Career development of special populations: A framework for research

Journal of Vocational Behavior 22, 12-29 (1983)

Career Development of Special Populations: A Framework for Research SUSAN D. PHILLIPS, DOUGLAS C. STROHMER,BARRY L. J. BERTHAUME, AND JOHN C. O’LEARY State University of New York at Albany In view of the recent interest in the career behaviors of diverse groups of individuals, this article presents a model for conducting research on the career development of special populations. Available research paradigms are reviewed in terms of their potential impact on such efforts. An alternative framework, designed to make maximum use of available data sources while minimizing potential theoretical bias, is offered. An illustration of research conducted within the recommended framework is presented in which the impact of a variety of career-related variables on the attitudinal and cognitive aspects of vocational maturity is examined from the perspective of two special population groups, disabled and disadvantaged college students. Drawing upon previous research tindings, variables under consideration included level of scholastic achievement and endorsement of rational, intuitive, and dependent decision-making styles. Also included were two population-specific variables related to the characteristics of membership in each of the two special groups. The regression analyses conducted to explore the relationships between vocational maturity and these predictor variables indicate, in general, that variance in the attitudinal and cognitive factors of vocational maturity can be at least partially explained by the variables considered. However, the relative contributions of the predictors and the resulting proportions of variance explained differs according to which population and which criterion factor is under examination. These findings are compared with those emerging from previous research, and implications for future efforts are discussed.

As one reviews the current state of affairs in the literature on the career development of special populations such as the disabled and the educationally and economically disadvantaged, it becomes clear that much is to be desired. In terms of volume alone. less than 6% of articles Based in part on papers presented at the annual meetings of the American Psychological Association in Montreal (August 1980)and Los Angeles (August 1981).The first two authors made equal contribution to this paper and order of primary authorship was therefore arbitrarily determined. Requests for reprints should be directed to Susan D. Phillips, Education 220, State University of New York at Albany, 1400Washington Avenue, Albany, NY 12222. Appreciation is expressed to Dr. Richard F. Haase for comments on earlier drafts of this manuscript and to Phyllis Bolling, Susan Chase, Greg Chase, Carolyn Conroy, Kevin Creegan, and Hector Rios for assistance in data collection and analysis. 12 0001~8791/83/010012-18$03.00/O Copyright All rights

Q 1983 by Academic Press, Inc. of reproduction in any form reserved.

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appearing in major professional journals during the period 1971 to 1975 addressed concerns of special populations (Holcomb & Anderson, 1977). Surely this proportion does not suggest that members of special populations experience fewer difficulties or appear less often in the offices of counselors. On the contrary, counselors who are confronted with the concerns of such groups are likely to confirm the suspicion that their clientele experience no fewer problems, nor do members of these groups come to counselors for assistance in fewer numbers. Such counselors are likely to suggest, in addition, that the problems presented by their clients are in many cases qualitatively different and stem from variables not explained in the available literature. Counselors are confronted with the task of translating explanations which have meaning for traditional groups into interventions for nontraditional groups without the benefit of theoretical or empirical precedence. In an apparent response to the difficulty of this task, as well as to the obvious void in the literature, there has been an increasing number of investigations since 1975 which take as their focus vocational phenomena as they relate to the specific concerns of special populations. Although this growing interest in the similarities, differences, and problems of diverse populations is encouraging, it must be evaluated ,with caution. The theories which guide these investigations have been frequently faulted with suspect assumptions, neglected variables, and inadequate research (Dillard, 1980; Osipow, 1975; Smith, 1975). Unfortunately, much of the criticism leveled at current and previous efforts has simply pointed out defects and oversights without providing useful alternatives designed to amend them. An awareness of the shortcomings of previous research efforts, while necessary, does not seem sufficient. It is the purpose of this paper to discuss and illustrate an alternative framework for conducting empirical research examining the career development of special populations and to discuss some implications of employing such a framework in future investigations. AVAILABLE RESEARCH PARADIGMS In considering a framework for special populations career research, it is necessary to keep in mind the major purpose of such an investigation: To deduce a set of organizing principles that provides a framework from which to explain and predict the career development of diverse groups of individuals. More specifically, such research should seek to describe career phenomena as they currently exist and, second, to explain the relationships among an array of vocational behaviors, attitudes, events, and outcomes. The necessary sequence in this statement should be noted: one cannot adequately explain that which one has not accurately described. Because no clear picture exists of the nature of career development for

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PHILLIPS ET AL.

