(c) 1984 gordon chenoweth sauer, jr
TRANSCRIPT
(c) 1984 Gordon Chenoweth Sauer, Jr.
CAREER DECISION MAKING: THE CONTRIBUTION OF INFORMATION,
VALUES, AND DECISION TRAINING TO EFFECTIVE CHOICE
by
GORDON CHENOWETH SAUER, JR., B.A., II.A.
A DISSERTATION
IN
PSYCHOLOGY
Submitted to the Graduate Faculty of Texas Tech University in
Partial Fulfillment of the Requirements for
the Degree of
DOCTOR OF PHILOSOPHY
Approved
\
August, 1984
O^t' '^ ^ ACKNOWLEDGEMENTS
My committee provided excellent guidance and editorial
assistance throughout this research. Particular apprecia
tion is extended to my chairperson. Dr. Clay George, for his
thoughtful guidance and support throughout this project and
others. I have sincerely enjoyed our collaboration during
my training at Texas Tech. Dr. Jane Winer especially de
serves thanks for setting an example for editorial excel
lence and for her administrative guidance throughout the
program. I was honored as the first recipient of the Jane
L. Winer dissertation scholarship which served as an impetus
for timely completion of my dissertation while helping to
defray some of its cost.
Ms. Betty Hunt provided superb word processing skills
by her rapid assimilation of WYLBUR/SCRIPT functions. Dr.
William Landers and Lubbock State School are thanked for
providing me an excellent employment opportunity. The work
flexibility I was allowed contributed heavily to completion
of my dissertation.
Within an environment that encouraged my ambitions, my
family of origin imbued me with enthusiasm for learning and
incentive for meeting my expectations. Most importantly, my
wife offered . me unmatched companionship and untiring
11
support, not only emotionally, but as a philanthropist, data
analyst, editor, and mother. Finally, thanks are extended
to Gordy for showing me the future and thereby exhorting
conclusion of my student tenure.
Ill
CONTENTS
ACKNOWLEDGEMENTS i i
CHAPTER
I. INTRODUCTION 1
II. REVIEW OF THE LITERATURE 5
Early Decision Theory 5 Economics 5 Psychology 8 Management 12
Current Career Decision-Making Models 15 Current Applications of Decision Theory to
Career Development 21 Descriptive Applications 22 Factors in the Career Decision-Making Process 27 Broad-Based Career Decision-Making Programs 36
Research Problem 48
III. RESEARCH DESIGN 53
Method 53 Subjects 53 Instruments 55 Program Format 57
Procedure 59
IV. RESULTS 65
Outcome of Hypothesis Testing 65 Hypothesis 1 68 Hypothesis 2 68 Hypothesis 3 69 Hypothesis 4 70 Hypothesis 5 70 Hypothesis 6 71 Summary 71
Post Hoc Analyses 72 Multivariate Analysis of Variance 72 Univariate Analysis of Variance 75
IV
V. DISCUSSION 77
Values Feedback 77 Information Feedback 81 Decision Training 82 Future Directions 97 Implications for Counseling 100 Overview 103
NOTES 106
REFERENCES 107
APPENDIX
A. DATA SHEET 121
B. VALUES FEEDBACK EXAMPLES 12 3
C. CAREER INFORMATION FEEDBACK EXAMPLES 12 5
D. DECISION TRAINING OUTLINE 129
E. STUDY SKILLS TRAINING OUTLINE 130
F. CONSENT FORM 131
G. COMPLETE FEEDBACK REPORT EXAMPLES 134
H. PROCEDURAL OUTLINE FOR EACH GROUP 139
LIST OF TABLES
1. Subject sex, ethnicity, and age distributions by treatment group 54
2. Group means (and standard deviations) for Self-Appraisal (SA), Occupational Information (01), and Problem Solving (PS) CMI-CT by treatment conditions 66
3. Number of experimental and control group subjects (n), group means (X), standard deviations (SD), degrees of freedom (df), and t ratios (t) for each of the experimental hypotheses 67
4. Multivariate analysis of variance (MANOVA) results showing the effects (Source), degrees of freedom (df), and Pillai's trace F ratios (F) 73
5. Results of analysis of variance (ANOVA) showing the effects (Source), mean square (MS), degrees of freedom (df), and F ratios (F) for each of the CMI-CT dependent variables 76
VI
LIST OF FIGURES
1. Representation of hypotheses and testing methods. 51
2. Representation of 2 X 2 X 2 completely randomized factorial design. 60
3. Interaction effect of decision-making training with information feedback in terms of Problem Solving CMI-CT. 74
VI 1
CHAPTER I
INTRODUCTION
Recent advances in cognitive psychology emphasize the
utility in training people in various mental processes
(e.g., Goldfried & Davison, 1976; Meichenbaum, 1977). In
this vein, researchers have explored the various elements
making up the process of decision making. Decision making
is a cognitive process whereby an individual receives inputs
from the environment and manipulates those inputs to arrive
at a choice output.
Decision making is similar to problem solving and much
cognitive exploration has addressed the mental machinations
involved in problem solving. Decision making is often seen
as one component of the problem-solving process (e.g.,
D'Zurilla & Goldfried, 1971). The similarity of problem-
solving elements with decision-making elements warrants a
review (which appears in Chapter II) of the problem-solving
literature to shed light on the components of the decision
making process.
Both applied and theoretical explorations of decision
making have been undertaken. Theoretical explorations
include model building. The models attempt to predict
decision making based on normative expectations of effective
choice processes. Such models take a normative or
prescriptive approach to decision making (Pitz & Harren,
1980). Subjective expected utility (Edwards, 1954),
Bayesian (Marshall, 1967), linear (Dawes & Corrigan, 1974),
and Markov (Lohnes, 1965) models have been developed to pre
scribe decisional processes. Applied explorations of deci
sion making are usually descriptive (behavioral) in nature
(Pitz & Harren, 1980) and take both reductionistic
(Atkinson, 1957; Feather, 1959; Ruber, 1980; Payne,
Braunstein, & Carroll, 1978) and global (Adelbratt &
Montgomery, 1980; Tversky, 1972) approaches to decisional
processes. Slovic, Fischoff, and Lichtenstein (1977) have
referred to these reductionistic and global methodologies as
"decomposition" and "wholistic" approaches, respectively.
For example, Payne, Braunstein, and Carroll (1978) took a
reductionistic stance when they examined predecisional be
havior. They analyzed information acquisition behavior and
verbal protocols recorded while individuals talked them
selves through decisions. Adelbratt and Montgomery (1980)
used a wholistic approach when they provided subjects with
decision-making schemata (decision rules) and assessed
choice-response distributions for hypothetical job offers
and apartment selections.
Decision making has been applied to the career
development process. The impetus to view decision-making
facilitation as an adjunct of career guidance may be par
tially attributed to Eli Ginzberg. In the 1950's Ginzberg
offered a theoretical description of the career choice pro
cess (Ginzberg, Ginsburg, Axelrad, & Herma, 1951) based on
choice as a long-term, ongoing process progressing through
particular stages at particular ages. Ginzberg et al.'s
theorizing was a break from previous thinking which concep
tualized vocational choice as a single stage, one-time
event. Others suggested critical factors influencing the
choice process, noting such dimensions as vocational maturi
ty (Super, 1955), personality type (Meadow, 1955), self-
concept (Super, 1951), and psychological needs (Roe, 1956,
1957). Further attempts to clarify the choice process fo
cused on the decision-making process itself. Such explana
tions were seen as offering insights into the essential ele
ments of career choice.
From this early thinking about career decision making
grew several models of the career choice process (Gelatt,
1962; Harren, 1979; Hilton, 1962; Kaldor & Zytowski, 1969;
Katz, 1966; Tiedeman, 1961; Tiedeman & O'Hara, 1963). The
models have attempted to define the process that is involved
in making a career choice. However, the utility of the
models for career choice facilitation has not been examined
thoroughly to date.
This investigation systematically tested the utility of
the decision-making elements included in current career
decision-making theories. Early decision theory and current
career decision-making models were reviewed to ascertain a
consensus as to what are relevant career decision-making el
ements. The elements were empirically examined to explore
their utility for training people in career decision making.
CHAPTER II
REVIEW OF THE LITERATURE
Relevant historical background about the decision mak
ing process is reviewed to lay the groundwork for an under
standing of the roots of current career decision-making mod
els. Having delineated the elements involved in career
decision making models, recent studies exploring the career
decision-making process are reviewed to provide empirical
data on what comprises career decision making. By reviewing
recent empirical investigations critically, and by relating
the empirical research to the career decision-making models,
hypotheses were developed to resolve some of the issues
raised.
Early Decision Theory
Economics
Edwards (1954) set out to bring early economic consumer
decision-making research into the realm of psychology. He
grouped economic decision making into five areas: (a) risk-
less choice theories, (b) risky choice theories, (c)
decision-making transitivities, (d) game theory, and (e)
statistical decision functions.
Riskless choice research assumes the critical notion of
"economic man." Economic man is completely informed, infi
nitely sensitive, and rational, and, as such, seeks the best
alternative. Consequently, the goal in riskless choice de
cision making is one of "maximum utility." Utility is the
positive or negative attraction of a certain item or outcome
for the individual making the choice.
Risky choice theory uses the concept of "expected val
ue." Expected value is essentially what riskless choice
theory refers to as utility, although the construct is modi
fied to include chance or probability influences. The no
tion of expectation is related to the probability the choos
er attaches to attainment of each of alternative goals. The
focus of research in this area has centered on gambling,
lotteries, and game theory. Edwards noted two factors that
influence risky choice behavior: general preferences or
dislikes for risk taking and specific preferences among
probabilities.
Transitivity, as used in choice theory, is related to
ordering preferences. Choices are transitive if A is pre
ferred to B, B is preferred to C, and therefore, A is
preferred to C. Two researchers (May, 1954; Papandreou,
1953) supported transitive operations in choice processes,
although May found more intransitivities in his study than
Papandreou. Edwards suggested dealing with intransitivities
and discovering laws to explain their occurrence. He sug
gested stochastic models as offering promise in this area.
Stochastic models mathematically account for progressive
random fluctuations in events. These models, then, would
account for conflicting stimulus dimensions upon which
choice judgments are based. In support, Vail (1954) pro
posed that "choices are dependent on utilities that oscil
late in a random manner around a mean value" (p. 405).
Game theory involves uncertainty rather than risk. The
distinction lies in uncertainty being an event in the future
to which no probability expectancies can be assigned, where
as risk can be assigned a probability value; i.e., any toss
of a coin has a .5 probability of coming up "heads." Games
employ strategies. A strategy is a series of potential re
sponses to counteract possible responses made by an oppo
nent. The object is to consider the worst possible outcome
and adopt a strategy to give the least ill effects given
that the worst outcome occurs. The overall goal is to mini
mize the maximum loss, referred to as minimax loss.
Statistical decision theory is essentially a game of
uncertainty against Nature. Gains and losses of certain
"moves" are calculated and then weighed against the loss of
being wrong vs right. The object of statistical decision
theory is to maximize the expected gain.
8
Concepts from these early economic theories permeate
much of the current career decision-making (CDM) literature.
For example, essential to career choice are a knowledge of
alternatives, the weights of those alternatives, and their
graded importance to the individual. The economic model
concept of transitivities can help explain the graded pref
erences that exist among various alternatives. Also, the
total process of decision making itself can be conceptual
ized as a series of strategies. Choices rarely are merely
available or unavailable depending on the chooser's deci
sion. In a social world others are frequently a part of
choosing and their involvement influences "pure" availabili
ty. Hence, given that certain alternatives will fluctuate
randomly (stochastic process) in availability, strategies
must be designed to cope with this fluctuating process of
choosing, and, finding the choice unavailable, being able to
expeditiously choose again. Some of the CDM models de
scribed here address this broad, multiple-goal, choice pro
cedure.
Psychology
Feather (1959) reviewed the work of early psycholo-*
gists, noting their influence on decision-making models. He
cited the work of Lewin, Dembo, Festinger, and Sears (1944),
9
Tolman (1955), Rotter (1954), Edwards (1954, 1955), and
Atkinson (1957). Each have contributed lasting concepts to
decision-making theory.
Lewin et al. (1944) theorized a "life space" with vari
ous vectors of positive and negative valence or force. The
concept of subjective probability was used to refer to the
decision-maker's perception of outcome probabilities.
Subjective probability is usually less than objective prob
ability and is inversely related to valence (a goal's at
tractiveness). A goal's positive valence decreases with in
creases in the subjective probability of attaining that
goal. Thus, Lewin et al. saw valence and subjective prob
ability as interdependent.
Tolman (1955), known for his "cognitive map" theoriz
ing, conceptualized a stimulus-stimulus (S-S) theory of be
havior. In approaching a goal a series of S-S chains ac
counts for expectancies for the goal. Tolman referred to
valences, positive and negative, for goals, but did not re
late valences to expectancies.
Rotter (1954) focused on determinants of performance
and the selection of alternative behaviors. Contrary to
Lewin et al.. Rotter theorized expectancies (measurable
probabilities held by the individual) and reinforcement
value of the goal were the determinants of behavior.
10
Furthermore, unlike Lewin et al.. Rotter believed
reinforcement value (Lewin's valence) and expectancy
(Lewin's subjective probability) were independent.
Edwards' (1954, 1955) theory of decision making was
based on notions of the utility or value of an object and
the subjective probability of obtaining that object. For
Edwards, choices were made on the basis of maximization of
subjective expected utility. Like Rotter, Edwards consid
ered utility and subjective probability to be independent.
Atkinson (1957) specifically studied risk-taking behav
ior and some of its determinants: incentive, motivation,
and subjective probability. Incentive was related to re
wards and goals, subjective probability to expectancies, and
motives to individual tendencies to avoid negative incen
tives and to approach positive incentives. Atkinson subca-
tegorized incentive and subjective probability into positive
and negative expectancies of success or failure.
Motivation, as well, was conceptualized as either toward
achievement or away from (avoidance of) failure. These
variables were believed to be independent but interactive as
they affected level of performance and willingness to take
risks. Specifically, incentive was inversely related to
subjective probability (i.e., the greater the subjective
probability of success the lower the incentive).
11
The commonality of constructs inherent in these
psychological theories is notable. Generally, the theories
incorporate concepts of goal attractiveness (labeled vari
ously as valence, reinforcement value, utility value, and
incentive) and individually held expectations for goal at
tainment (labeled variously as subjective probability and
expectancies). These constructs parallel those used in the
economic theories of choice. A point of disagreement arises
when the psychological theorists describe how goal attrac
tiveness and goal expectation interact. Rotter and Edwards
theorized the independence of these elements, while Tolman
did not specify one way or the other. On the other hand,
Lewin et al. and Atkinson described these constructs as in
teractive and inversely related; that is, with increasing
expectations or probabilities of goal attainment there is
decreasing goal attractiveness.
These constructs of goal attractiveness (value) and ex
pectation (subjective probability) which were also part of
economic decision theory will be seen again when the central
constructs of current career decision-making (CDM) models
are discussed. Several models will deal directly with
subjective probabilities and values. However, the
interactive relationships of the elements are not explicitly
incorporated into the model schema since they are viewed as
external correlates biasing the normal CDM process.
12
Management
In the early 1950's, applications of decision theory to
management and organizations gained momentum. Wilson and
Alexis (1962) neatly described this early work in terms of
"closed" and "open" decision models.
Closed decision models assumed an essential
construct--that of a "rational man"--who, operating with
given choices and outcomes, and preferences for some out
comes over others, takes action leading to the best or most
preferred consequence. This essentially describes the "eco
nomic man" referred to in the economic choice models.
"Closed" refers to the limited framework, limited complexi
ty, and limited environmental influences that impinge on the
rational being's conscious choice processes. Three states
exist in choice-consequence relationships: (a) certainty,
(b) uncertainty, and (c) risk. With certainty the outcome
is known, with uncertainty it is not and cannot be assigned
a probability; while for risk a probability for outcome can
be assigned.
Affecting the issue of probability is objective prob
ability (given an infinite number of occurrences the
frequency which an event occurs can be calculated) and
subjective probability (the interpretation of the likelihood
of a certain outcome based on perceptions of the decision
13
maker). Utility is also a crucial element of closed
decision systems. Utility, as noted in economic theory dis
cussed earlier, is the ordering of outcomes based on prefer
ences of the decision maker. Since this ordering is an in
consistent process, a stochastic (random) process is the
result. Finally, Wilson and Alexis note that
suboptimization rather than optimization is typically the
outcome in closed decisions in management. The outcome is
seen as suboptimization because global benefits for the or
ganization are relative to organizational constraints.
Therefore, what may be good or optimal for a single depart
ment may be less than perfect (suboptimal) for the entire
organization.
In contrast to closed decision models, Wilson and
Alexis viewed open decision models as more realistically de
scriptive of organizational choice processes. The cogni
tive, as opposed to the rational, nature of man was empha
sized. Hence, while cognitive limitations are recognized,
they offer an advantage as well: the individual's cogni
tions allow an "image" of the organization which includes
goals, roles, and values. This global image results in
decision-making outcomes more satisfactory to the whole than
possible with "closed" decision systems. The open decision
process proceeds in three stages: (a) conceptualizing a
14
certain aspiration or idealized goal, (b) searching a
defined, limited number of alternatives, and (c) reaching a
"satisfactory" solution.
A more sophisticated elaboration of the open model is
that of the multiple-choice open model whereby each decision
in the chain of decisions uses information from the previous
choice to improve the outcome of the next. This model ac
counts for level of aspiration adjustments based on each
successive decision. That is, satisfactory outcomes result
in raised aspiration levels while unsatisfactory outcomes
result in lowered aspiration levels. This difference be
tween that which is "aspired to" and "that which is
achieved" is referred to as attainment discrepancy. Wilson
and Alexis support use of the open decision model for man
agement referring to its deeper, richer, dynamic description
of the choice process.
Closed management decision models closely parallel eco
nomic decision theory with the use of constructs of economic
man, utility, and subjective probability. In contrast, the
deeper, broader processing described in the "open" and
"multiple-choice open" management decision models more
nearly parallels current career decision-making (CDM)
theory. In this regard, current CDM theory incorporates
feedback loops labeled as "level of aspiration adjustment"
15
in the management models. Moreover, open management models
account for overall, global planning processes characteris
tic of the cognitive, multilevel processing functions de
scribed in CDM models. Finally, management models more
closely characterize cognitive functioning than economic or
psychological decision theories do, since CDM decisions in
frequently consider all possible alternatives, and ongoing
compromise processes are critical as potential alternatives
are eliminated (Payne, 1976).
Current Career Decision-Making
Models
Several models of CDM have been developed by individual
researchers. Some of these models are quite descriptive of
the process of career decision making.
Tiedeman (1961) and Tiedeman and O'Hara (1963) devel
oped a CDM model conceptualized in terms of periods or
aspects of choice. At each stage a discrete change in deci
sion state occurs. Two overall periods of (a) anticipation
and (b) implementation-adjustment are described. The antic
ipation period includes exploration of the choice, crystal
lization of the choice, and the making of the choice itself.
The implementation-adjustment period includes "induction"
into the vocation, the transition made to the new job, and
maintenance of one's self on the job.
