen to me.
It is impossible for me to believe that chance

or luck plays an important role in my life.

much closer slightly closer

APPENDIX G

VOCATIONAL OPINION INDEX

239

FORM A

ASSOCIATES

VOCATIONAL OPINION INDEX

COPYRIGHT O AMCIA'I’ES FOR REEEARCH IN BEHAVIOR. INC.. 1073
THE SCIENCE CENTER
SCTH AND MARKET STREET.
PHILADELPHIA. PENNSYLVANIA IO! 04

 

 

 

 

 

 

Name:
Apartment No.
Street No.
City State Zip
Program:
Sex: I ( ) Male 2 ( ) Female
Age:
3. Race or Culture:
I ( ) Black
2( ) White
3 ( ) American Indian
4 ( ) Mexican-American
S ( ) Puerto Rican
6 ( ) Other:
SPECIFY
Highest Grade Completed:
l ( ) Less than 8
2 ( ) 8—1 I
3 ( ) 12 or more (include high school

eq ulvalcncy I

Are you currently participating in a
training program?

It ) Yes It )NoISKIPTOQS)
How do you get to the training center?
(check all that apply)

 

l ( ) My own car
2 ( ) Friend's car
3 l ) Public transportation
4 ( ) Walk
5 ( ) Other:
SPECIFY

Which of the followmg problems is most
likely to cause you to be late or absent
from the training program?

(check all that apply)

I ( ) Child care problems

2 ( ) Transportation problems
3 ( ) Medical problems

4 ( I Family problems

5 ( ) Money problems

6 ( ) Other:

 

SPECIFY

Identification No.

240

 

8. How many children do you have who are:

9.

ll.

(1) Under 6 years of age?
(2) 6-12 years?

 

(3) Over l2 years?

Why did you leave your last job?

I ( ) Never worked

2 ( ) Laid off

3 ( ) Lack of skill

4 ( ) Child care/family problems

5 ( ) Transportation problems

6 ( ) Medical problems

7 ( ) For a bettcrjob

8 ( ) Did not like the work

9 ( ) Did not like the people at work
10 ( ) Other:

 

SPECIFY

. Do you have any older sisters or brothers?

I ( )Yes 2 ( )No
Answer the following questions only if
you have children under I: years of age.

Who takes care of your children who are
net old enough to go to school when you
are at the training program?

I ( ) No children under school age

2 ( ) Relative/friend
3 ( ) Babysitter
4 ( ) Nursery/day care center
5 I ) Other:

 

SPECIFY

. Who takes care of your school age children

when you are at the training center?
I( ) No children overo

 

ZI ) Old enough to care for themselves
3 ( ) Relative/friend
4 ( ) Babysitter
5 ( ) School or public program
6 ( ) Other:
SPECIFY

. How did you find day care?

(check all that apply)
I ( ) I don‘t need day care
2 I ) Found it myself-
3 ( ) Training Center helped
4 ( ) Training Center provides
5 ( ) Other:

 

SPECIFY

Instructions:

241

All of the following statements are about working, or about thing that can or might happen
when people work. Some of them you may agree with and some you may disagree with.

For each statement. decide how much you agree or disagree with it. If you agree very strongly
with it and think it is definitely true about working. put a check mark beside "strongly agree".
If you agree somewhat and think that it's probably true. put a check mark beside “somewhat
agree". If you neither agree nor disagree or really don't know whether it's true or not, put a
check mark beside “neither agree nor disagree". If you somewhat disagree with the statement,
and think it‘s probably not true. put a check mark beside “somewhat disagree". If you di_sagr_e_e
strongly and are quite sure the statement is not true, put a check mark beside “strongly disagree?

 

The scale looks like this:

Example:
People who go to work become much more
beautiful.

||><|||

 

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree

strongly disagree

If you disagree somewhat with this statement and think it is probably not true, you would
put a check mark as shown.

Read over each statement and mark how much you agree or disagree with it.

l.Tliere is more choice about how to spend

.J

money earned from working than welfare
money.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

. People who work are usually treated better

than people on welfare.

strongly agree

__ somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

3.

4.

People who work are usually able to live
better than people on welfare.

strongiy agree

__ somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

Working gives people more freedom.

_ strongly agree

_ somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

—

The family will have more money if the
mother is working

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

People who work are more independent.

strongly agree

_ somewhat agree

neither agree nor disagree
__ somewhat disagree

_ strongly disagree

 

 

Working gives peOple more dignity.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

People who work usually feel better

about themselves.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

Children are more likely to finish school
and get good jobs if their parents work.

 

strongly agree

somewhat agree

neither agree nor disagree
_ somewhat disagree
strongly disagree

 

 

 

.People who work are bothered less by

their problems. partly because they have
less time to worry about them.

 

strongly agree

__ somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

 

 

14.

I5.

242

Working usually makes people better
parents and helps them do more for
their children.

strongly agree

somewhat agree

neither agree nor disagree
__ somewhat disagree
strongly disagree

 

 

 

 

.Working lets people raise their standard

of living

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

.People who work often seem more

attractive or interesting.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

Working helps keep people from be-

coming depressed or unhappy.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

Working means losing a lot of your

freedom.

 

strongly agree

_ somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

 

 

l6.