members of special populations, the design of future research must first be descriptive and, second, explanatory. With this basic directive in mind, the research task should become quite simple: One need only to observeindividuals from specialpopulations engaging in vocational behaviors and to describe the results of the observation. This task, however, is not as simple as it appears. What, for example, does one choose to observe? The selection of phenomena for observation is likely to be determined in part by that which is considered sign&ant. And that which is consideredsignificantis frequently determined by the theory-or set of organizing principles-to which one subscribes. If, in fact, the career development theory to which one subscribes is incomplete, or perhaps irrelevant with respect to special groups, then the direction one takes based on that theory must be suspect. Barker (1965) addressed this predicament in his discussion of the manner in which psychologistshave typically predeterminedwhat data will be obtained and have, as a result, deprived the discipline of the benefits of unaltered descriptive information. Career developmentresearchers,when confronted with a void such as that found in the special populations career literature, have in many cases succumbed to the temptation to fill it quickly by testing hypotheses derived from current career theory. The relevance of the premiseson which such investigationsare basedis at best questionable. As Barker pointed out, however, descriptive investigations of an atheoretical nature, in which one simply records behaviors as they occur, provide an alternative to such potentially misdirected investigations.Thus, the void noted in the literature on the career development of special populations provides vocational psychology with an opportunity to obtain some sorely needed descriptive information. As one considers the two alternative approaches to filling this void, neither hypothesis testing nor purely descriptive research alone is particularly attractive. Research using the hypothesis-testing approach has not only the effect of stressingconfirmation over generation of hypotheses, but also that of yielding one-sided pictures of multifaceted phenomena (Wachtel, 1980).For those populations about whom satisfactorydescriptive and explanatory models have been developed, the use of the hypothesistesting approach serves to strengthen and clarify the adequacy of existing theory. For those populations, however, whose membershipcharacteristics suggest substantial deviation from the assumptions of traditional models, a more cautious approach is necessary. It is too simple to test a theoryderived hypothesis with special populations and to conclude that members of that population do or do not behave in the way that traditional theory predicts. Such a conclusion adds little to the needed descriptive base and may lead to faulty inferences about the vocational behaviors of special groups. On the other hand, research conducted within purely

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descriptive paradigms runs the risk of discarding or ignoring theoretical notions that may be highly relevant to a given special population. An Alternative

The two research strategies outlined above have been traditionally regarded as incompatible; however, if considered in combination, a more attractive alternative may emerge. Consider the possibility of conducting special populations research based on designs which neither discarded existing theory nor assumedthat it was valid. Investigatorswould observe those phenomena suggested by current theory, those which are as yet unexamined but potentially significant, and those which are disclosed during the investigation itself. Hypotheses to be tested could be generated not only from existing theoretical premises, but also from exploratory analysis of descriptive information gathered during the course of the investigation. Studies conducted in a cumulative sequencecould be directed by hypotheses generated on prior steps in the sequenceand could provide alternate hypotheses and comparative data for subsequent steps. Each step of the sequencecould include both hypothesis testing and exploratory analysis; behavioral variables to be observed could be added, deleted, or modified as indicated by the analyses. Such an approach should add significantly to the special populations career literature by broadening the scope of investigation to include factors already determined to be significantfor the populations about which most is known, without allowing these same variables to interfere with the ability to identify others that may have relevance to special subgroups. The method suggested by the above strategy is certainly not revolutionary; rather, it reflects the need for careful and methodical consideration of a variety of data sources and potential explanations of those data. Attention to this need is clearly warranted for research with all kinds of populations and is particularly relevant for special groups, given the recent interest in understanding vocational experiences associated with special group membership. The translation of these concepts into a framework for special populations career research simply provides a method of minimizing theoretical bias while making maximum use of the richness of descriptive data and exploratory analysis. To apply these concepts to specialpopulations career research,however, several methodological and theoretical assumptions must be made. The first is that there is only one world of work. It holds that, despite considerable variety and biased representation within that world, members of special populations who work do so in the same arena as do members of any other population. The second assumption is that those theories which have been advanced in relation to that world of work (albeit with respect to a traditional population) may offer useful and perhaps valid concepts for investigation. There may be benefits in examining more

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ET AL.

closely the efforts of past investigators. The third assumption is that vocational behaviors and career development are multidetermined; not only are both individual and environmentalvariablespowerful determinants, but also different combinations of those variables may be significant for different groups of individuals. Finally, the fourth assumption is that exploratory research is not only a worthwhile endeavor, but also, at the current stage of knowledge, a necessary one. Given these assumptions, then, the method suggested above may be applied as follows. By taking the thoughts and findings of previous investigators as a point of departure, observations may be made of those variables which have been predicted as significant in career development, In addition, data can be gathered in an open-ended fashion in which the theoretical biases of the investigator do not come so strongly into play. An example of such an open-ended approach might be a semistructured interview or unobtrusive observation of career-related behaviors in a naturally occurring situation. Analysis of data collected in this fashion would include (a) testing the relationships predicted by previous findings, (b) examining the solicited and free response data with an eye to hitherto unpredicted relationships, (c) redefining, as necessary, the variables to be observed, and (d) generating working hypotheses to be tested in subsequent investigations. AN ILLUSTRATION