16
Similar to the Tiedeman model, in terms of delineating
stages of the decision-making process, is one of Harren
(1979). Four general stages of awareness, planning, commit
ment, and implementation are involved in the choice process.
The usefulness of this model as a format for career counsel
ing is apparent. While it only vaguely outlines decision
making processes, its strength resides in its potential for
orienting the counselor toward guidance areas that may fa
cilitate decision making.
Hilton (1962) made cognitive dissonance (Festinger,
1957) a critical element of his descriptive model of how ca
reer decisions are typically made. Whereas Festinger used
the term to describe the individual's need to maintain cog
nitive homeostasis following a choice, Hilton used it to de
scribe processes preceding choice whereby the individual
checks a decision for cognitive "fit." For Hilton, cogni
tive dissonance testing facilitates decision making. In
Hilton's model, decisional processes are activated by some
input from the environment. A dissonance test is made and
if dissonance is above tolerable levels, then either person
ally held premises are changed or one's behavior is altered.
This new alternative behavior is tested for dissonance. If
dissonance levels are now tolerable a decision is made, but
if not, then the system cycle begins anew.
17
Hilton borrowed from Simon (1955) when he
conceptualized choice alternatives as dichotomously classi
fiable into satisfactory vs unsatisfactory, i.e., dissonant
or consonant. Simon had suggested the notion of "satisfic-
ing as the outcome of decision making rather than "maximiz
ing." Satisficing denotes a limited capacity to handle in
formation resulting in a need to simplify choices by
processing one item at a time and declaring it satisfactory
or not. This is similar to Tversky's (1972) "elimination-
by-aspects" model cited earlier. Tversky suggested alterna
tives are assessed for a key positive aspect according to
individual preference; a choice is made, and those items not
containing that aspect are eliminated.
Hershenson and Roth (1966) have described decision pro
cess changes that occur over time once a decision has been
made. Their elaboration on post-choice processing is appli
cable to Hilton's model. Normal decision making is con
strued by Hershenson and Roth as a limiting, narrowing pro
cess in terms of availability of potential alternatives.
Concurrent with this narrowing-of-choices trend is an in
crease in the certainty that the choice made is the most
satisfactory one. This conceptualizing presents a
dissonance notion also; that is, a narrowed range of choices
results in more confidence about the decision made, thereby
18
lessening cognitive dissonance over time. Abnormal choice
processes, on the other hand, are made abruptly and impul
sively and, consequently, lack accompanying certainty about
the appropriateness of the choice.
The career decision-making model of Kaldor and Zytowski
(1969) incorporated many elements characteristic of economic
decision theory (Edwards, 1954). The authors described det
erminants of choice as (a) utility functions (the positive
or negative attraction of a certain outcome relative to the
individual making the choice), (b) resources at the indivi
dual's disposal, and (c) anticipated consequences that the
chosen occupation will make use of individual resources and
offer gratification. The choice resolution strategy is net
gain: the balance of costs (what one must forego to obtain
the choice) against output gains (what one will reap as a
consequence of that particular choice). Kaldor and Zytowski
noted that this net gain concept essentially described com
promise.
Katz (1966) developed a model for career decision mak
ing intended for direct use in guidance settings. Two com
ponents or subsystems--values and information--are numeri
cally assessed by the counselor. Counselee values are coded
and ordered as to their importance. Then, available career
information is recorded to objectify the value dimension.
19
The numerically represented values and information data are
tabulated and compared to current actual values and informa
tion associated with that occupation. This last step is the
prediction subsystem. Katz's model has been set up as a
computer guidance system to handle the numerical manipula
tions (Chapman, 1973).
Gelatt (1962) offered a three-stage decision model that
included: (a) a prediction system, (b) a value system, and
(c) a decision criterion. Gelatt borrowed from Bross (1953)
when he conceptualized information as fuel for the
decision-making process. The prediction component provides
for individually assessing alternatives and possibilities of
certain actions. The values component provides for fitting
potential alternatives and possibilities with one's needs
and preferences. The decision criterion implies the inte
gration of the prediction and value system to produce a
choice. Once an individual moves through the three stages,
either a terminal decision is reached or the investigatory
process is reactivated so that more information may be col
lected and cycled back through the system.
Mitchell, Jones, and Krumboltz (1979) while not
developing a model of decision making, provided a
conceptualization of career decision making as part of the
larger concept of social learning theory. The skills
20
required for effective career decision making were viewed as
part of the larger subset of "task approach skills." Task
approach skills include "a set of skills, performance stan
dards and values, work habits, perceptual and cognitive pro
cesses (such as attending, selecting, symbolic rehearsing,
decoding, encoding, reflecting, and evaluating responses),
mental sets and emotional responses" (Krumboltz, 1979, p.
25). Mitchell et al.'s (1979) literature review supported
the social learning acquisition of career decision-making
skills. The support offered was in terms of decision-making
models (Gelatt & Clarke, 1967; Katz, 1966; Miller &
Tiedeman, 1972) hypothesizing that the decision process was
composed of learnable skills.
These models represent current developments in career
decision-making theory. Those models which take a more
behavioral/cognitive descriptive approach have been most
widely used in applied settings. The more behavioral/
cognitive descriptive models, rather than the vaguely,
broadly descriptive ones, allow easier implementation and
outcome assessment. What follows deals with current appli
cations of career decision theory in various settings.
21
Current Applications of Decision Theory to Career Development
Core characteristics of career decision-making models
are information, personal values awareness, and decision
making strategies. Parsons (1909) and more recently
Holland, Gottfredson, and Nafziger (1975) have suggested
that effective career choice depends on self-knowledge, oc
cupational knowledge, and the ability to make appropriate
decisions using that knowledge.
Some current applications of decision-making training
address all three of the elements of values, information,
and decision making, while others deal with only some of the
elements. Some studies include outcome measures although
frequently the researchers generally describe an applied
system they have found "effective." Those studies that are
merely descriptive applications of decision-making training
are discussed first. Second, aspects and factors influenc
ing career decision making are presented. Finally, methodo
logically rigorous investigations of career decision-making
and problem-solving training are reviewed. By reviewing
current variables in career decision making and methodologi
cal considerations relevant to career interventions, it was
possible to capitalize on previous work that delineated the
salient features for study in decision making.
; • : > .
22
Descriptive Applications
Descriptive applications of career decision-making
training are typically not empirically validated methods.
Descriptive applications are similar to case studies. That
is to say, a counselor might use a method that seems to
possess clinical promise. The counselor then presents the
procedure as a suggestion for others to try.
Thoni and Olsson (1975) described a program that in
volved six decision-making stages designed to be distributed
over the four years of college. The stages are: (a)
Building Expectations, (b) Self-Assessment, (c) Exploration,
(d) Formation of Tentative Career Goals, (e) Reality
Testing, and (f) Accessing the World of Work. In the first
stage, clarification of the problem is facilitated by defin
ing what the college can offer and providing informational
experiences such as summer orientations. Self-assessment in
stage two is carried out in three-hour group sessions using
both experiential techniques and traditional assessment
techniques such as aptitude tests and vocational interest
inventories. This second stage is generally modeled after
the career development theorizing presented by Super,
Starishevsky, Matlin, and Jordaan (1963).
In the third stage, a differentiation process is
fostered based on Tiedeman's (1963) conceptions. Besides
the usual curriculum items, extracurricular experiences are
23
encouraged such as studies abroad or symposiums/courses
offered in community settings. Stage four is designed to
promote the first stages of career "crystallization"
(Ginzberg et al., 1951). Career interest identifications
are facilitated by assorted half-day excursions with profes
sionals on their jobs. In the next stage, the opportunity
for actual, ongoing work involvemeiit is available through
internships. The final stage provides the requisite skills
to obtain permanent employment. Training is provided in in
terview skills, resume writing, and persistent job hunting.
Thoni and Olsson described a seventh stage uniquely geared
to those reentering college after being out for many years.
This stage includes counseling to meet individual needs and
to facilitate a smooth reentry.
Celotta (1979) and Slater (1978) described programs
that incorporated several of the stages noted by Thoni and
Olsson (1975). Celotta described her guidance model as a
"systems approach" to decision counseling. The approach in
cludes assessing needs, specifying objectives, generating
alternative strategies, implementing choices, and evaluat
ing, revising, and selecting new options as necessary.
Slater described a counseling program that includes
awareness facilitation through various activities, and
clarification exercises using forced choice and ranking
24
methods to explore personal aspirations relative to job
characteristics and opportunities. Slater contended that
career counseling processes frequently deal with career
awareness and preparation, but overlook adequate career ex
ploration. It is intended that the exercises used by Slater
force ongoing "discriminations among initially undifferenti
ated items" (Slater, 1978, p. 135).
Krolik and Nelson (1978) described a counseling program
that incorporates the information seeking and strategy ele
ments found in decision-making models. They noted four
phases believed essential to an adequate preparation for a
career search: (a) skill identification so that personal
experiences and abilities are matched with potential employ
ment areas, (b) information collection, (c) development of
personal job search strategies, and (d) reprocessing of
goals and plans based on feedback from job search. Krolik
and Nelson were unique in implementing a specific feedback
loop in their process which may have been due to their par
ticular goal of job placement. They noted that the infre
quently reinforcing nature of job placement required ongoing
encouragement of the counselee and a thorough and constant
reevaluation of job prospects in light of tight and
constantly fluctuating job market conditions.
25
Morrill and Forrest (1970) attempted to broaden
counselors' repertoires by covering the gamut of counseling
techniques currently in use and viable as change tools.
They noted decision-making skill training as effective for
short- or long-term gains. The emphasis was on helping the
client focus on alternatives while using a rational,
thinking-through, weighting approach to decide with which
alternatives to proceed. In a similar fashion, Smaby and
Tamminen (1978) and Snodgrass and Healey (1979) described
counselor training strategies to maximize consistency in
decision-making interventions. Their delineation of train
ing strategies is consistent with other research describing
expectations of what counselees should receive from
decision-making interventions.
Sandmeyer (1980) described a workshop specifically tar
geting mid-life women possibly entering the work force. A
three day program was designed to help women identify per
sonal values, set goals, assess their abilities and inter
ests, and organize effective job search strategies.
Workshop themes focused on the objectives using group pro
cess procedures, didactic presentations, and role models.
Feedback from the participants indicated the workshop was
helpful. Additionally, it was felt more time was needed to
process the large amounts of information received.
26
Heck and Weible (1978) examined the combined success of
career development group meetings and field based explorato
ry career experiences on attitudes of college students.
Personal development seminars coupled with field experiences
were intended to interrelate self-knowledge and career
knowledge. Group meetings included exercises to increase
self-awareness and develop skills in communication, problem
solving, decision making, and goal setting. Concrete self
exploration was fostered by incorporating data from various
personality assessment instruments. An evaluation of the
program using locally developed measures suggested students
increased their career choice certainty, self-confidence,
on-the-job comfort, and willingness to accept change.
Most of the preceding studies have reiterated the ele
ments previously noted as critical to decision-making mod
els: (a) obtaining information, (b) assessing one's values
and needs, and (c) skill in performing, executing, and car
rying out decision processes. However, before comprehensive
empirical studies can be designed, counseling programs must
be broken into delineable portions so that the outcomes as
sociated with each intervention may be examined. The next
section surveys the more reductionistic approaches
researchers have taken to allow effective evaluation of
various potential factors that are critical in decision
making.
27
Factors in the Career Decision-Making Process
Payne, Braunstein, and Carroll (1978) explored informa
tion acquisition behavior by requiring subjects to visually
scan an informational matrix. Eye movements were an index
of the information seeking behavior. Using this procedure,
Payne (1976) assessed information search strategies under
varied conditions of decision-task complexity. Four infor
mation processing strategies were distinguished. Additive
or linear processing involves an individual assessment of
each component of an alternative. The components are summed
to give a value for the particular alternative. A conjunc
tive approach involves searching components of alternatives
until some minimum aspiration level is reached. An alterna
tive that exceeds the minimum expected value is chosen.
Additive difference processes involve comparing components
of alternatives across (intradimensional) alternatives. The
alternative with the highest value on all intradimensional
comparisons is selected. The elimination-by-aspects method
(Tversky, 1972) involves the selection of a critical aspect
which the to-be-chosen alternative must possess. Final
choice depends on one alternative remaining which possesses
some critical aspect the other alternatives lack. Payne
found that information search strategies varied with
28
decision task demands: more complex decisions involved
information search strategies that quickly eliminated avail
able alternatives based on intradimensional comparisons.
Simple decision tasks were handled by the more parsimonious
strategy of additive information processing. Such research
precisely addresses Thoresen and Mehrens' (1967) call for
studies exploring information packaging techniques so that
individual information processing can be maximized.
Other work on information seeking behavior shows model
ing and operant reinforcement of verbal information seeking
behavior increases the occurrence of such behavior
(Krumboltz & Thoresen, 1964). Furthermore, group and indi
vidual settings are equally effective for promoting such be
havior (Krumboltz & Thoresen, 1964).
Ryan and Krumboltz (1964), in one-to-one counseling
settings, operantly reinforced (e.g., nodding, responding
with positive acknowledgement) client decision and delibera
tion responses. Decision responses included statements that
indicated a decision had been made or a goal had been decid
ed upon. Deliberation responses were those that weighted
alternatives and/or considered factors inherent in one or
more alternatives. Using a reinforcement-extinction design,
Ryan and Krumboltz showed decision and deliberation
responses could be increased by operant reinforcement.
29
Additionally, using a projective-type story completion task,
the authors showed reinforced decision-making behavior gen
eralized to a non-counseling setting.
Regarding the specific acquisition of career informa
tion such as tested interests and aptitudes, several studies
have addressed information presentation formats. Frequently
used dependent measures of test interpretation format are
satisfaction with counseling and ability to recall test
data.
Holmes (1964) found mailing results to students rather
than one-on-one counseling produced less satisfaction with
the counseling process. However, different counseling
styles showed no differences on measures of attitude toward
the counselor and test recall ability. In a similar study,
Gustad and Tuma (1957) differentially involved students in
career test feedback. Based on a measure of self-knowledge,
they found no differences among counseling modes.
Folds and Gazda (1966) compared individual, group, and
written means of providing test interpretations to students.
Information recall measures showed no group differences al
though individual sessions were the most satisfying. No
changes in self-concept occurred for any presentation mode.
However, in an earlier study, Rogers (1954) did find
increased self-understanding following either test-centered
or self-evaluative information presentation modes.
30
Rubinstein (1978) hypothesized that previous studies on
information presentation mode indicated increased client ac-
tivity heightened test interpretation salience. He tested
whether actively involving clients in test data integration
might be an effective counseling method. Posttest measures
of test data recall, test information use, and clients' per
ceptions of the counselor showed the individual-integrative
method vs traditional group and individual methods signifi
cantly increased counseling satisfaction. No treatment ef
fect differences were found for test results recall. Degree
of vocational choice certainty also showed no differences
among treatments.
Hoffman, Spokane, and Magoon (1981) noted the equivocal
nature and methodological shortcomings of earlier studies
before they sought to explore the impact of test feedback
mode on counseling outcomes. Using no contact (profile
only), quasi-contact (audiotape and profile), and direct
counseling (counselor and profile) procedures, the direct
counseling group differed from the other two groups on three
of eight outcome measures. The direct counseling group at
tained their three preintervention, self-stated vocational
goals at posttest. However, on measures of occupational
information seeking, vocational identity, and ability to
identify future job possibilities, there were no significant
treatment differences.
31
As an offshoot of information delivery research,
current research on information feedback focuses on the
utility of recently developed computer-based career informa
tion systems. The issues that surround individual counsel
ing service delivery, such as satisfaction with counseling,
are also important for computer-based information systems.
Brandt (1977) cited work by Harren (1964) using the
Vocational Decision-Making Checklist (VDC) to assess the ef
fectiveness of a computer assisted counseling program. The
program is based on Katz's (1966) decision model that is
called SIGI for System of Interactive Guidance and
Information (ETS, 1974). SIGI includes six subsystems: (a)
values clarification, (b) locating appropriate occupational
alternatives, (c) obtaining information on various activi
ties, (d) predicting success in academic coursework, (e)
planning academic programs, and (f) career decision-making
training. Pre/posttest measurements using the VDC showed
the program was effective in facilitating decision making
for college major choice, but not for vocational choice. It
might be expected that training would affect the more immi
nent choice of picking a college major, rather than a
career. Such an assumption suggests there may be a lack of
readiness for processing career-relevant information when
other choices are more pressing at the time.
32
Further support for SIGI comes from a study by Sampson
and Stripling (1979). They compared use of the system with
and without adjunctive one-to-one counseling. In general,
they found the system was well received, sparked enthusiasm,
and was in demand. However, it was still helpful to offer
personal counseling conjointly with the computer-based as
sistance.
Myers, Lindeman, Thompson, and Patrick (1975) reported
that an average three-hour contact with a computer-based
educational and occupational exploration system enhanced ca
reer maturity based on pre/postmeasurements. Specific ca
reer maturity gains were for degree of planfulness and
knowledge/use of career resources. Measures of edu
cational/occupational information and career decision making
were no different than control group scores. Similar career
maturity gains are reported by Pyle and Stripling (1976) for
students using SIGI. The computer-based career development
group exceeded a control group on measures of the Attitude
Test of the Career Maturity Inventory (Crites, 1973). Also,
Cochran, Hoffman, Strand, and Warren (1977) showed that SIGI
facilitated college major decision making as measured by
Barren's (1964) Vocational Decision-Making Checklist.
Related to computer-based systems, in terms of the
independence given the counselee, are studies of the effect
33
of self-administered tests and results (e.g., Self-Directed
Search; Holland, 1974) on counseling satisfaction and infor
mation seeking. In general, the data support the findings
that self-directed tests are functional, informative, and
cost effective (Atanasoff & Slaney, 1980; Krivasky & Magoon,
1976).
Other factors showing some tentative relationship to
the career decision-making process are various individual
personality/cognition variables. For example, Bordin (1946)
described five features of individuals unable to make deci
sions: (a) dependence, (b) lack of information, (c) self-
conflict, (d) choice anxiety, and (e) no problem (person has
made choice but needs reassurance).
Several instruments (Chadbourne, Rosenberg, & Mahoney,
1982; Jones & Chenery, 1980; O'Neil & Ohlde, 1978) have been
developed to measure career decision-making styles.
Research using the instruments for group selection indicate
some decision-making training methodologies are differen
tially effective depending on the counselee's cognitive
style (Hesketh, 1982; Phillips & Strohmer, 1982; Rubinton,
1980).
Salomone (1982) has reiterated the important
distinction between "undecidedness" and "indecisiveness."
Undecidedness typically applies to those younger than 25
34
years of age. The person has not yet made a firm career
choice due to a lack of appropriate career-relevant informa
tion and experiences. On the other hand, indecisiveness is
typically accompanied by anxiety about choosing.
Consequently, the person is personally unable to choose due
to emotional barriers.
It seems that intervention for problems associated with
a lack of information is immediately open to decision-making
training. Training in information procurement and practice
in rationally weighting alternatives are included in most
decision-making skills development programs.
