Journal of Counseling Psychology 1999, Vol. 46, No. 2, 207-217
Copyright 1999 by the American Psychological Association, Inc. O022-0167/99/$3.0O
Applying Vocational Interest Models to Naturally Occurring Occupational Perceptions Victoria A. Shivy
James Rounds and Lawrence E. Jones
Virginia Commonwealth University
University of Illinois at Urbana-Champaign
The authors examined the structure of naturally occurring occupational perceptions. They first assessed the stability of individuals' perceptions of 18 occupational titles, both across time and 2 samples. They then tested whether J. L. Holland's (1992) spatial model, D. J. Prediger's (1982) dimensional model, or I. Gati's (1979) classificatory model could be identified in the data. Findings are discussed in light of previous research and suggest the marked stability of the occupational perceptual structure as well as support for Gati's (1979) classificatory model.
Structural studies of occupational titles have resulted in two distinct research literatures that are rarely synthesized or even considered in tandem (Rounds & Zevon, 1983). The literature most familiar to vocational psychologists involves structural studies of occupational preferences. Another literature, less well-known, reflects the efforts of sociologists, and focuses on the examination of individuals' occupational perceptions. Individuals' occupational perceptions encompass their cognitions concerning specific jobs, whether these cognitions arise from observation or direct experience with those jobs (e.g., Tinsely, Bowman, & Ray, 1988). This study operationalized occupational perceptions by way of the method of paired comparisons (Thurstone, 1927) and examined the structure of occupational perceptions in light of the models that have emerged from the study of occupational preferences. Given that vocational theorists continue to speculate on the importance of acquiring a cognitive structure for organizing information about the self, the world of work, and one's relation to that work world (Gati, 1984; Holland, Magoon, & Spokane, 1981; Peterson, Sampson, & Reardon, 1991), it is Victoria A. Shivy, Department of Psychology, Virginia Commonwealth University; James Rounds, Department of Educational Psychology, University of Illinois at Urbana-Champaign; Lawrence E. Jones, Department of Psychology, Division of Quantitative Methods, University of Illinois at Urbana-Champaign. This study was designed and the data were collected while Victoria A. Shivy was a postdoctoral trainee in the Department of Psychology, Division of Quantitative Methods, University of Illinois at Urbana-Champaign, and was supported by a National Institute of Mental Health/National Research Service Award. We benefited from the expert assistance and helpful comments of Brad Crouch, Laura Koehly, Lawrence Hubert, and Jacqueline Meulman. Thanks are also due to Ev Worthington, who offered comments on a draft of this article; Brigitte Seraphin who carried out much of the data collection and coding; and Carlton Gallagher who helped with manuscript preparation. A copy of all proximity matrices and ratings taken on unidimensional attribute scales can be obtained from Victoria A. Shivy. Correspondence concerning this article should be addressed to Victoria A. Shivy, Department of Psychology, P.O. Box 842018, Virginia Commonwealth University, Richmond, Virginia 232842018. Electronic mail may be sent to
[email protected]. 207
surprising that all but a few vocational researchers (Gati & Nathan, 1986; Shivy, Phillips, & Koehly, 1996) have neglected the study of occupational perceptions. In contrast, researchers from outside mainstream vocational psychology have shown considerable interest in individuals' occupational cognitions. These investigators have not only hinted at the role that organized occupational information might play in various aspects of the career development process, but they have also conducted substantial empirical research (e.g., Burton, 1972; Coxon, Davies, & Jones, 1986; Coxon & Jones, 1974a; Magana, Burton, & Ferreira-Pinto, 1995; Reeb, 1959, 1971, 1974, 1979; Rowell, 1985; Shubsachs & Davison, 1979). Their reasoning is typified by Rowell's (1985) statement that "if mental organization is conceptualized as playing a causal role in action, it would seem desirable to produce representations of this organization to aid our understanding of both existing situations and their potential for change" (p. 201). Most of these researchers used the method of paired comparisons to understand occupational perception, and then considered occupational perception with regard to individuals' behavior. Hence, they explored individuals' judgments regarding the similarities and dissimilarities among various occupations, instead of their stated occupational preferences or expressed vocational interests. Nearly all vocational researchers who conduct structural explorations of occupational phenomena (Gati, 1991; Reeb, 1979; Rounds & Tracey, 1996) have acknowledged the purported relationship between individuals' occupational cognitions, their vocational preferences, and subsequent career behavior. Most have either tacitly or explicitly (Gati, 1979) adopted Holland's (1976a, 1985, 1992) position that individuals' occupational perception structure resembles their occupational preference structure. From Holland's (1976a, 1992) perspective, both structures are organized according to his RIASEC model, which posits that individuals behave in ways that reflect their resemblance to his vocational interest types of realistic (R), investigative (I), artistic (A), social (S), enterprising (E), and conventional (C). The structural relations among the types, when taken together, are often termed the hexagonal model. Holland's spatial model specifies that types that are more proximate on