As an illustration of an investigation conducted within this framework, consider the following example. Selected for investigation was a sample of 33 disabled and 29 economically/educationally disadvantaged college students. Despite the obvious problems in selecting such an elite group of individuals for study, the vocational tasks and behaviors of college students are among the most thoroughly documented in the current literature. The selection of subgroups from a population whose career development has been described and explained to some extent by a number of theories provided the investigatorswith a wealth of preliminary hypotheses for testing in attempting to understand the career development of special populations. Among these hypotheses is that represented by the construct of vocational maturity (Super & Overstreet, 1960). The vocationally mature individual has been characterized as one who is oriented toward planning, accepts responsibility for choices, is aware and makes use of available resourcesin plan&g, has specificinformation about preferred occupations, and demonstrates competence in decision-making (Dilley, 1965;Jordaan & Heyde, 1979;Super & Overstreet, 1960).Although these characteristics would appear desirable for any individual participating in the world of work, few studies are reported in which factors influencing vocational maturation are investigated from the perspective of special populations.

SPECIAL

POPULATIONS

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Once such factors are more clearly understood it would then be possible to design realistic treatment strategies to enhance the vocational functioning of disabled or disadvantaged individuals. Consistent with the foregoing discussion, a suggested starting point for examining this area with a disabled or disadvantaged group would be to examine the findings drawn from research conducted with more traditional populations. For example, among traditional groups, this model of the vocationally mature individual is based heavily on an endorsement of rationality as the prescribed approach to career-related decision making. The adoption of a rational decision-making style has been presumed to be associated with a methodical approach to the gathering and use of career relevant information, which, in turn, is associated with enhanced vocational maturity (Egner & Jackson, 1978; Myers, Lindeman, Thompson, & Patrick, 1975; Schenk, Johnston, & Jacobsen, 1979). Although logical, the relationship between vocational maturity and the use of a rational decision-making style has not enjoyed much empirical support. Rubinton (1980) found no difference at pretest in vocational maturity among community college students who endorsed a rational, intuitive, or dependent decision-making style. Phillips and Strohmer (1982) found that, for traditional college students, only a lack of a dependent decision-making style in combination with scholastic achievement was even moderately predictive of vocational maturity. Furthermore, the inclusion of rational and intuitive styles added little to the prediction of vocational maturity for this group. In addition, both Krumboltz (1979) and Rubinton (1980) have provided evidence to suggest that the wholesale use of rationalitybased interventions is not warranted and that such practice may encourage less effective responses among some individuals. Thus, it appears that among traditional populations a rational style of decision making is not necessarily indicative of effective and mature vocational functioning. Taking these findings based on traditional populations as a point of departure, the question remains: “What is the relationship between decision-making style and vocational maturity among special populations?” Given the differences in the complexity of developing vocationally typically associated with special group membership, it seems appropriate to question in what way the same factors influence the vocational maturity of these groups. The study reported here attempted to extend existing research findings to include the disabled and disadvantaged. Drawing upon previous findings in relation to the correlates of vocational maturity (Phillips & Strohmer, 1982; Rubinton, 1980; Super & Overstreet, 1960), this study examined variables clearly related to career functioning along with possible moderating variables not explicitly vocational in nature. The use of different decision-making styles was selected as a primary focus with level of scholastic achievement serving as a possible moderating variable. To account for variables that may be unique to special population

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group membership, the study reported here included an additional variable for exploratory investigation. It was hypothesized that an individual’s perception of the characteristics of his or her group membership would function as a moderator of current vocational functioning. For the disabled subjects the unique effects of being disabled on career development were explored. With disadvantaged students the unique effects of maturing in a disadvantaged environment were examined. Thus, in this study, previously identified correlates of vocational maturity in traditional groups, in combination with population-specificfactors, were examinedas potential correlates of vocational maturity in disabled and disadvantaged college students. METHOD Subjects

Subjects for the study were volunteers from two student organizations on a large northeastern university campus. Disadvantaged students were recruited from the campus Educational Opportunity Program (EOP). Student eligibility in this program requires both educational and economic disadvantagement. Disabled students were recruited from the campus disabled students organization. A total of 29 EOP students, 11 males and 18 females, agreed to participate. Thirty-three disabled students, 21 males and 12 females, agreed to participate. The age range within each group was from 17 to 30. Measurement of Vocational Maturity

The Career Development Inventory (CDI)-Form IV (Thompson, Lindeman, Super, Jordaan, & Myers, 1981)was used to assessvocational maturity. The CD1 measures four aspects of vocational maturity as postulated by Super (1974): planning orientation, use of resources, career decision making, and knowledge of the world of work. It is a paper-andpencil inventory consisting of 80 items (20 per scale). The total of the four subscale scores yields a total career orientation score considered a valid estimate of an individual’s career attitudes, knowledge, and skills. In addition, the scales can be combined into a cognitive factor (use of resources and knowledge of world of work scales) and attitudinal factor (planning orientation and career decision-makingscales).These two factor scaleswere used in the study reported here. The hfth subscale(knowledge of preferred occupation) was excluded from the study in part on the basis of time constraints and in part on the basis of the recommendation of the CD1 Manual (Thompson et al., 1981)that the composite score of the four scales, representing the broad aspects of attitudinal and cognitive factors in vocational maturity, is of superior reliability compared with that of the preferred occupation scale and provides a sufficient estimate of an individual’s vocational functioning.