Choice anxiety may require other or additional counsel
ing techniques (Crites, 1974; Mendonca & Siess, 1976).
Furthermore, certain individuals, such as those who "lack
structure" (Osipow, Carney, Winer, Yanico, & Koschier, 1976)
and/or those who are "dependent" (Harren, 1978) may need to
be identified for specifically designed intervention strate
gies (Barak & Friedkes, 1981; Jones & Chenery, 1980).
Smaby and Tamminen (1978) described counseling proce
dures to deal with the ineffective decision strategies of
avoidance, excessive caution, and impulsiveness. Bonar and
Mahler (1976) described a college program specifically
formulated to deal with undecided students. Their program
includes exploration of alternatives and subsequent
35
consequences, along with full access to and sampling of
educationally and vocationally relevant information. Such a
program would seem to meet several of the decision-making
elements described by Bordin as lacking in "decision-locked"
individuals.
Other personality/cognition factors accumulating some
evidence for their relationship to decision making are
consistency/differentiation/congruence (Holland et al.,
1975), cognitive complexity (Winer, Cesari, Haase, & Bodden,
1979), conceptual level (Warner & Jepsen, 1979), sex role
self-concept (Moreland, Harren, Krimsky-Montague, & Tinsley,
1979), need/commitment (Dixon & Claiborn, 1981), and self-
concept/esteem/affiliation (Wigent, 1974). Lunneborg (1978)
showed sex was not a factor in career decision-making stage
or style although Krumboltz, Scherba, Hamel, and Mitchell
(1982) found differential treatment outcomes by sex and age
for their decision-making intervention.
Given these various elements involved in decision mak
ing and having looked at descriptive applications of deci
sion theory, it is possible to critically focus on broad-
based research programs that have attempted to make changes
in career decision making.
36
Broad-Based Career Decision-Making
Programs
Career decision making is a career approach skill which
is considered to be an aspect of career maturity. Career
maturity also includes competencies of realistic self-
appraisal and knowledge of the world of work. Furthermore,
career maturity reflects an attitude of career readiness ex
emplified by a willingness to explore career options and
realistically examine how personal abilities mesh with po
tential careers (Crites, 1973).
Crites has developed a scale, the Career Maturity
Inventory (CMI; Crites, 1973), to measure the skills and
attitudes believed to comprise career maturity. The Career
Maturity Inventory has been a major outcome measurement in
assessing the effectiveness of career interventions directed
at fostering career development and thereby increasing ca
reer maturity. Two major CMI test sections are intended to
collectively assess vocational maturity. The CMI Attitude
Test assesses career readiness and attitudes. The CMI
Competence Test is designed to assess the requisite skills
demonstrative of career maturity. The Competence Test in
cludes subscales of Self-Appraisal, Occupational Infor
mation, Goal Selection, Planning, and Problem Solving.
37
Winer et al. (1979) showed the CMI Competence Test
subscales of Occupational Information, Planning, and Problem
Solving were significantly correlated with measures of cog
nitive complexity. Winer et al. showed greater career ma
turity was related to a greater number of available catego
ries for processing information as measured by the Bieri
Repertory Test (Bieri, 1955).
Holland et al. (1975) demonstrated that the CMI
Attitude Test was correlated with various indices of deci
sion making. Holland et al. used quasi-performance criteria
(e.g., satisfaction with current vocational choice; whether
the student had decided upon a choice) to validate the
decision-making quality of the scale.
Jepsen and Prediger (1981) intercorrelated the CMI
Attitude Test and the Goal Selection Competence Test with
multiple career development assessment instruments. They
found the CMI Attitude Test to be only moderately correlated
with the career development measures. The Goal Selection
subscale was functionally similar to a major cluster the au
thors believed to indicate ability to conceptualize career
decisions. The evidence from these studies suggesting the
relationship of the CMI with decision-making/cognitive
qualities has contributed to its widespread use as an
outcome measure.
38
Yates, Johnson, and Johnson (1979) utilized the CMI to
assess the effectiveness of a small group, career develop
ment program designed to foster realistic decision making.
The experimental condition focused on exploring the world of
work and assessing individual interests, needs, values, and
competencies. Following the workshops, career maturity in
creased as indicated by treatment vs control group gains on
CMI Attitude, Self-Appraisal, Occupational Information, and
Goal Selection indices. Furthermore, the career maturity
changes were still apparent at follow-up (Johnson, Johnson,
& Yates, 1981). At six months, experimental group gains
were maintained on the Attitude Test and Occupational
Information Competence Test.
Also using a group setting, Sauer (Note 1) trained high
school juniors in decision making, values clarification, and
career exploration. Compared to a control group that omit
ted the decision skills training, the experimental group
showed significant differences from controls for the Problem
Solving and Planning Competence Tests of the CMI. With a
similar format combining values, information, and decision
making training, Boder (1976) showed treatment group gains
op. the CMI following training.
Ganster and Lovell (1978) showed training success as
measured by the CMI for their career development program.
39
The semester-long training program focused on career
exploration, interests assessment (SDS; Holland, 1974) and
values clarification. Compared to the control condition,
the treatment group showed significant gains on the CMI
Attitude Test and a summed Competence Test score.
Statistical results were not reported for individual
Competence Test subscales.
In an interesting departure from the use of the CMI for
outcome evaluation, Wiggins and Moody (1981) used the CMI as
a training instrument for one of their career intervention
procedures. Three other procedures Wiggins and Moody used
included (a) the Career Survey (Wiggins, 1974) and
Vocational Preference Inventory (Holland, 1978), (b) the
Self-Directed Search (Holland, 1974), and (c) traditional
career teaching and job cluster exploration. Wiggins and
Moody followed the treatments with an assessment of voca
tional identity and decision-making problems by administer
ing My Vocational Situation (Holland, Daiger, & Power,
1980). Gain scores showed groups using the Self-Directed
Search and Career Survey/Vocational Preference Inventory
were equally most effective compared to the other groups in
increasing vocational identity and decreasing decision
problems. The CMI group was next most effective while the
traditional procedure was the least effective treatment.
40
The authors noted that the cost benefit of using the SDS
should be a consideration in planning career interventions.
Johnson, Smither, and Holland (1981) used My Vocational
Situation along with a satisfaction measure to evaluate a
comprehensive, three-month, career development program. The
program included decision making, career exploration, val
ues, and temperament topics. Additionally, to provide ca
reer interest feedback, students were administered the SDS
and the Strong Campbell Interest Inventory (Campbell, 1977).
Pre/postmeasures showed increased vocational identity and
less decision-making difficulty over the course of the pro
gram. Additionally, a pretest measure had been taken to as
sess the interaction of vocational identity with treatment
effects. Johnson et al. found no differential outcomes for
students based on their program entry measure of vocational
identity.
Smith and Evans (1973) applied Bross's (1953)
decision-making strategies in a career development program.
The one-hour weekly, five-week program covered values, ca
reer information, traits, and social influences topics.
Administration of the Kuder DD (Kuder, 1968) provided tested
career interest feedback to the students. Group,
individual, and control treatment conditions were compared
using the Vocational Decision-Making Checklist (VDC; Harren,
41
1964). Students were assessed regarding their stage of
vocational development relative to their occupational and
academic major choice. The group experimental condition vs
the individual and control conditions showed increased voca
tional development for the total VDC score and separately
for occupational and academic major choice. Likewise, the
individual treatment condition was more effective than the
control condition. There were no treatment by sex interac
tions. Smith and Evans concluded that for most students,
occupational and academic major choice are synonymous.
Snodgrass and Healy (1979) used four, one and one-half
hour sessions to work with students on (a) formulation of a
vocational self-concept, (b) occupational selection with a
commitment to obtain vocationally relevant information, (c)
decision strategies, and (d) tentative career planning. The
decision-strategies session focused on five steps: goals,
alternatives, information, outcomes, and plan. In the di
dactic decision-strategy session, clients were asked to pro
vide personal, illustrative examples of the materials. Two
brief choice dilemmas also were analyzed to demonstrate the
decision process. Results from the Problem Solving scale of
the Career Maturity Inventory (Crites, 1973) showed no
significant change from pretest to posttest. However, on
measures of knowledge of decision factors, decision
42
processes, and information sources, counselees significantly
improved at posttest. It seems the mainly didactic format
of the decision-strategies session was effective as a teach
ing tool.
By assessing eighth graders on their ability to work
through hypothetical decision situations, Evans and Cody
(1969) showed a didactic decision-making training program
was more effective than nondirective and control conditions.
Judges rated both written and oral approaches to various de
cision situations. No difference was found between the
written or oral mode. Delayed outcome measures taken in a
dissimilar setting indicated training transferred to the new
situation.
In an attempt to refine the assessment of career
decision-making interventions, researchers have explored the
utility of simulated exercises in mimicking actual decision
process performance. Process oriented, mock decision-making
exercises have as a strength their utility as both training
and assessment instruments. While most researchers have
attempted to tap the process of decision making, others
have argued for assessment tools that reveal "good"
decision-making outcomes (Dilley, 1965; Krumboltz, Scherba,
Hamel, & Mitchell, 1982).
43
Krumboltz et al. designed a simulated decision-making
exercise where the criterion was matching one's values with
a fictitious job selection. Krumboltz et al. defined "good"
decision making as being able to match personal values with
the values inherent in selected alternatives. The idea that
a choice should result in an outcome that fits one's values
is in contrast to the belief that the process in decision
making is more important than the outcome (Dilley, 1967).
Krumboltz et al. found their 90 minute, rational decision
making training resulted in differential effects by communi
ty college students' age and sex. Females made significant
gains in establishing action plans (based on a paper and
pencil task measure). Superior simulated career choices
were made by females and younger males. After the training,
older males (over 21 years) made poorer simulated decisions.
Brandt (1977) reviewed various career development pro
grams potentially useful as decision-skill training modali
ties. Brandt referred to a program of Varenhorst's (1969)
that used decision-making simulation games to develop
decision-making skills. The training group walks a hypo
thetical person through life. Several "life information
booths" such as for jobs, marriage, and children are set up
to provide information and assure adequate consideration of
alternatives and outcome consequences prior to major
decisions.
44
Katz, Norris, and Pears (1978) reported on an elaborate
simulated decision-making exercise. The instrument is based
on the idea that what information a person collects and what
they do with it is indicative of effective decision making.
Bergland, Quatrano, and Lundquist (1975) and Jepsen,
Dustin, and Miars (1982) used videotaped role models in
their decision-making programming. Bergland et al. compared
the videotaped condition to a structured/didactic group and
a waiting-list control. Based on various paper/pencil and
simulated decision-making measures, Bergland et al. found no
treatment effects. However, Jepsen et al. compared a video
tape condition to field-trip, cognitive problem-solving, and
control group conditions. The eleventh graders in Jepsen et
al.'s study, like Bergland et al.'s study, showed no differ
ences compared to controls on career exploration or
decision-making measures. Comparing only the problem-
solving and field-trip groups, the problem-solving group ev
idenced greater career exploration. The career exploration
measure was based on how many information sources students
contracted to receive at the end of the program. While
Bergland et al. and Jepsen et al. seemed to develop well
designed study and outcome measures, highly simulated, novel
approaches to decision-making training assessment may
obscure real effects. Neither research team presents
45
psychometric data supporting the effectiveness of the
measures, nor does either provide traditional assessment
measures as a means of corroborating the novel assessment
exercises.
A study developed for training in problem-solving
skills focused on many of the previously delineated
decision-making constructs. Dixon, Heppner, Peterson, and
Ronning (1979) assumed decision making to be a generic skill
applicable to the first three phases of their hypothesized
five-stage, problem-solving model. The first three stages
include problem definition, goal selection, and strategy se
lection. In their study, counselors led a group training
program on the five stages for five, one and one-half hour
sessions. Outcome measures included the Problem-Solving
Inventory (Heppner & Peterson, 1982) and two subtests of the
Problem-Solving Test (Mendonca, cited in Dixon et al.,
1979): (a) a count of alternatives generated to given prob
lem situations, and (b) preference rankings of potential al
ternative responses provided for a given problem situation.
Results showed that while the quantity of generated alterna
tives did not significantly increase after the workshop, the
.quality of generated alternatives did increase (i.e.,
specific actions to be taken were clarified). Furthermore,
from the Problem-Solving Inventory it was apparent that
46
following workshop training, counselees scored lower on the
impulsive behavior factor than the control group. Hence,
the workshop seemed to provide a more thoughtful problems
approach to problems resulting in increased response quali
ty.
Mahoney (1979) described the elements of effective
problem solving using a mnemonic: SCIENCE. The proposed
factors overlap those already detailed as decision-making
elements. According to Mahoney, problem solving includes
specifying the general problem area, collecting information,
^identifying possible causes, examining possible solutions,
narrowing solutions/experimenting, comparing progress, and
extending, revising, or replacing the solutions (p. 38). By
extensive descriptions of these stages and pertinent case
illustrations, Mahoney offers a rational approach to work
through day-to-day crises and the more major decisions that
life includes.
In offering some further clarification on how decision
making fits into problem-solving models, Goldfried and
Davison (1976) suggested the following stages as comprising
the problem-solving process: (a) general orientation, (b)
problem definition and formulation, (c) generation of
alternatives, (d) decision making, and (e) verification.
Horan (1979) emphasized the similarities of problem solving
47
and decision making by suggesting that models from both
fields may be consolidated into the elements of (a) problem
conceptualization, (b) response repertoire enlargement, (c)
identification of outcomes, and (d) response selection.
Horan emphasized the contributions of problem-solving theo
rists to problem definition conceptualization and alterna
tives generation. Likewise, decision-making theorists have
had more to say about datum gathering strategies and choice
implementation/evaluat ion.
With reference to the models previously presented, and
with reference to the summary of decision-making steps de
scribed prior to this section, it is clear how much overlap
exists between the two differently labeled but similarly im
plemented cognitive skills of decision making and problem
solving. One major difference lies in the lack of personal
values clarification in problem solving. Thoresen and
Mehrens (1967), addressing this issue, note the essential
role desirability of possible outcomes (utility) and subjec
tive and objective probabilities play in choice. A major
issue they raised deals with the effect knowledge of objec
tive probabilities has on personally held subjective
probabilities. Mehrens (cited in Thoresen & Mehrens, 1967)
found that no matter what the known objective probabilities
were, subjective probabilities were higher. This is in
48
contrast to studies of gamblers' betting behavior which
demonstrate that subjective probabilities fluctuate from
less than, to greater than, objective probabilities
(Griffith, 1949; Howard, 1963). Given this information it
seems clear that awareness of personally held utilities and
subjective probabilities is critical to effective decision
making. Clarification of such personal utilities (values)
along with relevant information, and the ability to effec
tively process the information, seem to be critical for mak
ing effective decisions.
Research Problem
Career development programs are often designed to in
crease self-awareness and occupational knowledge with the
goal being to facilitate decision making on career-related
issues. Early work of Parsons (1909) and more recently
Crites (1978) suggested that knowledge of one's self, know
ledge of the world of work, and career decision-making
skills (goal selection, planning, and problem solving) are
essential competencies of realistic career decision making.
However, career development programs infrequently provide
for specific training in what comprises effective decision
making. Without the specific cognitive skills to make
decisions, it seems the career information and self-
49
awareness components of career development programs may not
be utilized. It may be that only with cognitive decision
skills can career information and self-awareness be effec
tively processed to facilitate career choice.
Cognitive interventions in counseling (Krumboltz &
Thoresen, 1969) and research exploring the utility of educa
tional models of intervention (Guerney, Stollak, & Guerney,
1970) suggest that cognitive decision skills may be taught
using an educational format. Consequently, it seems fruit
ful to compare career development programs that do and do
not contain specific cognitive decision-making training com
ponents. Such a' study would specifically clarify the neces
sity of a decision-making training component in career de
velopment programs designed to foster career choice.
Additionally, a research design that mixed all combinations
of decision-making training, career information, and values
awareness, would provide outcome data on what each component
contributed to career decision making and development.
In the research presented here, hypotheses were tested
to assess the differential effects of the independent varia
bles of decision-making training, career information, and
personal values awareness on the dependent variables of
career decision-making ability, self-awareness, and
knowledge of the world of work. Dependent variables were
50
measured by the three competencies of problem solving,
self-appraisal, and occupational information (Crites, 1978).
The three dependent variables are subscales of the
Competency Test of the Career Maturity Inventory (Crites,
1973).
In accordance with the preceding issues, the research
design for this study tested several hypotheses:
(1) A career development program that includes a deci
sion training component, when compared to a career
development program without a decision training
component (study skills control), significantly
increases (g < .05) career decision-making ability
as measured by the Problem Solving subscale of the
Career Maturity Inventory Competence Test (CMI-CT)
(cells 1, 2, 5, 6 > 3, 4, 7, 8; Figure 1).
(2) A career development program that includes deci
sion training and career information feedback is
more effective (p < .05) than a career development
program that includes only decision training, in
increasing career decision-making skills as meas
ured by the Problem Solving CMI-CT (cells 1, 5 >
2, 6).
(3) A career development program that includes
decision training, career information feedback.
51
0 2
w Q)
>H
c 0
•H -p (d B M u o O OJ
4-1 XI c; 73
•H (U 0
>-l u-l Q) 0) J-l fO
" - 0 ^ ?^
V €' 2/
O'
Decision-making training
Hypothesis 1 tests cells 1, 2, 5, 6 vs 3, 4, 7, 8,
Hypothesis 2 tests cells 1, 5, vs 2, 6,
Hypothesis 3 tests cells 1 vs 6
Hypothesis 4 tests cells 6 vs 4
Hypothesis 5 tests cells 1, 4, 5, 8 vs 2, 3, 6, 7
Hypothesis 6 tests cells 1, 2, 3, 4 vs 5, 6, 7, 8
Figure 1: Representation of hypotheses and testing methods.
52
and work values feedback is more effective than a
career development program that includes only de
cision training, in significantly increasing (p <
.05) career decision-making skills as measured by
the Problem Solving CMI-CT (cell 1 > 6).
(4) A career development program that includes deci
sion training alone is more effective than a ca
reer development program that includes only values
awareness and career information, in significantly
increasing (g < .05) career decision-making abili
ty as measured by the Problem Solving CMI-CT (cell
6 > 4).
(5) A career development program that includes career
information feedback is more effective than a ca
reer development program that excludes career in
formation feedback in significantly increasing (g
< .05) occupational knowledge as measured by the
Occupational Information CMI-CT (cells 1, 4, 5, 8
> 2, 3, 6, 7).
(6) A career development program that includes work
values feedback is more effective than a career
development program that excludes work values
feedback, in significantly increasing (g < .05)
values awareness as measured by the Self-Appraisal
CMI-CT (cells 1, 2, 3, 4 > 5, 6, 7, 8).
CHAPTER III
RESEARCH DESIGN
Method
Subjects
Subjects were college students of a large Southwestern
university enrolled in Introductory Psychology classes.
Students voluntarily signed up for the experiment to obtain
bonus points contributing to their course grade. The 88
subjects who signed up were randomly assigned to one of
eight treatment groups. The only sign-up restriction was
that subjects be age 21 or under. The mean age for the sub
jects was 19.2 years. There were 54 male (61%) and 34 fe
male (39%) subjects. Ethnic breakdown for the entire group
was 83 Caucasian (94%), 2 Hispanic (2%), and 3 other (3%).
