(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

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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.

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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.

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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

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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

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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

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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

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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.

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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

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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

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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.

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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

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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

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