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Reliability estimates for the total score have been reported between 82 and 87 (Thompson et al., 1981).Reliability estimates for the cognitive and attitudinal factors are reported as .79-88, and .82--87, respectively (Thompson et al., 1981). Because the college level form of the CD1 was not available at the time of the data collection for this study, an adaptation of the School Form was used. Consistent with the methods used by the CD1 authors, several items were moditied to make the content appropriate to the college population under study. These modifications affected 31 of the 80 items, with 26 of those 31 being changes of the word “school,’ to the word “college.” None of the changes resulted in a substantive alteration of the meaning of the item. In view of these modifications, an internal consistency reliability coefficient was computed for this administration. The resulting reliability estimate (alpha = .83) is consistent with that reported above. Measurement

of Decision-Making

Style

The Assessmentof Career Decision Making (ACDM) (Harren, Note 1) provides an estimate of “the degree to which an individual takes personal responsibility for decision-making as opposed to projecting responsibility toward fate, peers and authorities, and the degree to which an individual uses logical versus emotional strategies in decision-making” (Moreland, Harren, Krimsky-Montague, & Tinsley, 1979).The decision-making style portion of the ACDM consistsof 30 items. The items are equally distributed among the three decision-makingstyles (rational, intuitive, and dependent). Scoring procedures yield a categorical ranking for the individual’s highest scale and interval scores reflecting the extent of endorsement of each decision style. Reliability estimates of the ACDM have been reported as between .75 and .85 (Han-en, Kass, Tinsley, & Moreland, 1978). Measurement

of Population-Specific

Factors

The Career DevelopmentAssessmentInterview (CDAI), a semistructured interview procedure developed for this study (available from the authors), was used to assess the impact of disability on the maturation of the disabled students and the impact of childhood environment on the maturation of the disadvantaged students. The CDAI was designed to allow a trained interviewer and research subject to explore in some detail the subjects’ perceptions of their career development and included topics which are not typically addressed by existing career development instrumentation. Disabled students explored with the interviewer their perceptions of the impact of their disability on their career development. Their responses were grouped into three categories: positive effects (e.g., the fact of disability served to focus their attention on their future needs), negative effects (e.g., subjects saw their disabilities as limiting their alternatives), and no perceived effects. Disadvantaged subjects explored

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PHILLIPS ET AL.

the effect that their early environment had on their career development. Their responses were grouped into the same categories as the disabled subjects. All interview protocols were scored by trained raters. Interrater reliability for these ratings was estimated at r = 90. Measurement of Scholastic Achievement

The scholastic achievement variable was elicited by asking each subject to indicate the category into which his or her cumulative college grade point average fell (e.g., 4.0-3.5, 3.0-3.4, 2.5-2.9, 2.0-2.4, under 2.0) Procedure

The disabled and disadvantaged students were contacted individually by an interviewer, and were seen at a time convenient to both. Subjects were asked to fill out a demographic questionnaire and the standardized instruments on their own before the interview. Interviewers assisted those students unable to complete the instruments themselves. After completing the instruments, each subject was interviewed using the CDAI. The total time for this process ranged from 2 to 5 hrs. Each subject received $5 for participation. Data Analysis

The relationship of the degree of endorsement of the different decisionmaking styles, scholastic achievement, and perceived effects of disability or environment on vocational maturity was examined using a heirarchical multiple regression analysis (Cohen dz Cohen, 1975).Each of the decisionmaking-style scale scores, the level of scholastic achievement (entered as a block set of dummy variables), and the population-specific factors (entered as a block set of dummy variables) were used as predictor variables of the vocational maturity criteria. Variables were entered in hierarchical order based on their zero-order correlation with the criterion variables. RESULTS

The procedures outlined above resulted in two separate regression analyses for each subject group using attitudinal and cognitive vocational maturity as the respective criteria. Means and standard deviations of continuous variables for each group are presented in Table 1. The results of the hierarchical multiple regression analyses are presented in Table 2. The analyses for the disabled subject group and for the disadvantaged subject group are discussed separately below. Disabled Subjects

Two hierarchical multiple regression analyses were conducted for the disabled subject group. The predictor variables were entered on the basis of their zero-order correlations with the criterion. For the regression