Table 1 shows the specific sex and ethnity breakdown for
each of the eight treatment groups.
Sample size for the study was determined by using data
from a pilot study (Sauer, Note 1) to calculate statistical
power (Cohen, 1969). Based on a large effect size (d = .80
as in the pilot research) it was calculated that a sample
size of 40 would detect a large effect 80% of the time for
alpha jD < .05.
53
54
TABLE 1
Subject sex, ethnicity, and age distributions by treatment group
Sex Ethnicity Age
Grp Trtmnt M F Cauc Hisp Other Mean
A Val 6 5 11 19.7
B Inf/Dec 8 2 19.1
Control 7 10 1 19.1
D Inf/Val 10 10 19.5
Val/Dec 5 11 18.7
Dec 10 19.3
Inf/Val/ 6 Dec
11 19.2
H Inf 11 19.0
Total %
54 34 61% 39'
83 94' I o.
. 'O
3 19.2 3%
55
Instruments
Three subscales of the Competence Test of the Career
Maturity Inventory (CMI-CT; Crites, 1973) were used to as
sess competencies (Self-Appraisal, Occupational Information,
and Problem Solving) believed critical for realistic career
decision making (Crites, 1978). Administration of the
three, 20 item, multiple choice scales takes about 45 min
utes. Crites (1969) reports the subscales are independent
with subscale internal consistency reliabilities ranging
from .72 to .88, Norms available for Grade 12 (high school)
were used. Subject age was restricted to 21 or under to re
duce whatever discrepancies might occur from using a norm
group somewhat younger than the study group. Winer, Cesari,
and Haase (Note 2) showed that college students tended to
score higher on the CMI Competence Tests relative to the
grade 12 norms producing a potential ceiling effect problem.
The Problem Solving CMI-CT was especially selected as an
outcome measure because Winer et al. showed it to be the
most difficult of the Competence Tests for a similar popula
tion.
The Self-Directed Search (SDS; Holland, 1974) was used
to assess career interests. The test is a 228 item,
self-paced, self-scored appraisal of expressed career
interests and self-estimated aptitudes. Summary scores for
56
five domains (activities, competencies, occupations, and two
sets of self-estimates) contribute to a total score that re
fers to six expressed career interest areas: Realistic,
Investigative, Artistic, Social, Enterprising, and
Conventional. These global vocational orientations to the
world of work comprise the points of the hexagonally depict
ed Holland typology. The highest three Holland types are
used to represent a person's career interest code (e.g., RIC
for Realistic, Investigative, and Conventional career inter
ests). Completion of the test takes about 20 minutes.
Corrected split-half reliability for the summary scores
range from .83 to .95 (Holland, 1979). Test-retest reli
ability correlations range from .56 to .95 (Holland, 1979).
The Work Values Inventory (WVI; Super, 1968) is a 45
item inventory using a 5-point Likert-type scale to obtain
preferences for 15 work values: Creativity, Management,
Achievement, Surroundings, Supervisory Relations, Way of
Life, Security, Associates, Esthetics, Prestige,
Independence, Variety, Economic Return, Altruism, and
Intellectual Stimulation. Median test-retest reliability is
.82. Inter-item correlation is .65 (Boros, 1978).
Administration of the inventory takes about 10 minutes.
A demographic data sheet (Appendix A) was administered
to the subjects to collect relevant background information
57
concerning age, sex, ethnicity, marital status, current
career choice, decidedness with current career choice, col
lege major area, high school/college course interests, and
parental occupations. Students also answered questions con
cerning where they attended high school and what career de
velopment experiences they had previously. Additionally,
students responded to the question "What are the two most
important qualities to you about any job?"
Program Format
The program included all possible combinations of the
three elements: (a) career information, (b) values aware
ness, and (c) decision-making skills training.
Values awareness was based on results from the WVI by
providing students with feedback on their tested work values
preferences. They received a written report summarizing
their three to five highest and three to five lowest WVI
work values and the meaning of those values for the work
world (Appendix B). Their response to the question request
ing their two most important job qualities was integrated
into the feedback report.
Career information was provided by informing students
of their tested career interests based on results of the SDS
(Holland, 1974). A written report provided students with
58
their career preferences and their Holland code as it
related to their career interests (Appendix C). The report
also examined a career cluster that related to the obtained
Holland code and coincided with the expressed current career
goal (taken from the demographic data sheet). To control
which students received career information, students did not
complete the self-exploration feature of the SDS, but in
stead calculated their summary scores only.
Decision-making skills training was provided in a group
meeting that included didactic presentations and decision
making exercises. Materials developed by the College
Entrance Examination Board (Gelatt, Varenhorst, Carey, &
Miller, 1973) guided the didactic/experiential format. The
didactic format focused on how values, information, and de
cision skills work together to facilitate career choice.
Seven elements of decision making were explained and dis
cussed: defining the problem, establishing an action plan,
clarifying values, identifying alternatives, discovering
probable outcomes, eliminating alternatives systematically,
and starting action/deciding. As students explored each
step, group discussion elicited variations on decisional
processes suggested by each of the seven decision-making
steps. For example, on the step "identifying alternatives,"
free-wheeling and brainstorming were explained and
59
practiced. For the step "eliminating alternatives
systematically," the method of weighting alternatives was
presented and practiced. Appendix D contains an outline of
the decision training format.
A study skills (control training) condition used a
group training format to review study skills materials de
veloped by the Texas Tech University Counseling Center
(Appendix E). Materials were selected to focus on study
skills processes that contained no inherent decision-making
feature. The study skills condition emphasized study loca
tion factors, time scheduling strategies, study/reading
skills techniques (SQ3R), organization/record keeping,
test-taking strategies, mnemonics, anxiety management, and
simple behavior principles to guide and strengthen effective
study habits. Group discussions focused on personal strat
egies for studying and on any particular study problems
raised by the students.
Procedure
A two by two by two completely randomized factorial de
sign (Kirk, 1968) was used to test the hypotheses (Figure
.2). There were two conditions each of career information
feedback, work values feedback, and group training format.
The two career information conditions were: (a) feedback
60
M O 03 XJ T5 <D QJ
M-l
c; o
- H -t-l
u o
•H
u Q) <D U rd U
O 2
Yes No
Decision-making training
Figure 2: Representation of 2 factorial design.
X 2 X 2 completely randomized
61
report on tested career interests or (b) no feedback report.
The two work values conditions were: (a) feedback report on
tested work values preferences or (b) no feedback report.
The two training formats included either (a) decision-making
training (experimental condition) or (b) study skills train
ing (control condition).
The experiment was conducted over two sessions with a
one week interval between sessions. At the first session,
all subjects read and signed an information and consent form
(Appendix F). They completed the demographic data sheet,
the WVI, and the SDS. The first session lasted about 45
minutes.
Following the first session, results of the SDS and WVI
were scored and interpreted by the experimenter (author).
For each subject, a single spaced, one page (legal size) re
port was prepared explaining the work values preferences and
tested career interests (Appendix G). Report preparation
for each subject consumed about one to two hours, with this
time decreasing as report preparation practice increased.
Report sections that overlapped many reports were repeated
in subsequent reports which decreased report production
.time. Ten careers corresponding to the subjects SDS Holland
code were offered as examples of careers which might
interest the student. Possible careers were obtained from
62
the Dictionary of Holland Occupational Codes (Gottfredson,
Holland, & Ogawa, 1982). When Holland codes were tied so
that, for example, the student might have RIC and RIA
Holland codes, five possible careers were presented for each
code. In the few cases of rare Holland codes with no match
ing jobs, careers were presented that closely approximated
the primary/secondary code and the students expressed career
objective.
Based on the Holland code jobs and the student's ex
pressed career choice, information was provided from the
Occupational Outlook Handbook (OOH; U.S. Dept. of Labor,
1982) on the most relevant career cluster typifying their
expressed/tested career interests. Following an approach
used by Bihm (1982), the reports included OOH information
of: (a) nature of the work, (b) places of employment, (c)
training requirements, (d) employment outlook, and (e) earn
ings.
Feedback levels of career information and work values
were controlled by providing students with all, a portion
of, or none of the feedback report. For example, students
in the values-only feedback condition did not receive any
.results from their SDS administration. In turn, those in
the information-only feedback condition did not receive any
results from their WVI administration. Likewise, in the
63
no-values-no-information feedback condition, students were
not provided with either WVI or SDS results. However, at
the end of the second session, every student was given the
full feedback report so that no information was withheld.
Additionally, students were told a time when they could come
as a group to discuss with the experimenter the testing and
research results.
At the beginning of the second session, groups were
told briefly what training they would have and why. For ex
ample, the decision training group was told training in
decision-making skills would help them put together their
career information/values (depending on the condition). In
the no-work-values/no-career-information/dec ision-training
condition, they were told they would obtain decision-making
training as a skill to help them obtain their career goals.
In the study skills groups, they were told study skills were
an important part of getting ahead in life and study skills
would be explored to offer a self-help skill to ease college
work and facilitate career access. Having briefly identi
fied the rationale for the group meeting, students were pro
vided with career information and/or work values feedback
reports as appropriate to their treatment conditions.
Students were given 15 minutes to review their reports and
ask any questions. In those treatment conditions where no
64
feedback was presented, students were told the session would
start in a few minutes after students arrived and materials
were pulled together. Hence, in the no-feedback conditions
there was about a 15-minute delay before group procedures
began (Appendix H presents the step-by-step procedures for
all conditions).
Of the 3-hour final session, 15 minutes was used for
feedback report reading or waiting control and 2 hours was
used for either study skills or decision skills training,
depending on the particular treatment exposure. The remain
ing 45 minutes was used to complete outcome assessments us
ing the CMI Competence Test subscales (Self-Appraisal,
Occupational Information, and Problem Solving).
CHAPTER IV
RESULTS
Outcome of Hypothesis Testing
Table 2 presents group means and standard deviations
for each of the three dependent variable Career Maturity
Inventory-Competence Tests (CMI-CT) including Self-Appraisal
(SA), Occupational Information (01), and Problem Solving
(PS). The group means for each of the dependent variables
are the standard score values obtained by each subject using
twelfth grade norms presented in the CMI test manual
(Crites, 1973). The standard score population mean is 50
with a standard deviation of 10.
A t_ test for uncorrelated means was used to test each
of the six a priori hypotheses. All a priori hypotheses
were directional allowing one-tailed t. tests of signifi
cance. An alpha level of .05 was adopted as a maximum value
for rejecting the null hypothesis and concluding that a sta
tistically significant between-group difference existed.
Table 3 presents the group means, standard deviations, de
grees of freedom, t ratios, and probability levels for each
of the six a priori hypotheses.
65
66
TABLE 2
Group means (and standard deviations) for Self-Appraisal (SA), Occupational Information (01), and Problem Solving
(PS) CMI-CT by treatment conditions
No Information Feedback Information Feedback
No Values Feedback
Values Feedback
No Values Values Feedback Feedback
No SA
Decision 01
Training PS
52.64 (8.39)
51.82 (4.79)
44.73 (9.13)
50, (10,
51, (11,
46, (7.(
.64
.25)
.55
.84)
.36 30)
5 7 . 1 8 ( 9 . 5 1 )
5 5 . 7 3 ( 8 . 5 8 )
5 5 . 9 1 ( 7 . 2 6 )
5 3 . 0 0 ( 8 . 2 5 )
5 4 . 2 7 ( 8 . 0 4 )
5 0 , 1 8 ( 8 . 1 5 )
SA
Decision 01
Training PS
52.09 (7.58)
54.55 (6.91)
54.91 (5.15)
5 3 . 6 4 ( 5 . 9 2 )
5 5 . 2 7 ( 5 . 4 6 )
5 0 . 6 4 ( 4 , 7 8 )
5 4 . 9 1 ( 7 . 6 7 )
5 7 . 5 5 ( 7 . 4 9 )
4 9 . 7 3 ( 7 . 7 6 )
5 5 . 3 6 ( 9 . 4 1 )
5 8 . 7 3 ( 6 . 2 6 )
4 2 . 7 3 ( 5 . 6 2 )
67
TABLE 3
Number of experimental and control group subjects (n), group means (X), standard deviations (SD), degrees of freedom
(df), and t ratios (t) for each of the experimental hypotheses
Experimental Control Statistics
n X SD n X SD df t
Hyp 1 44 49.50+ 7.24 44 49.30+ 8.80 86 0.12
Hyp 2 22 46.23+ 7.52 22 52,77+ 5,30 42 -3.33**
Hyp 3 11 42.73+ 5,62 11 54,91+ 5.15 20 -5.30**
Hyp 4 11 54.91+ 5.15 11 50.18+ 8.15 20 1,63
Hyp 5 44 56,57++ 7,57 44 53.30++ 7.66 86 2.02
Hyp 6 44 53.16+++ 8.48 44 54.20+++ 8.28 86 -0.58
* 2 < .05 ** 2 < ,001 + Problem Solving CMI-CT ++ Occupational Information CMI-CT +++ Self-Appraisal CMI-CT
68
Hypothesis 1
Hypothesis 1 stated that a decision training component
in a career development program would significantly increase
(g < .05) career decision-making ability as measured by the
Problem Solving CMI-CT. It can be seen in Table 3 that me
ans for the career development program with and without the
decision training component were not significantly different
(g < .453). The mean for the decision training career de
velopment program was 49,50 and the mean for the career de
velopment program without decision training was 49,30.
Hypothesis 2
Hypothesis 2 stated that decision training and career
information in combination in a career development program
would be more effective (g < ,05) than a career development
program with only a decision training component in producing
high scores on the Problem Solving CMI-CT, Table 3 shows
that the decision training component alone was more effec
tive than the decision training/information feedback combi
nation in increasing decision-making skills as measured
by the Problem Solving CMI-CT, The mean for the group
with combined career information feedback/decision-making
training was 46,23, while the mean for the decision-making
training only condition was 52,77. A ;t ratio of -3.33 gave
69
training only condition was 52.77. A ;t ratio of -3.33 gave
a one-tailed probability value of g < .001. As is evident
by the group means and the negative t. value, the results of
this hypothesis test were in the direction opposite of that
predicted.
Hypothesis 3
Hypothesis 3 predicted that a career development pro
gram combining decision training, career information feed
back, and work values feedback would be more effective than
a career development program including only a decision
training component in significantly increasing (g < .05) de
cisional skills as measured by the Problem Solving CMI-CT.
Results shown in Table 3 indicate that, contrary to the hy
pothesis, the decision training alone condition was more ef
fective than the combined decision training/career
information/work values group in increasing decisional
skills. The Problem Solving CMI-CT mean for the decision-
training-only condition was 54.91, whereas the mean for the
combination treatment was 42.73. These means give a t. ratio
of -5.30 with a probability of g < .0001. The negative t
.value for the group comparison indicates that the hypothesis
prediction was in the direction opposite of the actual
outcome.
70
Hypothesis 4
This hypothesis stated that a career development pro
gram with decision training alone would be more effective
than a career development program including only values
awareness and career information in increasing decision
making ability as measured by the Problem Solving CMI-CT.
Based on the group means shown in Table 3, this hypothesis
was not corroborated. However, the group means were in the
predicted direction and did approach statistical signifi
cance (g * .06). The mean for the decision training alone
condition was 54.91, while the mean for the combined values
awareness/career information condition was 50.18.
Hypothesis 5
Hypothesis 5 stated that a career development program
that included career information feedback would be more ef
fective than a career development program without career in
formation feedback for increasing occupational knowledge as
measured by the Occupational Information CMI-CT. As shown
in Table 3, the mean for the career information feedback
condition was 56.57 while the mean for the career
.development program without career information feedback was
53.30. These two means were significantly different from
each other at g < .024 with a t_ ratio of 2.02.
71
Consequently, this hypothesis was corroborated and in the
direction of prediction.
Hypothesis 6
It was stated in Hypothesis 6 that a career development
program including work values feedback would be more effec
tive than a career development program without work values
feedback in increasing self-awareness based on the
Self-Appraisal CMI-CT. This hypothesis was not substantiat
ed, since the means of the two career development groups
were not significantly different from each other (g < .280).
The work values feedback condition produced a Self-Appraisal
mean score of 53.16, while the no work values feedback con
dition produced a Self-Appraisal mean score of 54.20,
Summary
Results from the a priori hypotheses tests produced
some unpredicted results. Specifically, Hypotheses 2 and 3
results were statistically significant, yet in a direction
opposite of that predicted. Hypotheses 2 and 3 were some
what similar in that each hypothesis added in a portion of
.the overall career decision-making model to be tested. It
was expected that increased decision-making skills would
accompany the addition of each of the information, values,
72
and decision-training components. With results in
directions opposite than predicted and considering the hy
pothesized additive nature of the model being tested, it was
decided that a more complex statistical analysis allowing
for an examination of interaction effects might illuminate
the research results. Furthermore, since the design of the
experiment included three dependent variables, a multivari
ate analysis of variance was chosen to explore complex rela
tionships among the groups.
Post Hoc Analyses
Multivariate Analysis of Variance
Table 4 shows the results of the multivariate analysis
of variance (MANOVA) with degrees of freedom, Pillai's trace
F ratios, and two-tailed statistical probabilities for re
jecting the null hypothesis indicated. Pillai's trace sta
tistic was used to evaluate the significance of the multi
variate tests. Pillai's trace is the more appropriate of
the various multivariate F test approximations. It is most
robust with respect to violations of the assumptions of mul
tivariate testing and it is most appropriate for the
.response distributions that typify psychological research
populations (Olson, 1976).
73
TABLE 4
Multivariate analysis of variance (MANOVA) results showing the effects (Source), degrees of freedom (df), and Pillai's
trace F ratios (F)
Source df F
Information 3,78 1.58
Values 3,78 2.37
Decision Training 3,78 1.34
Information/Values 3,78 0.98
Infor/Decision Training 3,78 8.44*
Values/Decision Training 3,78 1.56
Infor/Values/Dec Training 3,78 0.21
*g < .001
The overall three-way interaction of information, val
ues, and decision-making training did not have a statisti
cally significant effect. However, the information and
decision-making training conditions did have a statistically
significant interactive effect (F 3,78 = 8.44, g < .0001).
Figure 3 displays the information by decision-making
'interaction. Either information feedback or decision-making
training alone, but not in combination, significantly
74
H U 1
H S u
^ - v
o i H
II
CnQ C
•H >
rH 0 W
s QJ rH XI
W
• ̂ o i n
II
C ftJ (U
0 s u P^
—'
70
60
50
No Information Feedback
Information Feedback
40
No Decision-making Training
(Study skills control)
Decision-making Training
Figure 3: Interaction effect of decision-making training with information feedback in terms of Problem Solving CMI-CT.
75
increased decision-making ability as measured by the Problem
Solving CMI-CT. Combining the two conditions of decision
training or information feedback or omitting them entirely
resulted in Problem Solving scores that were near to and
slightly below the normative mean. Since the MANOVA testing
detected an overall information by decision-making training
effect, it is necessary to look at the univariate statistics
to further explore the interaction.