People who work have less free time to
take care of personal needs.

strongly agree

somewhat agree

__ neither agree nor disagree
__ somewhat disagree
strongly disagree

 

 

 

.Most jobs require too much time and

money traveling back and forth between
home and work.

strongly agree.

somewhat agree

neither agree nor disame
somewhat disagree
strongly disagree

.Working leaves less time to take care of

family problems.

strongly agree
somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

. Mothers who work often cannot find time

to take good care of their families.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

 

 

 

 

. Parents spend less time with their children

when they work.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

 

 

 

 

243

2l.

Working gives people less freedom.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

.Working leaves people less time for a

social life.

_ strongly agree

_ somewhat agee

_ neither agree nor disayee
_ somewhat disagree
strongly diverse

 

.Working prevents people from raising

their children as well as they might if
they were not working

_ strongly agree

.... somewhat ayee

neither agree nor disagree
_ somewhat disagree
strongly disagree

 

 

. There are not enough jobs around.

strongly agree

__ somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

 

 

 

 

. There are certain types of people for whom

there isn’t much chance to get a job.

strongly agree

__ somewhat agree

neither agee nor disagree
_ somewhat disagree
strongly disagree

 

 

 

_ . For some people no matter how hard they

try they can‘t find a job.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

27. Most jobs don‘t pay enough to make
working worthwhile.

stronmy agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

Instructions:

244

28. Most people really have less money to
spend from working than they do on
welfare.

strongly agree

somewhat agree

neither agree nor disagree
somewhat disagree
strongly disagree

On the following pages are statements about problems that might make it difficult for some
people to get a job or to keep a job. Some of them are very difficult problems and some are not.

Put a check mark on the scale under each statement to show how difficult you think the

problem would be if you had it.
The scale looks like this:

Exarnple:
Having to find something to do with
one's paycheck.

You might check “not at all" because this

extremely
V911!
somewhat
a little
not at all

extremely
very
somewhat
a little
not at all

|><||||

is not a major problem. It can be solved very

easily and is not likely to cause any difficulty at all.

I. Having a high school diploma or being able
to pass the high school equivalency test
(GED).

extremely
very
_. somewhat
a little
not at all

 

 

 

 

2. Being afraid a job won't last.

extremely

__ very
__ somewhat

 

a little
notatau

3. Not having time to take care of the house.

extremely
very
somewhat
a little
not at all

4. Finding it too hard to be organized the
way you have to be at work.

extremely
very
somewhat
a little
not at all

Havrng child care problems.

__ extremely
very
_ somewhat
__ a little

not at all

 

Having to take care of parents or relatives.

 

extremely
_ very

_ somewhat
__ a little

not at all

Having too many family and personal
problems to take care of.

extremely
very
somewhat
a little
not at all

 

. Being too ill most of the time to hold a
regularjob.

extremely
very
somewhat
a little
not at all

 

Not being able to find someone to take
care of children who will be there and
ready on time every day.

extremely
very
somewhat
a little
not at all

 

 

 

 

 

10.

245

Not being able to find someone who will
take care of children for a reasonable
amount of money.

extremely
__ very
__ somewhat
a little

__ not at all

 

 

. Having trouble getting along with all the

different kinds of people at work.

extremely
very
somewhat
a little
not at all

 

. Having trouble getting along with people

who have more experience and may look
down on new people.

extremely
very
somewhat
a little
_ not at all

 

. Having to be nice to people you may not

like on ajob.

__ extremely
__ very
_ somewhat
__ a little
_ not at all

.Not being able to raise children right

because you have to be away at work.

extremely
_ very

__ somewhat
__ a little

not at all

 

APPENDIX H

WORK HISTORY QUESTIONNAIRE

246

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nae: be. oe—xee. he e. eounouo.c_ no» can Am.aoq amen

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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NMH¢ZZOHEmMDO WMOBmH: mmoz

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APPENDIX I

FOLLOW-UP QUESTIONNAIRE - COUNSELOR
FOLLOW-UP QUESTIONNAIRE - CLIENT

248

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APPENDIX J

FOLLOW-UP LETTERS

250

MICHIGAN STATE UNIVERSITY

 

COULGE Of EDUCATION : DEPARTMENT Of COUNSELING. EAST LANSING - IICHIGAN ~ «us-1054
EDUCATIONAL PSYCHOLOGY AND SPECIAL EDUCA‘DON

Dear

Last December, while you attended Job Club, you participated
in a research study conducted by me to determine the factors and
events that contribute to successful employment. A copy of your
signed Consent and Release of Information Form is enclosed for
your information.

A part of the study is to collect follow-up data on your
progress in obtaining employment. I have been unable to reach
you by telephone. Enclosed please find the FOLLOW-UP QUESTIONNAIRE-
CLIENT. It will take only a few minutes to respond. If you are
employed now or have been employed for some time since December,
1982 please give details of the job and other information. If you
are currently employed, omit #4 and #6. Your responses to this
questionnaire and earlier ones are totally confidential. All
information provided now and earlier will be analyzed only on a
group basis.

Please return the completed questionnaire in the enclosed
self-addressed stamped envelope. It is very important that you
kindly take a few minutes to respond to the questionnaire and
return it immediately. Without this information data cannot be
analyzed and your earlier participation becomes meaningless.

If you have any question please feel free to contact me at
(517) 355-8137.

Thank you for your c00peration.

Sincerely,

Madan M. Kundu, M.A., C.R.C.
Doctoral Candidate

MMK/ sq

MSU is an Mm Anion/Equal Opportunity Institutio-

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