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TABLE 1 Means, Standard Deviations, and Ranges of Continuous Variables for Disabled and Disadvantaged Subjects Variable Decision-making style: rational (Range 0- 10) Decision-making style: Intuitive (Range O-10) Decision-making style: Dependent (Range O-10) Vocational maturity: Attitudinal (Range 95-400) Vocational maturity: Cognitive (Range O-40)

Disabled (n = 33)

Disadvantaged (n = 29)

7.91 (2.34)”

7.86 (2.29)

4.21 (1.60)

4.45 (2.15)

3.15 (2.29)

2.45 (2.16)

234.33 (26.79)

232.38 (29.70)

31.30 (3.92)

26.96 (6.39)

a Standard deviations are indicated in parentheses.

analysis using the attitudinal factor of vocational maturity as the criterion variable, the following order of entry was determined (zero-order correlation estimates are indicated in parentheses): scholastic achievement (.49), decision-making style: rational (.41), perceived effect of disability on past vocational development (.28), decision-making style: dependent (- .23), and decision-making style: intuitive (. 10). Both the scholastic achievement variable and the variable representing the perceived effect of disability were entered into the analysis as block combinations of dummy variables. The resulting regression equation is presented in Table 2. Scholastic achievement was entered on the first step, and, although this variable accounted for approximately 24% of the variance in the criterion, the resulting equation failed to reach significance. Rational decision-making style was entered with a positive weighting on the second step. The addition of this variable increased the variance accounted for to 36% and resulted in a significant 0, < .05) cumulative regression equation. The equation remained significant through the addition of perceived effect of disability and the negatively weighted decision-making style: dependent. The perceived effect variable contributed an additional 7% to the amount of variance accounted for, while the dependent decisionmaking style variable contributed 3%. The inclusion of decision-making style: intuitive on the final step resulted in an insufficient additional contribution to maintain a significant prediction equation. Thus, the most explanatory regression equation was found on the fourth step. This equation is significant at the .05 level and accounts for approximately 46% of the variance in the attitudinal criterion. A different order of entry was determined for the regression analysis using the cognitive factor of vocational maturity as the criterion variable

F(5, 27) = 2.32

F(4. 28) = 1.65

F(1, 27) = 4&O*

R = .38 R’ = .14

DMS-I (- .38)

R2 = .I6 F(I, 27) = 5.34*

F(7, 25) = 3.12*

R = 68 R’ = .47

PED

F(7, 25) = 2.71*

R = .66 R= = .43

R = .53 R= = .28 F(5, 23) = 1.81

SA’

R = .50 R’ = .25 F(2, 26) = 4.39’

R = .57 R’ = .33 F(7, 21) = 1.45

PEW

R = SS R= = .30 F(6, 22) = I.60

Disadvantaged sample (N = 29) DMS-R SA’ (+.31)

R = .55 R2 = .30

DMS-D (+ .34)

R = .44 R’ = .19

F(5, 27) = 3.06*

SA

DMS-I (-.41) R = .41

Step 3 Disabled sample (N = 33) + PED’

F(4, 28) = 2.18

DMS-R (+ .36) R = .60 R= = .36

+

Step 2

R = .49 R’ = .24

SA’

Step I

Regression equation”,b

+

R = .57 R2 = .33 F(8, 20) = I.21

(+ .02)

DMS-D

R = .55 R’ = .30 F(7, 21) = 1.31

(- .02)

DMS-D

R = .71 R* = .50 F(8, 24) = 2.99:

DMS-I (+.21)

F(8, 24) = 2.61:

R = .68 R= = .46

DMS-D C-.19)

Step 4

+

DMS-R (-JO) R = .58 R’ = .33 F(9. 19) = 1.06

R = .58 R’ = .34 F(9, 19) = 1.06

PEN’

R = .75 R’ = .56 F(9, 23) = 3.23*

DMS-R (+ .29)

R = .68 R’ = .47 F(9, 23) = 2.25

( + .07)

DMS-I

Step 5

Note. The following abbreviations are used: DMS-R (Decision-making style: Rational); DMS-I (Decision-making style: Intuitive); DMS-D (Decision-making style: Dependent); SA (scholastic achievement); PED (perceived effect of disability on past vocational development); PEN (perceived effect of environment on past vocational development). L1Variables were entered on the basis of their zero-order correlations with the criterion; beta weights are indicated in parentheses. * Regression equation information in reman print indicates that portion of the variables considered which resulted in a significant F valw; regression equation information in italic print indicates that portion of the variables considered which did not significantly contribute to the equation lo maintain a significant F value.