Univariate Analysis of Variance
Table 5 presents results of the univariate analysis of
variance (ANOVA) for each of the dependent variables of
Self-Appraisal, Occupational Information, and Problem
Solving with degrees of freedom, F ratios, and statistical
probabilities indicated. Since only the information by
decision-training interaction was significant in the MANOVA
testing, it will be the only result discussed. While the
ANOVA shows other effects to be significant, an ANOVA on
multivariate data inflates statistical probabilities result
ing in chance findings (Neher, 1967).
The information by decision-training interaction as
.found in the MANOVA analysis is substantiated in the ANOVA
by an F ratio of 22,10 (g < .0001). Either decision
training or information increased decision making as
76
measured by the Problem Solving CMI-CT although combining
the treatments failed to increase decision skills.
Results of analysis of (Source), mean square
ratios (F) for each
TABLE 5
variance (ANOVA) showing the effects (MS), degrees of freedom (df), and F of the CMI-CT dependent variables
Source MS df
Self-Appraisal CMI-CT Inf 180.41 Val 24.05 Dec 8.91 Inf/Val 14.73 Inf/Dec 7.68 Val/Dec 92,05 Inf/Val/Dec 1,64
Occupational Information CMI-CT Inf 235,64 Val 0,05 Dec 222,73 Inf/Val 0,73 Inf/Dec 0,05 Val/Dec 18,05 Inf/Val/Dec 3,68
Problem Solving CMI-CT Inf 5,01 Val 324,56 Dec 0,92 Inf/Val 140,01 Inf/Dec 1085,01 Val/Dec 70.92 Inf/Val/Dec 29.56
1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1
2 0 0 0 0 1, 0,
3. 0. 3 . 0. 0. 0, 0.
0 . 6 . 0 . 2 .
2 2 , 1 . 0 .
. 5 2
. 3 4
. 1 2
. 2 1
. 1 1 , 28 . 0 2
. 9 7 *
.00
.76
.01
.00
.31
.06
,10 , 6 1 * ,02 ,85 , 1 0 * * ,44 60
**, < ,05 < ,0001
CHAPTER V
DISCUSSION
Values Feedback
Of the six hypothesis tests, three were statistically
significant, albeit two were in the direction opposite of
that predicted. The statistical significance of Hypotheses
2, 3, and 5 supported the importance of career information
feedback and decision-making training for increasing occupa
tional knowledge and problem resolution skills. Support for
these hypotheses excluded any corroboration for the utility
of the values feedback condition in increasing self-
awareness as measured by the Self-Appraisal CMI-CT. While
values training conditions have been shown to increase
self-awareness (e.g., Yates, Johnson, & Johnson, 1979) it
may be that written feedback on paper/pencil assessed values
is insufficient to produce real changes in increasing self-
awareness. Most research on values procedures reports a
group process, experiential approach (e.g., Thompson &
Hudson, 1982) in contrast to the written report procedure
used in this study. Also, research typically uses packaged
intervention strategies (e.g., Simon, Howell, &
Kirschenbaum, 1972) with a high school population in a class
77
78
setting. Consequently, there has been limited use of values
awareness exercises with the population used here and the
procedures for inducing values awareness are not estab
lished. A typical Simon et al. program contrasts values in
tervention with some other intervention strategy that is in
tended to enhance appropriate behaviors. For example,
Thompson and Hudson (1982) used a Simon et al. values clari
fication program with ninth grade students. The values pro
gram was pitted against a behavioral, group counseling ap
proach with objectives of increasing appropriate behaviors,
reducing unhappiness, and reducing maladaptive acts.
Thompson and Hudson found that the values clarification and
behavioral group counseling programs were equal in terms of
the intended outcomes.
Values awareness is generally considered to be but one
part of a career development program (Bergland, Quatrano, &
Lundquist, 1975; Smith & Evans, 1973; Ganster & Lovell,
1978). Students are assessed in some way on their work val
ues and those work values are then tied into their career
preferences through various exercises. In the present re
search, the form of the report to control for main effect
.feedback conditions required that no integration occurred
between the work values feedback and the career information
feedback. It may be the interactive nature of values and
79
career information which contributes to enhanced
self-awareness. However, in research where a specific meas
ure (e.g., Self-Appraisal CMI-CT) has been taken of career
values changes, effects are usually not found for the values
portion of the program. However, Yates et al. (1979) used a
long-term, integrative career development program and did
find increased CMI-CT Self-Appraisal scores following inter
vention. Yates et al. may have designed a program that al
lowed sufficient time to process and integrate values aware
ness and career knowledge.
Cochran (1983) showed there was a critical difference
between implicit and explicit personally-held values. That
is, self-reported values preferences (explicit) are not ap
plied when prioritizing (implicit) career choices as to
their inherent values properties. It may be essential that
values awareness conditions center on integrating and con
fronting the interface of values preferences with career
preferences (e.g., Yates et al,, 1979). It is perhaps in
one-to-one counseling sessions that this integration could
be maximized thus accounting for the general lack of values
changes in this group study and others.
Questions regarding the need and effectiveness of
self-awareness procedures in career development models are
raised by the paucity of findings for self-awareness changes
80
accompanying other change indices of career maturity. Most
models of decision making (e.g., Harren, 1962; Gelatt, 1979)
and theories of career maturity/development (e.g., Crites,
1973) propose that self-awareness contributes to career ma
turity and effective choice. Intuitively, it seems that if
one is unable to identify personal needs and values, there
is little use in exploring career options that depend on
satisfaction with values inherent in the work.
Values are at the heart of the objective utility vs
subjective utility debate. Those who assert we operate
mainly on subjective utilities (e,g,, Edwards, 1954), high
light the biasing effect of personally held values on our
judgments. Because of the human's insensitivity to relevant
facts in making judgments, actuarial predictions typically
outpredict human judgments (e.g., Meehl, 1954). Given that
humans are biased, it seems that knowledge of personal needs
and values is essential in making choices designed to meet
one's preferences. Consequently, it seems at least theoret
ically plausible that values are a necessary part of career
development and the process of effective career choice.
Particularly, since values are such a core feature of
.decision-making models, it seems that procedures must be
developed to enhance the values awareness/career information
interface before effective career decision-making training
can be consistently carried out.
81
Information Feedback
Support for Hypothesis 5 corroborated the utility of
the career information feedback condition for increasing oc
cupational knowledge based on results from the Occupational
Information CMI-CT. This finding supports research showing
that a written report is an effective means of providing oc
cupational information based on measures of test results re
call and occupational information seeking (Folds & Gazda,
1966; Rubinstein, 1978; Hoffman, Spokane, & Magoon, 1981).
The,feedback report was extensive (Appendix G) in that it
provided SDS results of career interest, listed ten Holland
typology-based careers, and provided current Occupational
Outlook Handbook data for a career cluster relevant to
expressed/assessed career interests. One purpose of the re
port was to simulate those clinical situations where a writ
ten assessment is the outcome of a paper/pencil career in
terest evaluation and background information report. Tests
of Hypothesis 5 suggest the utility of traditional clinical
methods of background data collection and paper/pencil ass
essment as an effective means of transmitting career-
relevant information.
Based on the MANOVA results confirming a decision
making training by occupational information interaction, it
can be seen that the occupational information feedback
82
condition also contributed to increased problem-solving
skills as measured by the Problem Solving CMI-CT (Figure 3).
Sauer, Ingram, and Pierce (Note 3) and Nezu and D'Zurilla
(1979) have pointed out the systematic nature of problem
solving/decision making and the importance of such systemat
ic processing for improving decisional and problem resolu
tions skills. As can be seen from the career information
feedback report (Appendix C), the report format may have mo
deled the systematic process to be followed when faced with
a career choice. That is to say, the report systematically
described test results, defined relevant Holland dimensions,
offered careers relevant to those dimensions, described a
career cluster containing Holland-matched jobs, and provided
current occupational knowledge and resources to consult. It
may be that the report itself demonstrated decision-making
processes thereby contributing to systematic responses on
the Problem Solving CMI-CT.
Decision Training
Hypotheses 2 and 3 focused on the additive effective
ness of decision training, values feedback, and information
feedback. It was expected that as career development
program content was additively expanded, there would be
concommitent increases in problem resolution skills. This
83
theorizing was grounded in career decision-making models
emphasizing inclusion of decision skills, values awareness,
and career information as essential for effective career de
cision making (e.g., Gelatt, 1962; Harren, 1979).
Contrary to the theoretical basis for Hypotheses 2 and
3, it was shown that the decision training program alone,
rather than the additive content program, was most effective
in increasing problem resolution skills. Interestingly,
this finding was in contrast to the null effects for
Hypothesis 1, which stated that decision training vs no de
cision training would increase problem-solving skills.
Consequently, it appeared the addition of other training
components such as values awareness or career information
feedback suppressed the acquisition of decision skills.
Such a suppression theory would account for an effect of de
cision training alone, while null effects were obtained for
decision training when packaged with some other program con
tent.
Consistent with the idea of combined independent vari
ables suppressing the effects of singular interventions, the
multivariate results (Table 4) show information interacting
with decision training to produce a significant impact on
the Problem Solving CMI-CT dependent variable. Figure 3
represents this interaction which is consistent with a
84
suppression theory since information and decision training
together wash out the positive effects of either information
feedback or decision training alone for increasing problem
resolution skills. Perhaps having both conditions results
in a cognitive or informational load situation that inter
feres with processing the complete content of the career de
velopment program.
S. Streufert and his colleagues (Streufert & Driver,
1965; Streufert & Schroder, 1965; Streufert & Streufert,
1970; Streufert, Suedfeld, & Driver, 1965) conducted early
research into the relationship between information load/
utilization and cognitive complexity (conceptual structure).
His work demonstrated an inverted U-function could represent
the relationship between information handling and environ
mental conditions. That is to say, when conceptual level is
held constant, information processing capabilities increase
up to a certain asymptote and then begin to decrease, demon
strating that there is an optimum information load for indi
viduals.
Expanding on the information load issue, S. C.
Streufert (1973) explored the impact of information
.relevance on decision making. Streufert found that
relevance level affected what she called respondent (simple)
decisions.. The effect of relevance level on respondent
85
decisions followed the inverted U-function that had been
previously found in information load studies when the
relevant/irrelevant dimension of information was not held
constant. However, for integrated (complex) decisions, ef
fective decision making was increased with increased infor
mation relevance when information load was held constant.
Consequently, it seems that complex decisions may continue
to be processed effectively with increasing information rel
evance, while simple decisions are affected by relevant in
formation in the same way that they are affected by informa
tion load. S. Streufert's results take on a special meaning
when considering that the dimensions of respondent and inte
grated decision making are typically correlated at +.79.
Consequently, it would have been expected that as respondent
decisions decreased, so would have integrated decisions de
creased.
S. Streufert and Schroder (1965) explored another
aspect of information load when they looked at abstract vs
concrete conceptual level in terms of decision-making task
responses. They did not control the relevance-irrelevance
dimension of information but instead sought to see if there
would be a difference between abstract and concrete
conceptual structure in terms of decision-making strategies
under varying information load conditions. Results showed
86
that maximal decision-making performance peaked for both
abstract and concrete thinkers at the same level of informa
tion load and had similar curvilinear characteristics (in
verted U-function). However, the two curves were offset
with the abstract thinkers consistently demonstrating a
greater number of integrated decisions throughout the infor
mation load conditions. Streufert, Suedfeld, and Driver
(1965) replicated these results in a wider study that in
cluded dimensions of delegated and self-initiated informa
tion search. On the information search variables, informa
tion search tended to drop off as information load
increased, with complex persons tending to continue their
information search at higher rates than the conceptually
simple persons. It seemed that the complex individuals con
tinued their information search with the expectation that
they had not yet received the critical information needed to
make an effective decision.
More recent studies of cognitive complexity have
evolved in the vocational psychology literature in terms of
differential occupational information use relative to the
cognitive complex-simple dimension as measured by the Bieri
.(1955) construct grid. The Bieri method uses the construct
grid to rate the number of meaning categories available to
individuals when classifying information. This is in
87
contrast to the abstract-simple measures used in the
Streufert studies that judged conceptual level based on re
sponses to sentence completion tests. Cognitive complexity
studies in terms of occupational information, focus on the
evaluation of whether cognitively complex individuals with a
more flexible response repertoire differentially use career
information compared to cognitively simple individuals.
Bodden (1970), in an early study of the cognitive complexity
issue, did not find support for his hypothesis that cogni
tively complex individuals might make career choices more
appropriate to their abilities.
Bodden and James (1976) looked at the effect of occupa
tional information giving on cognitive complexity. They
found that occupational information tended to reduce cogni
tive complexity. To more clearly differentiate among infor
mation modes, Haase, Reed, Winer, and Bodden (1979) provided
positive, negative, and mixed occupational information to
subjects. Results showed that the positive occupational in
formation tended to reduce cognitive complexity. However,
negative or mixed information retarded the trend toward re
duced cognitive complexity, Cesari, Winer, Zychlinski, and
.Laird (1982) attempted to replicate Bodden and James' (1976)
work and were not able to do so. However, Cesari (1983) in
follow-up research used positive, negative, and mixed
88
occupational information, as in the Haase, Reed, Winer, and
Bodden (1979) study, and found that positive information
tended to decrease cognitive complexity which was consistent
with the Haase et al. data. Cesari suggested that the posi
tive information given in her study could be classified as
information load rather than information relevance based on
S. C. Streufert's (1973) work. Her information consisted of
packaged excerpts on careers taken from the Occupational
Outlook Handbook.
These studies on cognitive complexity and information
load have particular meaning for the present research since
it was found that when career information and decision
making skill training were combined, subject performance was
not improved in terms of problem resolution skills.
Consequently, the subtractive, rather than additive, nature
of the present study suggests that some information/
cognitive load condition may have been operative.
In the present study it is difficult to define the in
formation feedback reports in terms of their relevance-
irrelevance as S. C. Streufert (1973) used that term. In
Streufert's work, the tasks were simulated war games with
integrated (complex) decisions being represented by
strategies such as bombing a munitions plant, landing
personnel to secure the plant, and parachuting in troops to
89
capture the withdrawing enemy. in contrast, respondent
(simple) decisions were, for example, bombing a dump, para
chuting into enemy lines, or organizing an assault on an air
base (Streufert, Clardy, Driver, Karlins, Schroder, &
Suedfield, 1965; Streufert, Kliger, Castore, & Driver,
1967). These decision-making measures are in sharp contrast
to the problem-solving and decision-making processing called
for by the Problem Solving CMI-CT. Furthermore, information
load in the war game simulation was controlled by how many
war-feedback reports were given to the team members per unit
of time. The relevance dimension was regulated by the util
ity of the report for the current war process vs the report
being some statement about world affairs that was specifi
cally unrelated to the mock war. Consequently, it is only
possible at a theoretical level to draw some relationship
between S. Streufert's and S. C. Streufert's research on in
formation load and relevance.
Fortunately, the work on cognitive complexity within
the career development domain tends to lend some consensual
validity to the information load/relevance data offered by
the war simulation research. It seems the career
information reports that were provided the subjects in this
research could be classified as relevant information in
terms of its personal relevance for the subjects since the
90
report was written for them based on their tested career
interests. However, relative to the problem-solving task
that was required of them on the Problem Solving CMI-CT, the
career information reports could be seen as information
load. That is, the career information report did not con
tribute to problem resolution skills as needed in the
Problem Solving CMI-CT. In this sense, adding career infor
mation feedback to decision training acted as an information
load in terms of the tasks called for on the Problem Solving
CMI-CT. Since the career information feedback condition in
creased occupational knowledge based on the Occupational
Information CMI-CT, the report could be viewed as informa-
tionally relevant for the occupational knowledge measure,
but informationally irrelevant and redundant to the problem
solving measure. Consequently, each of the independent
variables of information and decision training could be con
sidered information load in terms of the other variable.
The additive nature of career information and problem solv
ing may have appeared in this study if one overall outcome
measure had been used. The outcome measures used in this
study were specific to the independent variables in that
there was no overall measure of career development.
Perhaps, if some overall career development measure such as
the Attitude Test of the Career Maturity Inventory had been
91
used, then the additive nature of the model would have been
apparent.
Related to the information load issue, it is important
to consider the time frame of the career development program
presented here compared to other programs of career inter
vention. Particularly if cognitive overload is a concern,
it may be that a cognitive load situation would be less of a
problem with expanded time frames for training. In consid
ering how much time may be required to teach career
decision-making skills, Newell (1980) suggests that
problem-solving skills are one of the building blocks of
cognition. That problem solving, which is closely related
to decision making, is a building block might suggest that
it would take a considerable period of time to teach
decision-making skills. Anderson (1982), using computer
models, has estimated that acquiring a cognitive skill takes
at least 100 hours. However, from another perspective, the
fact that problem solving is a building block suggests it
may only be a matter of refreshing problem-solving cogni
tions so that individuals can reaccess the cognitive media
tors of problem solving (e.g., Meichenbaum, 1977).
Generalizability might also be an issue here, in that
teaching career decision-making skills may merely allow
people to bridge the gap between the established cognitive
92
building blocks of problem solving and the use of those
cognitive skills in career choice. Training for decision
skills generalization or cognitive mediator reactivation
would consume much less time than that required to teach de
cision making as a new cognitive skill.
In addition to the theoretical data that suggest cogni
tive skill training requires considerable time, it is possi
ble to empirically examine decision-making training programs
to look at successes in terms of program time frames.
Descriptive studies of career development programs, as
discussed in the first chapter, do not precisely duplicate
the content of the career decision-making skills training
program presented here or in other studies, although overall
they show success and, consequently, can be examined for the
time frames used to obtain that success. Typically, general
career development programs are of an extensive nature in
that they are designed to continue for days (e.g.,
Sandmeyer, 1980), for a semester (e.g., Krolik & Nelson,
1978; Heck & Weible, 1978), or for the entire college term
(Slater, 1978; Thoni & Olsson, 1975). Baker and Popowicz
(1983) reviewed 18 career education programs that covered a
broad range of treatment programs. The programs they
reviewed went somewhat afield from the career decision
making content of the program described in this study but
93
looking at the contact hours used in the studies will give
some indications of what time frames might be appropriate
for career decision-making programs. Furthermore, it might
be expected that these time frames would underestimate the
actual time frames necessary for training in decision-making
skills, considering the specific skill development expected
from a decision-making training program vs general career
attitude changes expected to result from career education
intervent ions.
Baker and Popowicz report an average of 7.41 contact
hours for their 18 reviewed studies. Outcome measures var
ied greatly; six used some measure of the Career Maturity
Inventory, four used some other kind of test, and eight used
some sort of survey or simulation measure. Among these dif
fering outcomes, there was an overall positive effect for
83% of the studies. Of the 118 effect sizes, the mean aver
age effect size was d = .50 which is indicative of a medium
effect size (Cohen, 1969).
Looking at studies that relate more specifically to the
career decision-making training program presented here,
there are great variances in times spent in programs.