Cognitive

Attitudinal

Cognitive

Attitudinal

Vocational maturity factor

TABLE 2 Results of Heirarchical Multiple Regression Analyses on Attitudinal and Cognitive Factors of Vocational Maturity

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POPULATIONS

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Again using the zero-order correlation estimates(indicated in parentheses) to establish order of inclusion, the following sequence was determined: scholastic achievement (.44), decision-making style: dependent (.34), perceived effect of disability on past vocational development (.29), decisionmaking style: intuitive (.24), and decision-making style: rational (.16). As indicated in Table 2, the regression equation resulting from the inclusion of the first two variables failed to reach significance. Scholastic achievement, entered first, contributed 19%to the amount of variance accounted for; Decision-making style: dependent, entered as a positively weighted second variable, contributed an additional 11%. At the third step, upon the entry of the perceived effect of disability variable, the cumulative equation became signticant (p < .05) and accounted for 47% of the variance in the cognitive criterion. The regression equation remained significant through the inclusion of the final two variables (intuitive and rational decision-making style), both of which entered with positive weightings. The most explanatory regressionequation, therefore, included all five variables and accounted for 56% of the variance in the cognitive criterion. Disadvantaged Subjects

Two hierarchical multiple regression analyses were conducted for the disadvantaged subject group. As with the disabled group analyses, the predictor variableswere entered on the basisof their zero-order conrelations with the criterion. For the analysis using the attitudinal factor of vocational maturity as the criterion variable, the following order of entry was determined (zero-order correlation estimates are indicated in parentheses): decision-making style: intuitive ( - .41), decision-making style: rational (.39), scholastic achievement(.25), decision-makingstyle: dependent (.23), and perceived effect of environment on past vocational development (. 15). Both the scholasticachievementvariable and the variable representing the perceived effect of environment were entered into the analysis as block combinations of dummy variables. The resulting regressionequation is presented in Table 2. The intuitive decision-making style was entered as a negatively weighted variable on the first step. The resulting regression equation was significant at the .05 level and accounted for approximately 16%of the variance in the criterion. This proportion of explained variance increased by 9% with the inclusion of the rational decision-making style (positively weighted) on the second step, and the resulting equation remained significant (p < .05). The inclusion of scholastic achievement on the third step failed to contribute a significant addition of explained variance and resulted in a nonsignificant prediction equation. Steps 4 and 5, on which dependent decision-making style and perceived effect of environment were included, also failed to make a sign&ant contribution. Thus, the most explanatory regression equation was found on the second

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PHILLIPS ET AL.

step. This equation is significant at the .05 level and accounts for approximately 25% of the variance in the attitudinal criterion. A different order of entry was established for the regression analysis using the cognitive factor of vocational maturity as the criterion variable. Using the zero-order correlation estimates (indicated in parentheses), the following order of inclusion was determined: decision-making style: intuitive (- .38), scholastic achievement (.32), perceived effect of environment on past vocational development (.20), decision-making style: dependent (- .19), and decision-making style: rational (.08). As indicated in Table 2, only the regression equation resulting from the inclusion of the negatively weighted intuitive decision-making style variable reached significance. The addition of scholastic achievement, perceived effect of environment, and dependent or rational decision making on subsequent steps failed to make significant contributions to the explained variance. Thus, for the disadvantaged group, the most explanatory regression equation for the cognitive criterion consists of only one variable-intuitive decision-making style. This equation is significant at the .05 level and accounts for approximately 14% of the variance in the cognitive criterion. DISCUSSION The illustration reported here examined the impact of a variety of career-related variables on the attitudinal and cognitive aspects of vocational maturity among two special population groups. Drawing upon the findings of Phillips and Strohmer (1982), variable under consideration included level of scholastic achievement and endorsement of rational, intuitive, and dependent decision-making styles. Also included were two population specific variables related to the characteristics of membership in each of the two special groups. Small sample sizes were used for this exploratory study, and the reader is cautioned against drawing firm conclusions regarding the reported results. The regression analyses conducted to explore the relationships between vocational maturity and these predictor variables indicate, in general, that variance in the attitudinal and cognitive factors of vocational maturity can be at least partially explained by the variables considered. The relative contributions of the predictor variables, however, and the resulting proportion of variance explained differs according to which population and which criterion factor are under examination. In the disabled sample, 46% of the variance in attitudinal maturity can be explained by a combination of scholastic achievement, use of a rational, nondependent, decision-making style, and the subject’s perceived effect of disability. The single most powerful predictor of both attitudinal and cognitive vocational maturity for this group appears to be scholastic achievement. All variables considered contributed to an explained total of 56% of variance in cognitive maturity, although a major difference from the attitudinal equation is the positive weighting of the dependent

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25

decision-making style. Thus, from these findings it would appear that once scholastic achievement is accounted for, the disabled individual who is attitudinally mature may be one who endorses a rational, nondependent, decision-making style; one who is cognitively mature may be one who relies heavily on the decision-making expertise of others. This disparity might be best explained in view of undeniably central role of external support services in the plans of a disabled individual. In subsequent investigations of this kind, it will be necessary to explore this observed disparity between decision-making styles associated with different aspects of maturity. Among the disadvantaged sample, however, the variables considred were far less effective in predicting vocational maturity: Scholastic achievement, endorsement of a dependent decision-making style, and the perceived effect of environment all failed to contribute to a significant regression equation for either vocational maturity factor. Furthermore, only 25% of the variance in attitudinal maturity and 14% of the variance in cognitive maturity could be adequately explained by the remaining variables. It is interesting to note that the intuitive decision-making stylenegatively weighted-served as the single most powerful predictor for both factors. Although variance in the attitudinal factor could also be explained by the inclusion of a positively weighted use of a rational decision-making style, 75% of the variance remains unexplained. It is apparent from these findings that the vocational maturity of this disadvantaged group cannot be satisfactorily understood with the limited range of variables considered in this study. It would appear from these findings that the role of nonintuitive decision strategies in the vocational maturation of disadvantaged groups would be a desirable avenue for continued investigation. Although caution must be used in generalizing from findings based on regression analyses conducted on data drawn from samples of the size used in this study, it is interesting to compare the current findings with those of Phillips and Strohmer (1982). In a study using nondisabled, nondisadvantaged students, they found the strongest predictors of attitudinal and cognitive vocational maturity combined to be scholastic achievement and the lack of a dependent decision-making style. Although the importance of scholastic achievement in the disabled sample appears equivalent, the similarity ends for both disabled and disadvantaged groups, once decisionmaking style is considered. Further investigation of the relative importance of different decision-making styles among the three groups is clearly warranted. Such investigations would profitably include not only relative endorsement of decision-making styles, but also style correlates and an estimate of both attitudinal and cognitive factors of vocational maturity. A comment should be noted in regard to the inclusion of group membership variables in the study of vocational maturity. The findings reported

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here suggest that the direction and nature of the impact of disability on vocational development are profitable avenues for future research. The role of a disadvantaged environment, however, necessitatesfurther definition and exploration. It is not clear whether that variable failed to contribute to the explanation of maturity because of inadequate measurement of insufficient sample size or becauseof genuine lack of impact. Additional exploratory research on the role of environmental factors in the vocational maturation of disadvantaged students would clarify this question. To summarize the illustration provided, the goal of this phase of investigation has been to generate a new set of hypotheses to be tested, a new set of questions to be clarified, and a refined set of phenomena to be observed concerning the correlates of vocational maturity in the two special groups. Given the limitations of the small sample sizes and the use of self-report instrumentation, the attempt to meet this goal was moderately successful. It is apparent from the results presented here that the use of different decision-making styles has a significant but differential impact on the vocational maturity of disabledand disadvantaged individuals. Based on these findings, the next steps in the proposed framework would include (a) an investigation of the role of different decision-making styles in attitudinal and cognitive vocational maturity among disabled individuals, (b) an investigation of the role of nonintuitive decision strategies among disadvantaged individuals, (c) an exploration of the impact of disability on vocational development,and (d) a clar&ation and redefinition of the disadvantaged environment variable. These future directions, while specific to the populations and questions under investigation, are illustrative of those which would emerge from research in which both descriptive and hypothesis-testing paradigms are combined. By conducting a series of thus sequentially modified studies, it would be hoped to arrive at a well-refined set of variables which, in this case, seem to be the most significant contributors to the vocational maturation of members of special populations who also college students. This new set of variables and the relationships observed among them then becomes the point of departure for an identical line of investigation that uses the special population college data as a base and examines how those data compare with those obtained from a noncollegepopulation. This sequential pattern of testing hypotheses, exploring new variables, and testing revised hypotheses avoids the perils mentioned earlier, for example, that it is automatically assumed that those variables determined to be highly significant with traditional groups will be as important for special populations and that the lessons learned in past investigationsare entirely disregarded. The continuation of this pattern with different age groups, each serving as a comparative base for the one following, should eventually yield a clearer picture of not only the nature of vocational development for

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special populations, but also the group differences within the populations under study. IMPLICATIONS AND CAUTIONS

The framework described here is one which attempts to address the criticism of theoretical and experimental bias in the investigationof special populations career development. Having described a rather ambitious framework for future research, however, it seems necessary to include several cautions about its use. It would be inaccurate to suggest that this framework is without its own troublesome dilemmas. Some of those dilemmas are foreseeable; others will be encountered, no doubt, as the line of investigation progresses. One such foreseeable dilemma is that of the selection of an appropriate criterion measure. Although one world of work has been assumed, there are multiple ways of defining an individual’s status within that world. Each definition may also be measuredin a variety of ways. This framework does not address the problem of what standardsto use in the measurement of such criteria as success, satisfaction, or maturity. Without clear and valid criteria, attempts to describe, predict, or improve career development among special populations will remain potentially misguided efforts. In future applications and extensions of this framework, it will be important to address this issue. A similarly troublesome area is that of the manner in which new variables are added for consideration. Earlier in this paper it was noted that one source of bias in current research was the inclusion of only variables derived from existing theory. In the proposedframework variables are added in three ways: when they are suggestedby current literature, when they appear intuitively or empirically promising, and when they are suggestedby the descriptive/exploratoryphase of the project. Although the latter methods address the earlier bias, There clearly remains the possibility of excluding those variables whose real significance is neither known, suspected, nor recommended by inquiry. A third dilemma is seen in the sequentialmod&ation of data collection. Although the benefit of creating internally generatedhypothesesis derived from that kind of progression, each subsequent data base is slightly different from that collected previously. Comparative power is therefore limited to those variables which were observed on a previous step. If comparative analysis is to be employed in addition to the recommended exploratory analysis, the number of necessary separate investigations increases geometrically, yielding quickly a lifetime program of required data collections. Furthermore, each data collection requires the availability of a sufficiently large quantity of special population subjects in order to support the data analysis of an increasingnumber of independentvariables.