Johnson, Smither, and Holland (1981) describe career
development seminars which used My Vocational Situation
(Holland, Daigar, & Power, 1980) as the outcome measure. It
94
was found that the vocational identity of the students was
increased following the 25 hours of workshop time. Wiggins
and Moody (1981) mixed traditional testing and career clus
ter survey procedures with experiential group treatments to
compare career exploration approaches. Using My Vocational
Situation as the outcome measure, after 37.5 hours of con
tact it was found that all approaches except the traditional
career cluster exploration approach produced positive gains
on the pre/post-outcome comparison. Evans and Cody (1969)
met with subjects for five sessions, although the time per
session was not specified. A guided and nonguided group
were compared to a control group on decision-making skill
attainment. It was found that the guided group met the
posttest criterion within the five-day training period, al
though the nonguided group did not. Jepsen, Dustin, and
Miars (1982) trained adolescents in problem-solving skills
using guided field trips and cognitive and behavioral
problem-solving training. On measures of career exploration
and career decision making, the eight hours of training
yielded no significant differences when the three experimen
tal conditions were compared to the control group. Both
problem-solving groups did score higher than the field-trip
group on career exploration, but not on career decision
making.
95
Krumboltz, et al. (1982) used a 1.5-hour rational
decision-making training workshop to increase the quality of
simulated career decisions on an overall measure of career
decision making using the Career Decision Simulation, it was
found that females and younger males made superior career
choices. Older males showed deteriorated decision making
after training. Females improved in their abilities in es
tablishing an action plan. Smith and Evans (1973) used a
five-week training program with unspecified time periods per
week to compare group guidance and individual counseling in
facilitating vocational development. The programs included
specific vocational decision training using Bross's (1953)
strategies, career information exploration, and career in
terest assessment. Small and large group meetings through
out the five-week program focused on personal trait check
lists and the relationship of those traits to expressed
occupational interests. Harren's (1964) Vocational
Decision-Making Checklist and a counseling assessment form
were used to evaluate the program effects. Results showed
no treatment-by-sex interaction. Both treatment conditions
exceeded the control condition on the outcome measures.
Furthermore, the experimental group guidance treatment was
more effective than the individual counseling treatment as
measured by the Vocational Decision-Making Checklist. The
96
counseling assessment scores indicated no significant
treatment nor sex effect. Ganster and Lovell (1978) provid
ed 15 hours of a career development seminar to freshmen and
sophomore management students. Using the Career Maturity
Inventory Attitude Test and a summary score calculated by
summing the five Competence Tests of the CMI, they found
significant positive changes in both career attitudes and
career competencies.
Career development programs and, specifically, career
decision-making formats cover a wide range of treatment
types, contact time frames,and outcome evaluations. Based
on the review of these studies, it can be seen that most ca
reer interventions are of an extended nature. Only
Bergland, Quatrano, and Lundquist (1975) (from Baker &
Popowicz, 1983 review) and Krumboltz et al. (1982) used
brief interventions similar to the time frames reported in
this study (2.5 hours and 1,5 hours, respectively),
Bergland, Quatrano, and Lundquist had their 2,5-hour program
spread over five weeks which would further enhance
processing/absorption time for the training, Krumboltz et
al, used the overall Career Decision Simulation measure for
their study which was previously suggested as possibly a
more suitable measure of the kind of program used in this
research. No research has specifically trained for
97
decision-making skills while providing career information
feedback and values feedback, as in the present study, in
such a short time period. Consequently, given the cognitive
load research presented previously and the nature of the
present research, it seems that cognitive load could have
well been a factor which might have been lessened by an ex
tended or distributed training time.
Future Directions
Overall, findings from this research suggest possibili
ties for changing the ways career decision-making abilities
are assessed or changing career decision-making models. In
terms of career decision-making assessment, it seems that
some overall assessment needs to be made of the processes
that contribute to career decision making: values, informa
tion, and decision skills. Some researchers have suggested
career decision-making simulation measures that concurrently
assess values/career congruency, information search strat
egies, and decision-making skills. For example, Krumboltz
et al. have developed the Career Decision Simulation meas
ure which requires students to integrate personal values
preference ratings with career choice priorities. The
Career Decision Simulation moves in the direction of making
a broad assessment of career decision-making skills while
98
specifically measuring what are theoretically considered to
be the components of career decision making.
One problem with the Career Decision Simulation scheme
is its weakness in assessing decision-making abilities.
Krumboltz et al. assume that good decisions are based on
congruent value/career preferences in contrast to typical
decision-making thinking that suggests the decision-making
process is the important factor rather than outcome.
Additionally, it seems the Career Decision Simulation deper
sonalizes the career decision-making process which removes
personal decision making from the realm of individual impact
and, hence, seems to automatically reduce how much congru
ence there may be between what one wants in a career and
what kind of career is eventually chosen.
In contrast to the Career Decision Simulation,
Varenhorst's (1969) "life games" assessment strategy evalu
ates decision-making effectiveness based on strategy and
process evaluations rather than outcome. Judges rate sub
ject strategy effectiveness as they walk through mock deci
sion situations asking questions and securing what informa
tion they need. Holmstrom and Beach (1973) used a procedure
similar to Krumboltz et al. in their study of subjective «
expected utilities. Subjects were required to relate career
preferences to expectations of outcomes for achieving those
99
careers. Another broad-based decision skills assessment
measure could be a summary score of the Competence Tests of
the Career Maturity Inventory. Such an overall score of ca
reer maturity competencies might be a better measure of ca
reer decision-making ability than the individual scales.
Ganster and Lovell (1978) attempted to use such a measure
when they compared group outcomes based on a summary score
of the five CMI-CT subtests. However, until normative data
are presented that validate the use of such a summary score
for measuring career decision-making abilities, it seems
senseless to apply such an outcome measure. Needless to
say, instrumentation is a critical need for career
decision-making research.
In terms of career decision-making models, it seems
they may misrepresent factors and factor relationships in
cluded in career decision-making processing. The research
presented here indicates that there is a definite career in
formation feedback by decision skills training interaction
in terms of problem-solving abilities acquisition. The ca
reer awareness model of Wise, Charner, and Randour (1976)
represents the interactive nature of values, knowledge,
preferences, and self-concepts as part of the career choice
process. However, traditional models of career decision
making (e.g., Gelatt, 1962; Harren, 1979) have presented
100
discrete stage models of choice. Perhaps career information
within career development programs should not be represented
as an entity, but rather as something distributed throughout
training. Furthermore, based on the null results for the
values feedback condition in this study, and the suggestion
that previous research has typically presented values as
part of a program that integrates values awareness with ca
reer information, it seems that values also need to be inte
grated with career information and decision skills. Perhaps
career information and values could be presented in such a
way that they were processing steps in a decision-making
scheme. It is in this vein that Krumboltz et al.,
Varenhorst, and Holmstrom and Beach have developed their
comprehensive, broad-based assessment schemes while attempt
ing to specifically measure what are theoretically consid
ered to be the components of career decision making.
Implications for Counseling
The research presented here suggests several practical
implications for how career interventions might be deliv
ered. First, the interaction finding suggests either
decision training or extensive career interests feedback
reporting alone may be sufficient to produce wanted
increases in problem resolution skills and occupational
^mimmesT'^'
101
knowledge. Particularly, if the counseling goal is one of
brief intervention, then using either but not both proce
dures would be warranted. Perhaps training in decision
skills and career interest reporting combined in an extended
program would produce greater or longer lasting career ma
turity changes; however, the results of this study provide
no answers regarding the impact of a longer training pro
gram. The research here attempted to find an effective,
brief intervention strategy combining information, values,
and decision training to promote career decision-making com
petencies. The fact that the values part of the program as
delivered was ineffective for the intended outcome further
suggests career counselors choose between decision training
and career interest feedback when intervention is to be di
rected at increasing problem resolution skills and knowledge
about the world of work.
Second, in designing career interventions based on
these results the population of study must be considered. A
program modeled after this research may be ineffective for
high school students considering the reading demands of the
reports and the program time frame. The feedback reports
.were extensive, particularly the complete ones, which
required reading speeds of 50-60 words per minute
considering the alloted 15 minute report review time.
102
Additionally, the multisyllabic words and potentially
unknown words (e.g., philologist, mannequin, underwriting;
see Appendix G) could present further reading difficulties
and concomitant comprehension reductions. While the SDS is
an appropriate test for high school-level students (Holland,
1979), reports generated from it may need to be simplified
and offer more explanation along with increased reading time
to facilitate comprehension.
Finally, while the students for this study were random
ly selected which offered greater experimental control, the
generalizability of these results are limited when consider
ing interventions for students seeking career counseling.
Students seeking career counseling may have different needs
than nonseeking students who may be presented with packaged
career development programs (Cesari et al., 1982; Dixon &
Claiborn, 1981; Mendonca & Siess, 1976). The program out
lined in this research was developed as a brief, broad-based
career development intervention such as might be contained
in school curricula or special school programs (e.g., "ca
reer day"). Whether part or all of the program described
here may be useful for career counseling seekers remains
.unanswered. However, the powerful impact of the information
feedback and decision training conditions suggests their
potential for increasing career approach skills albeit more
103
individualized, extensive procedures may be necessary as
adjunctive interventions for addressing the various problems
which career counseling seekers present.
Overview
The interaction of career information with decision
making skills was a major finding in this study which was
addressed by suggesting that information load/relevance and
training time frames are critical when planning career de
velopment programs. The present data and a review of the
decision-making research suggest the need for integrating
decision-making skills, career information, and values
awareness to produce effective career choice skills. Models
of career decision making represent decisional processes,
career knowledge, and personal values awareness as discrete
entities as if they were additive in nature and sequential
in attainment. Gelatt (1962) represents a feedback loop in
his decision-making model which offers only mild clarifica
tion of the integrative nature of decision processes, infor
mation, and values. The model of Wise, Charner, and Randou
(1976) demonstrates the interactions among career awareness
.components but offers no schema for skill acquisition,
integration, and training. In general, the decision-making
models oversimplify the process of career choice and the
104
acquisition of the knowledge and skills necessary for
effective choosing. Krumboltz et al.'s (1982) outcome meas
ure that assesses the interface between personal values and
career preferences moves in a direction of integrating some
of the basic elements of career development and maturity.
Any new model that evolves out of current research in
career decision making must take into account the interac
tive nature of values, information, and decision processes.
The question seems to be what schema can best represent the
way or ways these variables interface in promoting effective
decision making. Research at this time needs to more clear
ly differentiate the interactive nature of information, val
ues, and decision processes in career development so that a
model characterizing process interactions may be developed.
It may be possible to borrow from the general decision
making literature in terms of schematizing such a model and
developing it to the level of complexity required.
Based on the research presented here, it would also
seem apparent that career decision-making models need to in
dicate processing levels in terms of a simple-complex con
ceptual dimension so that conceptual level may be partialed
.out in terms of its relationship to decision making. For
example, cognitively complex individuals could receive more
career-relevant information in one chunk or practice highly
105
integrated decision making in earlier training stages. The
research presented here raises issues regarding the inter
face of decision training with conceptual level, information
relevance, and training time frames. A research direction
might be to use Gelatt's decision-making model and the ma-
terials available (Gelatt et al., 1973) to format a
decision-making training program for cognitively simple vs
cognitively complex individuals. Furthermore, the informa
tion feedback reports used in this research could be varied
in terms of their content so that they presented negative,
positive, and mixed information feedback which would address
issues raised by Bodden and James (1976), Haase et al,
(1979), and Cesari (1982). In turn, based on such initial
studies, some changes could be made in program format to al
low integration of career information and decision making
which would be expected to capitalize on learning decision
making skills due to their more personal association with
the individual's career interests, thereby perhaps reducing
cognitive load by increasing information relevance.
Addressing these issues in further career decision-making
research designs hopefully will eventually foster a model
that more appropriately represents the career decision
making process while offering a significant heuristic for
further research on the career choice process.
NOTES
1. Sauer, G. C. An experimental test of vocational decision-making skill training. Paper presented at the meeting of the Texas Psychological Association, Houston, November, 1981.
2. Winer, J. L., Cesari, J., & Haase, R. F. Career maturity among Holland types. Paper presented at the meeting of the Southwestern Psychological Association, San Antonio, 1979.
3. Sauer, G. C., Pierce, R., & Ingram, J. Assessment and intervention strategies for educationally/vocationally undecided students. Workshop presented at the meeting of the Southwestern Psychological Association, San Antonio, April, 1983.
106
REFERENCES
Adelbratt, T., & Montgomery, H. Attractiveness of decision rules. Acta Psycholoqica. 1980, 4^, 177-185.
Anderson, J, R. Acquisition of cognitive skill. Psychological Review, 1982, 89, 369-406.
Atanasoff, G. E., & Slaney, R. B. Three approaches to counselor-free career exploration among college women. Journal of Counseling Psychology, 1980, 21_, 332-339.
Atkinson, J, W, Motivational determinants of risk-taking behavior. Psychological Review, 1957, ̂ , 359-372.
Baker, S. B,, & Popowicz, C, L, Meta-analysis as a strategy for evaluating effects of career education interventions. Vocational Guidance Quarterly, 1983, ̂ , 178-186,
Barak, A. , & Friedkes, R. The mediating effects of career indecision subtypes on career-counseling effectiveness. Journal of Vocational Behavior, 1981, 2^, 120-128.
Bergland, B. W., Quatrano, L. A., & Lundquist, G, W, Group social models and structured interaction in teaching decision making. Vocational Guidance Quarterly, 1975, Z4, 28-36,
Bieri, J. Cognitive complexity-simplicity and predictive behavior. Journal of Abnormal and Social Psychology, 1955, 52, 263-268.
Bihm, E. M. The effect of thematic organization upon the long term recall of occupat ional information. Unpublished doctoral dissertation, Texas Tech University, 1982.
Bodden, J. L, Cognitive complexity as a factor in appropriate vocational choice. Journal of Counseling Psychology, 1970, 1/7, 364-368,
Bodden, J, L., & James, L. E. Influence of occupational information giving on cognitive complexity. Journal of Counseling Psychology, 1976, 2^, 280-282.
107
108
Boder, C. K. The relationship of work values, career decision-making and career maturity of eleventh grade students in a joint vocational school. (Doctoral dissertation, The Ohio State University, 1976). Dissertation Abstracts International, 1976, 37, 4661A-5402A. —
Bonar, J. R., & Mahler, L. R. A center for "undecided" college students. Personnel and Guidance Journal, 1976, S±, 481-484.
Bordin, E. S. Diagnosis in counseling and psychotherapy. Educational and Psychological Measurement, 1946, 6, 169-184.
Boros, O. K. (Ed.). The eighth mental measurements yearbook. Highland Park, NJ: Gryphon Press, 1978.
Brandt, J. D. Model for the delivery of career development programs by the college counseling center. Journal of Counseling Psychology, 1977, 2A, 494-502.
Bross, I. D. Design for decision. New York: Macmillan, 1953.
Campbell, D. P. Manual for the Strong-Campbell Interest Inventory T325 (Merged form). Stanford, CA: Stanford University Press, 1977.
Carey, M. A., & Weber, L, J, Evaluating an experience-based career education program. Vocat ional Guidance Quarterly, 1979, 22, 216-222.
Celotta, B. The systems approach: A technique for establishing counseling and guidance programs. Personnel and Guidance Journal, 1979, 5^, 412-414.
Cesari, J. P. Effect of four types of occupational information on cognitive complexity in decided and undecided students. Unpublished doctoral dissertation, Texas Tech University, 1982.
Cesari, J. P., Winer, J. L., Zychlinski, F., & Laird, I. 0, Influence of occupational information giving on cognitive complexity in decided versus undecided students. Journal of Vocational Behavior, 1982, 21, 224-230,
109
Chadbourne, J. W., Rosenberg, J. H., & Mahoney, J. T. Defining decision making patterns: A tool for career-life planning. Personnel and Guidance Journal, 1982, 60, 51-53.
Chapman, W. Counselor's handbook for SIGI. Princeton, NJ: Educational Testing Service, 1973.
Cochran, D. J., Hoffman, S. D., Strand, K. H., & Warren, P. M. Effects of client/computer interaction on career decision-making processes. Journal of Counseling Psychology, 1977, 2A, 308-312.
Cochran, L. Implicit versus explicit importance of career values in making a career decision. Journal of Counseling Psychology, 1983, 3̂ ^ 188-193.
Cohen, J. Statist ical power analysis for the behavioral sciences. New York: Academic Press, 1969,
Crites, J, 0. The maturity of vocational attitudes in adolescence. Iowa City: University of Iowa Press, 1969.
Crites, J. 0. Career Maturity Inventory. Monterey, CA: CTB/McGraw-Hill, 1973.
Crites, J. 0. Career counseling: A review of major approaches. The Counseling Psychologist 1974, 4̂ (3), 3-23.
Crites, J. 0. Career Maturity Inventory; Administration and use man lal. Monterey, CA: CTB/McGraw-Hill, 1978.
Dawes, R. M., & Corrigan, B. Linear models in decision making. Psychological Bulletin, 1974, 81., 95-106.
Dilley, J. S. Decision-making ability and vocational maturity. Personnel and Guidance Journal, 1965, 44, 423-427.
Dilley, J. S. Decision-making: A dilemma and a purpose for counseling. Personnel and Guidance Journal, 1967, 45, 547-551.
Dixon, D. N., & Claiborn, C. D. Effects of need and commitment on career exploration behaviors. Journal of Counseling Psychology, 1981, 2^, 411-415.
110
Dixon, D. N., Heppner, P. P., Peterson, C. H., & Ronning, R. R. Problem solving workshop training. Journal of Counseling Psychology, 1979, 2^, 133-139.
D'Zurilla, T. J., & Goldfried, M. R. Problem solving and behavior modification. Journal of Abnormal Psychology, 1971, 7^, 107-126.
Educational Testing Service. A computer based system of interactive guidance and information. Princeton, NJ: Author, 1974.
Edwards, W. The theory of decision making. Psychological Bulletin, 1954, 5^, 380-417.
Edwards, W. The prediction of decision among bets. Journal of Experimental Psychology, 1955, 50^, 201-214.
Evans, J. R., & Cody, J. J. Transfer of decision-making skills learned in a counseling-like setting to similar and dissimilar situations. Journal of Counseling Psychology, 1969, 26. 427-432.
Feather, N. T. Subjective probability and decision under uncertainty. Psychological Review, 1959, 6̂ , 150-164.
Festinger, L, A theory of cognitive dissonance. Stanford, CA: Stanford University Press, 1957,
Folds, J. H., & Gazda, G. M. A comparison of the effectiveness and efficiency of three methods of test interpretation. Journal of Counseling Psychology, 1966, 23, 318-324.
Ganster, D. C., & Lovell, J. E. An evaluation of a career development seminar using Crites' Career Maturity Inventory. Journal of Vocational Behavior, 1978, 13, 172-180.
Gelatt, H. B. Decision making: A conceptual frame of reference for counseling. Journal of; Counseling Psychology, 1962, 9, 240-245.
Gelatt, H. B., & Clarke, R. B. Role of subjective probabilities in the decision process. Journal of; Counseling Psychology, 1967, ]A, 332-341.
Gelatt, H. B., Varenhorst, B., Carey, R., & Miller, G, Decisions and outcomes. New York: College Entrance Examination Board, 1973,
Ill
Ginzberg, E., Ginsburg, S. W., Axelrad, S., & Herma, J. I. Occupational choice: An approach to theory. New York: Columbia University Press, 1951.