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Despite this list of troublesome dilemmas, this framework provides a model of combining traditional and alternative methodological paradigms. It is presented, therefore, with the hope that it will serve to stimulate methodological improvements in career research. In view of the scarcity of empirical efforts with the career development of special populations, this framework is most enthusiastically directed toward those investigators examining the concerns of diverse groups. It is hoped that in so doing, modifications, revisions, and improvements may be made to the benefit of future special populations career research and to more adequately formulated and tested models which are able to explain career behaviors of traditional as well as special populations. REFERENCES Barker, R. G., Explorations in ecological psychology. American Psychologist, 1965, 20, 1-14. Cohen, J., & Cohen P. Applied multiple regressionlcorrelation analysis for the behavioral sciences. New York: Halsted, 1975. Dillard, J. M. Some unique career behavior characteristics of blacks: Career theories, counseling practice, and research. Journal of Employment Counseling, 1980,17, 288298. Dilley, J. S., Decision making ability and vocational maturity Personnel and Guidance Journal, 1965, 44, 423-427. Egner, J. R., & Jackson, D. T., Effectiveness of a counseling intervention program for teaching career decision making skills. Journal of Counseling Psychology, 1978, 25, 45-52. Harren, V. A., Kass, R. A., Tinsley, H. E. A., & Moreland, J. R. Influence of sex role attitudes and cognitive style on career decision making. Journal of Counseling Psychology, 1978, 25, 390-398. Holcomb, W. R., 8t Anderson, W. P. Vocational guidance research: A five-year review. Journal of Vocational Behavior, 1977 10, 341-346. Jordaan, J. P., L Heyde, M. B. Vocational maturity during the high school years. New York: Teachers College Press, 1979. Krumboltz, J. D. The effect of alternative career decision making strategies on the quality of resulting decisions. Final report. Stanford, Calif.: Stanford University, 1979 (ERIC Document Reproduction Service No. ED 195 824). Moreland, J. R., Harren, V. A., Krimsky-Montague, E., & Tinsley, H. E. A. Sex role self concept and career decision making. Journal of Counseling Psychology, 1979, 26, 329-336. Myers, R. A., Lindeman, R. H., Thompson, A. S., & Patrick, T. A. Effects of educational and career exploration system on vocational maturity. Journal of Vocational Behavior, 1975, 6, 245-254. Osipow, S. H. The relevance of theories of career development to special groups: Problems, needed data, and implications. In S. Picou & R. Campbell (Eds.), Career behavior of special groups. Columbus, Ohio: Merrill, 1975. Phillips, S. D., & Strohmer, D. C. Decision making style and vocational maturity. Journal of Vocational Behavior, 1982, 20, 215-222. Rubinton, N. Instruction in career decision making and decision making styles. Journal of Counseling Psychology, 1980, 27, 581-588. Schenk, G. E., Johnston, J. A., L Jacobsen, K. The influence of a career group experience

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on the vocational maturity of college students. Journal of Vocational Behavior, 1979, 14, 284-296. Smith, E. J. Profile of the black individual in vocational literature. Journal of Vocational Behavior, 1975, 6, 41-59. Super, D. E. (Ed.) Measuring vocational maturity for counseling and evaluation. Washington, D.C.: National Vocational Guidance Assoc. 1974. Super, D. E., & Overstreet, P. L. Vocational maturity of ninth grade boys. New York: Teachers College Press, 1960. Thompson, A. S., Lindeman, R. H., Super, D. E., Jordaan, J. P., & Myers, R. A. Career Development Inventory. Palo Alto, Calif.: Consulting Psychologists Press, 1981. Wachtel, P. L. Investigation and its discontents: Some constraints on progress in psychological research. American Psychologist, 1980, 35, 399-408.

REFERENCE NOTE 1. Harren, V. A. Assessment of career decision making (ACDM). Preliminary manual. Unpublished manuscript, 1980 (Available from Dr. Howard E. A. Tinsley, Department of Psychology, Southern Illinois University, Carbondale, Ill. 62901). Received: March 17. 1981.