Goldfried, M. R., & Davison, G. C. Clinical behavior therapy. New York: Holt, Rinehart & Winston, 1976.
Gottfredson, G. D. , Holland, J. L., & Ogawa, D. K. Dictionary of Holland occupational codes. Palo Alto, CA: Consulting Psychologists Press, 1982.
Griffith, R. M. Odds adjustments by American horse-race betters. American Journal of Psychology, 1949, 62, 290-294. —
Guerney, B. G., Jr., Stollak, G. E., & Guerney, L. A format for a new mode of psychological practice. The Counseling Psychologist, 1970, ̂ , 97-104.
Gustad, J. W., & Tuma, A. H. The effects of different methods of test introduction and interpretation on client learning in counseling. Journal of Counseling Psychology, 1957, 4, 313-317.
Haase, R. F., Reed, C. F., Winer, J. L,, & Bodden, J, L. Effects of positive, negative, and mixed occupational information on cognitive and affective complexity. Journal of Vocational Behavior, 1979, 2^, 294-302.
Harren, V. A. A study of the vocational decision-making process among college males. Unpublished doctoral dissertation. University of Texas, 1964.
Harren, V. A. The vocational decision-making process among college males. Journal of Counseling Psychology, 1966, 21, 271-277.
Harren, V. A. Assessment of Career Decision-Making (ACDM): Counselor/instructor guide. Unpublished manuscript. Southern Illinois University at Carbondale, 1978.
Harren, V. A. A model of career decision making for college students. Journal of Vocational Behavior, 1979, 14, 119-133.
Heck, S,, & Weible, T, Study of 1st year college student perceptions of career choice based on exploratory field experiences. The Journal of Educational Research, 1978, 71, 272-276,
112
Heppner, P. P., & Peterson, C. H. The development and implications of a personal problem-solving inventory. Journal of Counseling Psychology, 1982, 2^, 66-75.
Hershenson, D. B., & Roth, R. M. A decisional process model of vocational development. Journal of Counseling Psychology, 1966, U, 368-370.
Hesketh, B, Decision-making style and career decision-making behaviors among school leavers. Journal of Vocational Behavior, 1982, ̂ , 223-234,
Hilton, T, L. Career decision making. Journal of Counseling Psychology, 1962, 3, 291-298.
Hoffman, M. A., Spokane, A. R., & Magoon, T. M. Effects of feedback mode on counseling outcomes using the Strong Campbell Interest Inventory: Does the counselor really matter? Journal of Counseling Psychology, 1981, 28, 119-125.
Holland, J. L. The Self-Directed Search. Palo Alto, CA: Consulting Psychologists Press, 1974.
Holland, J, L. Vocational Preference Survey, Palo Alto, CA: Consulting Psychologists Press, 1978,
Holland, J, L, The Self-Directed Search: Professional manual, Palo Alto, CA: Consulting Psychologists Press, 1979.
Holland, J. L,, Daiger, D, C., & Power, P. G. My Vocational Situation. Palo Alto, CA: Consulting Psychologists Press, 1980.
Holland, J. L,, Gottfredson, G. D., & Nafziger, D, H, Testing the validity of some theoretical signs of vocational decision-making ability. Journal of Counseling Psychology, 1975, 2/2, 411-422,
Holmes, J, E. The presentation of test information to college freshmen. Journal of Counseling Psychology, 1964, 12, 54-58.
Holmstrom, V. L., & Beach, L. R. Subjective expected utility and career preferences. Organizational Behavior and Human Performance, 1973, 2^^ 201-207,
Horan, J. J, Counseling for effective decision making. North Scituate, MA: Duxbury Press, 1979.
113
Howard, T. C. The relation between psychological and mathematical probability. American Journal of Psychology, 1963, l^, 335-338.
Huber, 0. The influence of some task variables on cognitive operations in an information-processing decision model. Acta Psychologica, 1980, 4^/ 187-196.
Jepsen, D, A., Dustin, R., & Miars, R. The effects of problem-solving training on adolescents' career exploration and career decision making. Personnel and Guidance Journal, 1982, ̂ , 149-153.
Jepsen, D. A., & Prediger, D. J, Dimensions of adolescent career development: A multi-instrument analysis. Journal of Vocational Behavior, 1981, 25. 350-368,
Johnson, J, A,, Smither, R., & Holland, J. L. Evaluating vocational interventions: A tale of two career development seminars. Journal of Counseling Psychology, 1981, 28, 180-183,
Johnson, N,, Johnson, J,, & Yates, C, A 6-month follow-up on the effects of the vocational exploration group on career maturity. Journal of Counseling Psychology, 1981, 28, 70-71.
Jones, L. K., & Chenery, M. F. Multiple subtypes among vocationally undecided college students: A model and assessment instrument. Journal of Counseling Psychology, 1980, 21_, 469-477.
Kaldor, D. R., & Zytowski, D. G. A maximizing model of occupational decision-making. Personnel and Guidance Journal, 1969, ^ , 781-788,
Katz, M. A model of guidance for career decision-making. Vocational Guidance Quarterly, 1966, 2^^ 2-10.
Katz, M,, Norris, L,, & Pears, L. Simulated occupational choice: A diagnostic measure of competencies in career decision making. Measurement and Evaluation in Guidance, 1978, 1^, 222-232,
Kirk, R, E, Experimental design: Procedures for t\\e_ behavioral sciences. Belmont, CA: Brooks/Cole, 1968,
Krivasky, S, E., & Magoon, T. M. Differential effects of three vocational counseling treatments. Journal of Counseling Psychology, 1976, 23_, 112-118.
114
Krolik, J. J., Sc Nelson, J. L. Non-academic career counseling for graduate students. Personnel and Guidance Journal, 1978, ̂ , 126-127.
Krumboltz, J. D. A social learning theory of career decision making. In A. M. Mitchell, G. B. Jones, & J. D. Krumboltz (Eds.), Social learning and career decision making. Cranston, RI: Carroll Press, 1979.
Krumboltz, J. D., Scherba, D. S., Hamel, D. A., & Mitchell, L. K. Effect of training in rational decision making on the quality of simulated career decisions. Journal of Counseling Psychology, 1982, ̂ , 618-625.
Krumboltz, J. D., & Thoresen, C. E. The effect of behavioral counseling in group and individual settings on information-seeking behavior. Journal of Counseling Psychology, 1964, 32. 324-333.
Krumboltz, J. D., & Thoresen, C. E. (Eds.). Behavioral counseling: Cases and techniques. New York: Holt, Rinehart, & Winston, 1969.
Kuder, G. F. Kuder DD: Occupational Interest Survey. Chicago: Science Research Associates, 1968.
Lewin, K., Dembo, T., Festinger, L., & Sears, P. S. Level of aspiration. In J. McV. Hunt (Ed.), Personality and the behavior disorders. New York: Ronald Press, 1944.
Lohnes, P. R. Markov models for human development research. Journal of Counseling Psychology, 1965, ]^, 322-327.
Lunneborg, P. W. Sex and career decision-making styles. Journal of Counseling Psychology, 1978, 25^, 299-305.
Mahoney, M. J. Self-change: Strategies for solving personal problems. New York: W. W. Norton, 1979.
Marshall, J. C. Bayesian decision. Journal of Counseling Psychology, 1967, 2i' 342-345.
May, K. 0. Transitivity, utility, and aggregation in preference patterns. Econometrica, 1954, 2/2, 1-13.
Meadow, L. Toward a theory of vocational choice. Journal of Counseling Psychology, 1955, 2, 108-112.
Meehl, P. E. Clinical vs statistical prediction. Minneapolis: University of Minnesota Press, 1954.
115
Meichenbaum, D. Cognitive-behavior modification: An integrative approach. New York: Plenum, 1977.
Mendonca, J. D., & Siess, T. F. Counseling for indecisiveness: Problem-solving and anxiety-management training. Journal of Counseling Psychology, 1976, 23, 339-347.
Miller, A. L., & Tiedeman, D. L, Decision-making for the 70's: The cubing of the Tiedeman paradigm and its application in career education. Focus on Guidance, 1972, 5, 1-15.
Mitchell, A, M. Relevant evidence. In A. M. Mitchell, G. B. Jones, & J. D. Krumboltz (Eds.), Social learning and career decision making. Cranston, RI: Carroll Press, 1979.
Mitchell, A. M. , Jones, G. B., & Krumboltz, J. D. (Eds,), Social learning and career decision making. Cranston, RI: Carroll Press, 1979,
Moreland, J. R., Harren, V. A., Krimsky-Montague, E,, & Tinsley, H, E, A. Sex role self-concept and career decision making. Journal of Counseling Psychology, 1979, 26, 329-336.
Morrill, W. H., & Forrest, D. J. Dimensions of counseling for career development. Personnel and Guidance Journal, 1970, 49, 299-305.
Myers, R. A,, Lindeman, R. H., Thompson, A. S., & Patrick, T. A. Effects of educational and career exploration system on vocational maturity. Journal of Vocational Behavior, 1975, 6, 245-254.
Neher, A. Probability pyramiding, research error and the need for independent replication. Psychological Record, 1967, r7, 257-262.
Newell, A. Reasoning, problem solving, and decision processes: The problem space as a fundamental category. In R. Nickerson (Ed.), Attention and performance VIII. Hillsdale, NJ: Erbaum, 1980.
Nezu, A., & D'Zurilla, T. J. Effects of problem definition and formulation on decision making in the social problem-solving process. Behavior Therapy, 1981, 1^, 100-106.
116
Olson, C. L. On choosing a test statistic in multivariate analysis of variance. Psychological Bulletin, 1976, 83, 579-586. —
O'Neil, J. M., & Ohlde, C. Career Factor Checklist. Unpublished manuscript, 1978 (available from James M. O'Neil, Department of Counseling and University Counseling Center, University of Kansas, Lawrence, Kansas 66045).
Osipow, S. H., Carney, C. G., Winer, J. L., Yanico, B. J., & Koschier, M. The Career Decision Scale (3rd rev.). Columbus, OH: Marathon, 1976.
Papandreou, A. G. An experimental test of an axiom in the theory of choice. Econometrica, 1953, 21. 477. (Abstract)
Parsons, F. Choosing a vocation. Boston: Houghton Mifflin, 1909.
Payne, J. W. Task complexity and contingent processing in decision making: An information search and protocol analysis. Organizat ional Behavior and Human Performance, 1976, 16, 366-387,
Payne, J, W,, Braunstein, M, L., & Carroll, J. S, Exploring predecisional behavior: An alternative approach to decision research. Organizational Behavior and Human Performance, 1978, 22^, 17-44,
Phillips, S. D., & Strohmer, D, C. Decision-making style and vocational maturity. Journal of Vocational Behavior, 1982, 20' 215-222.
Pitz, G. F., & Harren, V. A. An analysis of career decision making from the point of view of information processing and decision theory. Journal of Vocational Behavior, 1980, 2§' 320-346.
Pyle, K. R., & Stripling, R. 0. The counselor, the computer, and career development. Vocational Guidance Quarterly, 1976, 2^, 11-lb.
Roe, A. The psychology of; occupations. New York: Wiley, 1956.
Roe, A. Early determinants of vocational choice. Journal of Counseling Psychology, 1957, 4_, 212-217.
117
Rogers, L. B. A comparison of two kinds of test interpretation interview. Journal of Counseling Psychology, 1954, 1, 224-231.
Rotter, J. B. Social learning and clinical psychology. New York: Prentice-Hall, 1954.
Rubinstein, M. R. Integrative interpretation of vocational interest inventory results. Journal of Counseling Psychology, 1978, 2^, 306-309.
Rubinton, N. Instruction in career decision making and decision-making styles. Journal of Counseling Psychology, 1980, 21_, 581-588.
Ryan, T. A., & Krumboltz, J. D. Effect of planned reinforcement counseling on client decision-making behavior. Journal of Counseling Psychology, 1964, 11, 315-323.
Salomone, P. R, Difficult cases in career counseling: II--The indecisive client. Personnel and Guidance Journal, 1982, 6^, 496-500,
Sampson, J, P,, & Stripling, R, 0, Strategies for counselor intervention with a computer assisted career guidance system. Vocational Guidance Quarterly, 1979, 27, 230-237.
Sandmeyer, L. E. "Choices and changes": A workshop for women. Vocational Guidance Quarterly, 1980, 2^, 352-359,
Simon, H. A. A behavior model of rational choice. Quarterly Journal of Economics, 1955, £9, 99-118.
Simon, S. B,, Howe, L. W., & Kirschenbaum, H. Values clarification: A handbook of practical strategies for teachers and students. New York: Hart Publishing, 1972.
Slater, J. M. Career exploration: Theory, practice, and assessment. Vocational Guidance Quarterly, 1978, 27, 130-136.
Slovic, P., Fischoff, B., & Lichtenstein, S. Behavioral decision theory. In M. R. Rosenzweig, & L. W. Porter (Eds.), Annual review of psychology. Palo Alto, CA: Annual Reviews, 1977.
Smaby, M. H., & Tamminen, A. W. Counseling for decisions. Personnel and Guidance Journal, 1978, bl_, 106-110.
118
Smith, R. D., & Evans, J. R. Comparison of experimental group guidance and individual counseling as facilitators of vocational development. Journal of Counseling Psychology, 1973, 20. 202-208.
Snodgrass, G., & Healy, C. C. Developing a replicable career decision-making counseling procedure. Journal of Counseling Psychology, 1979, 2^, 210-216.
Streufert, S., Clardy, M. A., Driver, M. J., Karlins, M., Schroder, H. M., & Suedfield, P. A tactical game for the analysis of complex decision making in individuals and groups. Psychological Reports, 1965, 2Z' 723-729.
Streufert, S., & Driver, M. J. Conceptual structure, information load and perceptual complexity. Psychonomic Science, 1965, }_, 249-250.
Streufert, S., Kliger, S. C , Castore, C. H., & Driver, M. J. Tactical and negotiations game for analysis of decision integration across decision areas. Psychological Reports, 1967, 2^, 155-157.
Streufert, S., & Schroder, H. M. Conceptual structure, environmental complexity and task performance. Journal of Experimental Research in Personality, 1965, \ , 132-137.
Streufert, S., & Streufert, S. C. The perception of information relevance. Psychonomic Science, 1970, 18, 199-200.
Streufert, S., Suedfeld, P., & Driver, M. J, Conceptual structure, information search, and information utilization. Journal of Personality and Social Psychology, 1965, 2, 736-740,
Streufert, S, C. Effects of information relevance on decision making in complex environments. Memory ^ Cognition, 1973, 1, 224-228.
Super, D. E. The criteria of vocational success. Occupations, 1951, 2^. 5-8.
Super, D. E. The dimensions and measurement of vocational maturity. Teachers College Record, 1955, bl_, 151-163.
Super, D. E. Work Values Inventory. Boston: Houghton Mifflin, 1968,
119
Super, D. E., Starishevsky, R., Matlin, N., & Jordaan, J. P. Career development: Self-concept theory (Research Monograph No. 4). New York: College Entrance Examination Board, 1963.
Thompson, D. G., & Hudson, G. R. Values clarification and behavioral group counseling with ninth-grade boys in a residential school. Journal of Counseling Psychology, 1982, 22. 394-399.
Thoni, R. J., & Olsson, P. M. A systematic career development program in a liberal arts college. Personnel and Guidance Journal, 1975, 53^, 672-675.
Thoresen, C. E., & Mehrens, W. A. Decision theory and vocational counseling: Important concepts and questions. Personnel and Guidance Journal, 1967, 2§. 165-172.
Tiedeman, D. V. Decision and vocational development: A paradigm and its implications. Personnel and Guidance Journal, 1961, 4^, 15-21.
Tiedeman, D. V. Career development: Choice and adjustment (Research Monograph No~! 3) , New York: College Entrance Examination Board, 1963,
Tiedeman, D, V,, & O'Hara, R, P. Career development: Choice and adjustment, New York: College Entrance Examination Board, 1963,
Tolman, E. C. Principles of performance. Psychological Review, 1955, 6^, 315-326.
Tversky, A. Elimination by aspects: A theory of choice. Psychological Review, 1972, 22. 281-299.
U. S. Department of Labor. Occupational outlook handb(pok (1982-83 ed,), Washington, D,C.: Government Printing Office, 1982,
Vail, S. V. Alternative calculi of subjective probability. In R. M. Thrall, C. H. Coombs, & R. L, Davis (Eds,), Decison processes. New York: Wiley, 1954,
Varenhorst, B, B. Learning the consequences of life's decisions. In J. D. Krumboltz, & C. E, Thoresen (Eds.), Behavioral counseling: Cases and techniques. Mew York: Holt, Rinehart, & Winston, 1969.
120
Warner, S. G., & Jepsen, D. A. Differential effects of conceptual level and group counseling format on adolescent career decision-making processes. Journal of Counseling Psychology, 1979, 26^, 497-503.
Wigent, P. A. Personality variables related to career decision-making abilities of community college students. Journal of College Student Personnel, 1974, 15, 105-108.
Wiggins, J. D. The Career Survey. Washington, D.C.: National Vocational Guidance Association, 1974.
Wiggins, J. D., & Moody, A, A field-based comparison of four career exploration approaches. Vocational Guidance Quarterly, 1981, 3^. 15-20.
Wilson, C. Z., & Alexis, M. Basic frameworks for decisions. In W. T. Greenwood (Ed.), Decision theory and information systems. Cincinnati, OH: South-Western Publishing Co., 1969. (Reprinted from Journal of the Academy of Management, 1962, 5_.)
Winer, J, L, , Cesari, J., Haase, R, F,, & Bodden, J. L. Cognitive complexity and career maturity among college students. Journal of Vocational Behavior, 1979, 15, 186-192.
Wise, R., Charner, I., & Randour, M. L. A conceptual framework for career awareness in career decision-making. The Counseling Psychologist, 1976, 6, 47-53.
Yates, C , Johnson, N. , & Johnson, J. Effects of use of the vocational exploration group on career maturity. Journal of Counseling Psychology, 1979, 2^' 368-370.
APPENDIX A
DATA SHEET
Name _^_ (print) Age
Sex Ethnicity Marital Status
Father's occupation
Mother's occupation
College year College major
What career do you plan to enter when you leave Texas Tech?
How sure are you of entering that career? (Circle response) 1 2 3 4 5 6
Strongly Moderately Slightly Slightly Moderately Strongly Unsure Unsure Unsure Sure Sure Sure
What, if any, career counseling have you had? Include any career development courses you may have taken. Tell date, place, and brief program description.
In what city did you attend high school?
What were your main course interests in high school?
(at least two)
What are your main course interests here at Tech?
(at least two)
How far did your father go in his education?
How far did your mother go in her education?
How are you paying for college?
121
122
What are the two most important qualities to you about
any job?
APPENDIX B
VALUES FEEDBACK EXAMPLES
Example 1.
The Work Values Inventory you completed asked questions about what you like about a job. There were several values that you scored high and it may make the most sense to cluster these values to get some meaning from them. There were a few values that seem to relate to the situational-material aspects of work. You indicated that surroundings, supervisory relations, way of life, were all important to you. Surroundings has to do with wanting to work in a pleasant work environment. Supervisory relations has to do with wanting a boss with whom you get along and who is fair with you. Way of life has to do with your job allowing you to live in the style of life that you choose. You also indicated that achievement was important to you in a job. Achievement has to do with feeling a sense of accomplishment in a job well done. Related to the kind of work that you might be doing, you indicated that esthetics is important to you in work. Esthetics has to do with creating beautiful things or contributing beauty to the world. Finally, you indicated that altruism was important to you in a job. Altruism has to do with contributing to the benefit and welfare of others. There were two values in particular that you indicated were not so important to you in a job: associates and management. Associates has to do with work that would bring you into contact with fellow workers who you liked. Management has to do with organizing and planning work for others to do. These last two values were relatively unimportant to you as indicated by your Work Values Inventory results. These are your current work values. They may change somewhat as you continue in your curriculum at Tech and take on various part-time and full-time jobs.
123
124
Example 2
The Work Values Inventory you completed asked questions about what you like in a 30b. You indicated that achievement and economic return were important to you. Achievement has to do with feeling a sense of accomplishment from a job well done. Economic return has to do with a good wage for the work completed. Your preference for a job that allows achievement relates to your data sheet statement where you said that you like a job where you could accomplish significant things or be productive. In contrast, there were a few values that you indicated were relatively unimportant to you in a job. You indicated that esthetics, creativity, and management were not so important to you. Esthetics has to do with creating beautiful things or contributing beauty to the world. Management has to do with organizing or planning work for others to do. Creativity has to do with work that would permit you to invent new things, design new products, or develop new ideas. These are your current work values. They may change somewhat as you continue in your curriculum at Tech and take on various part-time and full-time jobs.
APPENDIX C
CAREER INFORMATION FEEDBACK EXAMPLES
Example 1
The Self-Directed Search you took explored your career interests. Based upon what activities, jobs, etc., you indicated as like/dislike, you are currently most interested in jobs with "Social," "Conventional," and "Realistic" components. Social-type jobs are those that involve dealing with people and using teamwork to solve problems. Conventional-type jobs are regular and routine in nature. They often involve precise numerical and verbal skills, such as might characterize clerical work. Realistic-type jobs are practical in nature and usually involve hands-on work with machinery and/or tools. You did tend to mark five of the possible six career interests at a very high level. The one career interest that, at this time, does not seem so strong for you is Enterprising, Enterprising-type jobs are those that involve business transactions and persuasion and verbal skills that are used to get others to see things your way. This Enterprising component is one element that stands out as not so important to you at this time.
Based on your primary career interests of Social, Conventional, Realistic, it is possible to look at various SCR jobs that match your career interests. Such SCR jobs include:
Philologist Office Copy Selector Chief Projectionist Library Clerk, Talking Books Extension Clerk Inspector Medical Asst Mannequin Coloring Artist Physical Ther Asst Sulfuric Acid Plant Supervisor Asst
The Medical Assistant and Physical Therapist Assistant jobs do seem to mesh with your expressed interest in nursing and medical professions. Your strong expressed interest in being a teacher of children does not fit this SCR career interest shown by the Self-Directed Search, In fact, your 'top-listed job interest of teacher and lawyer both include the Enterprising component that you do seem to indicate is not desirable to you in a job. Consequently, it may give you some information and help your career thinking to provide you with some information about health assistants.
125
126
The Occupational Outlook Handbook (OOH) and Dictionary of Occupational Titles (both available in library) give information about job groups. You might consult these references to discover more about the above jobs or any others. For example, the OOH gives the following general information about health technologists. Health technologists' jobs involve operating or monitoring bio-medical equipment. Career preparation varies. Some workers learn their skills on the job through several months of classroom and laboratory study, combined with closely supervised clinical experience. A few occupations require more extensive preparation. The distinction between a health technologist and health technician lies in the complexity of the job. Technologists perform a higher level of responsibility than technicians and, therefore, need more training. For example, medical technologists, who use laboratory techniques to test specimens of body fluids and tissues for evidence of disease, need a Bachelor's degree with a specialization in medical technology, and medical technicians usually are graduates of two-year programs. Technologists also usually earn about $2,000 more per year than technicians. For example, electroencephalogram technologists earn about $14,000 a year starting salary when working for hospitals, medical schools, or medical centers. Employment in the health industry is expected to grow much faster than the average for all industries in the 1980s. This is due to population growth, especially for substantial increases in the number of older people.
These are your current career interests based on results from the Self-Directed Search. Your career interests may change somewhat as you continue in your curriculum at Tech and engage in various part-time and full-time jobs.
127
Example 2_
The Self-Directed Search you took explored your career interests. Based on what activities, jobs, etc., you marked as like/dislike, you are currently most interested in jobs with "Social," "Conventional," and "Enterprising" components. Since your Conventional and Social interests are tied in strength, it is possible to look at both Social, Conventional, Enterprising career interests (SCE) and Conventional, Social, Enterprising interests (CSE). Social-type jobs have to do with dealing with people and using teamwork to solve problems. Conventional-type jobs are fairly routine and regular in nature and often require precise verbal or numerical tasks such as would characterize clerical work. Enterprising-type jobs are business oriented and involve using persuasion and speaking effectively to get others to see things your way.
Possible jobs that fit your SCE career interests include real estate appraiser, market research analyst, field cashier, supervisor..,, and mortgage closing clerk. Possible SCE supervisor jobs include agency appointment supervisor, correspondence section supervisor, trust account supervisor, data control clerk supervisor, and securities vault supervisor.
Possible CSE jobs that relate to your career interests include systems accountant/account analyst, medical record technician, supervisor..., account information clerk, and title examiner/supervisor. Possible supervisor jobs in the CSE group include personnel clerk supervisor, money room supervisor, accounting clerk supervisor, underwriting clerk supervisor, and trust evaluation supervisor.
You will note that these various accounting jobs and supervisory positions relate well to your interest in accounting and management information systems.
The Occupational Outlook Handbook (OOH) and Dicticpnary of Occupational Titles (both available ^ in library) give information about job groups. You might want to consult these references for information about these or any other jobs. For example, the OOH gives the following general information about managers. There are top-level managers, such as executives, primarily concerned with policy making, planning, and overall coordination of organizations. Middle managers may handle particular areas, such as personnel, accounting, sales, finance, or marketing. Middle managers
128
work with the assistance of support personnel. Managers and administrators are employed in virtually every type of industrial plant, commercial enterprise, and government agency. Job duties vary greatly. Earnings for managers and administrators vary widely. They depend on the industry and on the size and nature of the particular establishment. On the whole, employment of managers and administrators is projected to grow about as fast as the average for all occupations through the 1980s. The greatest management employment is among restaurant, cafe, and bar manager positions. Third in size are sales managers of retail trade establishments.
The SCE code indicates your current career interests based on your Self-Directed Search scores. As you gain experience in various full and part-time jobs and continue in your college program here at Tech, your career preferences may change somewhat.
APPENDIX D
DECISION TRAINING OUTLINE
A. Essentials of choice Values Information Decision-making skills
Process vs outcome
B. Decision-making steps (Mnemonic: DECIDES) Define the problem
Concrete; behavioral Establish action plan
Strategies Reversibility Relative importance of decisions
Clarify values Knowing what is preferred
Identify alternatives Brainstorming Using/seeking information
Discuss probable outcomes Risks and costs
Eliminate alternatives systematically Start action/Decide
C. Discussion of previous decision-making steps
in terms of recent decision/college major choice
D. Predicting Outcomes* exercise; discussion
E. Mark's Critical Decision* exercise; discussion
F. Catskinner* exercise; discussion
G. Decision-making exercise (by author); discussion
*From Gelatt, Varenhorst, Carey, and Miller, 1973
129
APPENDIX E
STUDY SKILLS TRAINING OUTLINE
A. Exploration of study location Administer distractability measure Discuss distractability of study location Cues that study area gives for studying
B. Time Scheduling Personal preferences for study time periods How long to study at one time Massed vs distributed study time Timing the study of certain subjects
Studying for tests vs next day's lecture Studying Chem vs Eng Lit, for example
C. Record keeping/organization Assignment book Moderate vs strict organization
D. Study/reading skills techniques SQ3R Note taking - abbreviations Outlining
E. Mnemonics Narrative chaining Method of loci Coined word procedure Pairing method Peg word
F. Test taking Studying for objective vs essay tests Easy test items first Outline necessary items; organize Consulting class notes for instructor's orientation Gauging time; doing important things first
130
APPENDIX F
CONSENT FORM
You will be participating in research that is exploring what skills might benefit people in making career choices. Today you will take two paper and pencil tests that will give the researcher information about your career interests and work values. Tonight's session will last about 1 hour.
The tests will be scored and written up in a one-page report that explains you career interests and work values. The report will also provide you with information about possible careers that fit your interests and values. The report will be given to you at the next session.
Session 2 will meet one week from tonight at 6:00 p,m, in this same room. At Session 2 you will be involved in a program designed to provide you with some basic skills that are intended to increase your chances of getting what you want in a career. The second session will be a lot like a typical college class period. You will be taught some things, participate in some exercises, and become involved in the subject of the session. At the end of the session, you will complete another paper and pencil test to see what you got out of the exercises. Session 2 will take three hours about two hours for the skill training program and one hour for the final testing.
When you attend Session 2, you will get credit for completing all four hours of your Psych 130 research bonus points. If you do not return next week to complete the study, you will give up the one hour of credit you would have earned for tonight. It is very important to this research that you finish the entire project. If you cannot be sure you can finish the project, then it would be best for you to sign up for other things. By signing below, you are making a contract with me that you will be here for the second session. Hopefully the report on your tested career interests and work values will be worth coming for next week.
Late in the semester I will put up an announcement on the undergraduate bulletin board about a time/place you may all meet with me to review the research results and answer any questions you may have about your specific results. When I put up the announcement, I will refer to this experiment by
131
132
its code: C. Remember the code for later reference. If you get real curious about your career interests and work values, feel free to contact me at home (747-8609) to talk about your results. Also, the Texas Tech Counseling Center can tell you about your career interests--if you showed them the report I will give you, they could tell you more about its meaning. You can call the Counseling Center at 742-3674.
As a Psych 130 student participating in research for bonus points, you have the right to terminate this research at any time without penalty. However, you must contact me in person to withdraw from this research. Missing scheduled sessions is not the same as withdrawal from the study. To withdraw you must see me and tell me you want to quit the study.
In accordance with Texas Tech regulations governing human research. Dr. Clay George (742-3727; Psych, Building Rm, #302) has agreed to answer any inquiries you may have concerning the research procedures. Additionally, you may contact the Texas Tech University Institutional Review Board for the Protection of Human Subjects by writing them in care of the Office of Research Services, Texas Tech University, Lubbock, Texas 79409, or by calling 742-3884,
If the research project causes any physical injury to participants in this project, treatment is not necessarily available at Texas Tech University or the Student Health Center, nor is there necessarily any insurance carried by the University or its personnel applicable to cover any such injury. Financial compensation for any such injury must be provided through the participant's own insurance program. Further information about these matters may be obtained from Dr. J. Knox Jones, Jr., Vice President for Research and Graduate Studies, 742-2153, Room 118, Administration Building, Texas Tech University, Lubbock, Texas 79409,
Your signature below shows you have read this consent form and understand it. If you have any questions, ask me to answer them before you sign below.
Thank you for your participation. Since I will keep this form, be sure to record from it any information you may want later.
133
Signature ~~~ Printed Name Date
Investigator Signature Date
APPENDIX G
COMPLETE FEEDBACK REPORT EXAMPLES
Example _1
The Work Values Inventory you completed asked questions about what you like about a job. There were several values that you scored high and it may make the most sense to cluster these values to get some meaning from them. There were a few values that seem to relate to the situational-material aspects of work. You indicated that surroundings, supervisory relations, way of life, were all importnt to you. Surroundings has to do with wanting to work in a pleasant work environment. Supervisory relations has to do with wanting a boss with whom you get along and who is fair with you. Way of life has to do with your job allowing you to live in the style of life that you choose. You also indicated that achievement was important to you in a job. Achievement has to do with feeling a sense of accomplishment in a job well done. Related to the kind of work that you might be doing, you indicated that esthetics is important to you in work. Esthetics has to do with creating beautiful things or contributing beauty to the world. Finally, you indicated that altruism was important to you in a job. Altruism has to do with contributing to the benefit and welfare of others. There were two values in particular that you indicated were not so important to you in a job: associates and management. Associates has to do with work that would bring you into contact with fellow workers who you liked. Management has to do with organizing and planning work for others to do. These last two values were relatively unimportant to you as indicated by your Work Values Inventory results. These are your current work values. They may change somewhat as you continue in your curriculum at Tech and take on various part-time and full-time jobs.
The Self-Directed Search you took explored your career interests. Based upon what activities, jobs, etc, you indicated as like/dislike, you are currently most interested 'in jobs with "Social," "Conventional," and "Realistic" components. Social-type jobs are those that involve dealing with people and using teamwork to solve problems. Conventional-type jobs are regular and routine in nature. They often involve precise numerical and verbal skills, such
134
135
as might characterize clerical work. Realistic-type jobs are practical in nature and usually involve hands-on work with machinery and/or tools. You did tend to mark five of the possible six career interests at a very high level. The one career interest that, at this time, does not seem so strong for you is Enterprising. Enterprising-type jobs are those that involve business transactions and persuasion and verbal skills that are used to get others to see things your way. This Enterprising component is one element that stands out as not so important to you at this time.
Based on your primary career interests of Social, Conventional, Realistic, it is possible to look at various SCR jobs that match your career interests. Such SCR jobs include:
Philologist Office Copy Selector Chief Projectionist Library Clerk, Talking Books Extension Clerk Inspector Medical Asst Mannequin Coloring Artist Physical Ther Asst Sulfuric Acid Plant Supervisor Asst
The Medical Assistant and Physical Therapist Assistant jobs do seem to mesh with your expressed interest in nursing and medical professions. Your strong expressed interest in being a teacher of children does not fit this SCR career interest shown by the Self-Directed Search. In fact, your top-listed job interest of teacher and lawyer both include the Enterprising component that you do seem to indicate is not desirable to you in a job. Consequently, it may give you some information and help your career thinking to provide you with some information about health assistants.
The Occupational Outlook Handbook (OOH) and Dictionary of Occupational Titles (both available in library) give infoF^ mation about job groups. You might consult these references to discover more about the above jobs or any others. For example, the OOH gives the following general information about health technologists. Health technologists' jobs involve operating or monitoring bio-medical equipment. Career preparation varies. Some workers learn their skills on the job through several months of classroom and laboratory study, combined with closely supervised clinical experience. A few occupations require more extensive preparation. The 'distinction between a health technologist and health technician lies in the complexity of the job. Technologists perform a higher level of responsibility than technicians and, therefore, need more training. For example, medical technologists, who use laboratory techniques to test
136
specimens of body disease, need a Bac medical technology, graduates of two-yea earn about $2,000 mo pie, electroencephal year starting salar schools, or medical dustry is expected t all industries in t growth, especially of older people.
fluids and tissues for evidence of helor's degree with a specialization in and medical technicians usually are
r programs. Technologists also usually re per year than technicians. For exam-ogram technologists earn about $14,000 a y when working for hospitals, medical centers. Employment in the health in-o grow much faster than the average for he 1980s. This is due to population for substantial increases in the number
These are your current career interests based on results from the Self-Directed Search. Your career interests may change somewhat as you continue in your curriculum at Tech and engage in various part-time and full-time jobs.
137
Example ^
The Work Values Inventory you completed asked questions about what you like in a job. You indicated that achievement and economic return were important to you. Achievement has to do with feeling a sense of accomplishment from a job well done. Economic return has to do with a good wage for the work completed. Your preference for a job that allows achievement relates to your data sheet statement where you said that you like a job where you could accomplish significant things or be productive. In contrast, there were a few values that you indicated were relatively unimportant to you in a job. You indicated that esthetics, creativity, and management were not so important to you. Esthetics has to do with creating beautiful things or contributing beauty to the world. Management has to do with organizing or planning work for others to do. Creativity has to do with work that would permit you to invent new things, design new products, or develop new ideas. These are your current work values. They may change somewhat as you continue in your curriculum at Tech and take on various part-time and full-time jobs.
The Self-Directed Search you took explored your career interests. Based on what activities, jobs, etc., you marked as like/dislike, you are currently most interested in jobs with "Social," "Conventional," and "Enterprising" components. Since your Conventional and Social interests are tied in strength, it is possible to look at both Social, Conventional, Enterprising career interests (SCE) and Conventional, Social, Enterprising interests (CSE), Social-type jobs have to do with dealing with people and using teamwork to solve problems. Conventional-type jobs are fairly routine and regular in nature and often require precise verbal or numerical tasks such as would characterize clerical work. Enterprising-type jobs are business oriented and involve using persuasion and speaking effectively to get others to see things your way.
Possible jobs that fit your SCE career interests include real estate appraiser, market research analyst, field cashier, supervisor..., and mortgage closing clerk. Possible SCE supervisor jobs include agency appointment supervisor, correspondence section supervisor, trust account 'supervisor, data control clerk supervisor, and securities vault supervisor.
Possible CSE jobs that relate to your career interests include systems accountant/account analyst, medical record
138
technician, supervisor,.., account information clerk, and title examiner/supervisor. Possible supervisor jobs in the CSE group include personnel clerk supervisor, money room supervisor, accounting clerk supervisor, underwriting clerk supervisor, and trust evaluation supervisor.
You will note that these various accounting jobs and supervisory positions relate well to your interest in accounting and management information systems.
The Occupational Outlook Handbook (OOH) and Dictionary of Occupational Titles (both available in library) give information about job groups. You might want to consult these references for information about these or any other jobs. For example, the OOH gives the following general information about managers. There are top-level managers, such as executives, primarily concerned with policy making, planning, and overall coordination of organizations. Middle managers may handle particular areas, such as personnel, accounting, sales, finance, or marketing. Middle managers work with the assistance of support personnel. Managers and administrators are employed in virtually every type of industrial plant, commercial enterprise, and government agency. Job duties vary greatly. Earnings for managers and administrators vary widely. They depend on the industry and on the size and nature of the particular establishment. On the whole, employment of managers and administrators is projected to grow about as fast as the average for all occupations through the 1980s. The greatest management employment is among restaurant, cafe, and bar manager positions. Third in size are sales managers of retail trade establishments.
The SCE code indicates your current career interests based on your Self-Directed Search scores. As you gain experience in various full and part-time jobs and continue in your college program here at Tech, your career preferences may change somewhat.
APPENDIX H
PROCEDURAL OUTLINE FOR EACH GROUP
SESSION 1 SESSION 2
GRP TRTMNT CONSENT DATA SDS WVI 15" 2" 45"
V*
B
C
D
E
F
G
H
I /D
C
I/V
V/D
D
I /V/D
I
Yes Yes Yes Yes Read V
Read I D
Wait
Read I/V
Read V D
Wait
Read I/V D
Read I •
CMI-CT
*KEY
C = control study skills training D = decision-making skills training I = information feedback V = values feedback
139