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the hexagon are more similar than types that are more distant. Holland (1976a, 1992) took a strong position concerning the equivalence of the occupational cognition and occupational preferences structures, because such a stance is necessary for the coherence of his theory. If individuals are indeed able to seek appropriate vocational experiences then, as matching theories (e.g., Parsons, 1909) dictate, individuals must have some basis for judging the concordance of their occupational preferences with their perceptions of existing occupational environments. Holland (1992) offered his RIASEC hexagon as the major instrument in this process. Other theorists, although not disagreeing with Holland's arguments, have been a bit more cautious in characterizing the relationship between occupational perceptions and preferences. For example, Dawis (1991) limited his commentary to the observation that the constructs of vocational interests, values, and preferences all "have to do with the affective evaluation of cognitive representations of reality" (p. 839). One purpose of the present article was to test whether Holland's (1992) model emerges from structural analyses of occupational perception data. A second way of representing the RIASEC types, a dimensional model, has been advanced by Prediger (1982), who proposed that the two bipolar work-task dimensions of things/people and data/ideas underlie the RIASEC relational scheme. Prediger described these dimensions as foundational, suggesting that they capture the basic aspects of human activity and experience (Prediger, 1982; Prediger, Swaney, & Mau, 1993). He also suggested that the things/ people and data/ideas dimensions can be considered as an elaboration of the Holland scheme; thus, Prediger's notions are not strictly competitive with Holland's RIASEC structure (Rounds & Tracey, 1993). Prediger's dimensions have received both attention in the literature and some empirical support, based on analyses of occupational preferences and perceptions data (Rounds & Tracey, 1993; Shivy et al., 1996). A second purpose of this article was to test for the emergence of Prediger's dimensions in occupational perception data. In contrast to both Holland's (1992) spatial model and Prediger's (1982) dimensional model, Gati (1979,1991) has proposed a third, classificatory model that he claims better accounts for the relations among RIASEC types. In particular, Gati (1979,1991) proposed a parsimonious, three-group partition of the Holland types (R, I), (A, S), and (E, C), that he claimed shows a better fit than a circular order model. Gati's (1979, 1991) discrete representation predicts that the correlations between RIASEC types belonging to the same partition (e.g., R and I, or A and S) are greater than the correlations between pairs of RIASEC types belonging to different partitions (e.g., R and A, or I and A), and Gati (1984; Gati & Nathan, 1986) has applied his vocational interest model to occupational perception and occupational preference data. In a series of articles, Gati and his colleagues (e.g., Benyamini & Gati, 1987; Gati, 1984; Gati & Winer, 1987) have compared his hierarchical tree model with Roe's (1956) spatial model to account for occupational similarities, and this work largely has shown that the best fit
to Roe's fields comes from his hierarchical model. This finding however, has received serious challenges from other researchers. For example, Tracey and Rounds (1994) demonstrated that Gati's (1979, 1991) model comparisons were based on incomplete specifications of the predictions generated from both Gati's model and Roe's (1956) models, and that Gati used inappropriate statistical tests. Furthermore, Roe's circular order hypothesis regarding her occupational fields has generally yielded negative results (Rounds & Zevon, 1983) when applied to vocational interests. Therefore, Holland's spatial model, which has received much more empirical support than has Roe's, seems a better benchmark by which to evaluate spatial versus classificatory models. A third purpose of this article was to assess the fit of Gati's model to the occupational perceptions data, as well as to compare it with Holland's model. Studies that focus on the structure of naturally occurring occupational perceptions (e.g., Coxon et al., 1986; Magafia, et al., 1995; Reeb, 1979; Shubsachs & Davison, 1979) have received only limited attention in mainstream vocational psychology. These studies have enhanced our understanding of "occupational images" (Coxon & Jones, 1974a) or "occupational stereotypes." By using the direct similarity judgments that individuals make regarding occupational titles, gathered by means of paired comparison procedures or sorting tasks, investigators have been able to depict individuals' cognitive structures regarding the occupational world. Structural analysis of similarity data yields direct representations of the ways in which people perceive occupations, without the imposition of any particular theory or specific classificatory scheme. Hence, the best test for the emergence of any of the above-mentioned vocational models would seem to be a test that takes, as its starting point, the analysis of individuals' occupational cognitions. Few such studies, however, have been conducted. On the basis of their review of the naturally occurring occupational perception literature, Rounds and Zevon (1983) concluded that individuals' occupational cognitions are not organized in a fashion resembling the RIASEC hexagon; this conclusion seems to be supported by more recent research (e.g., Magafia et al., 1995; Rowell, 1985). Nevertheless, even recent studies have omitted the range of occupational titles that would be necessary to identify the dimensions that underlie the six RIASEC categories. Instead, most of this work has been directed toward the exploration of the prestige hierarchy (Coxon et al., 1986; Magana et al., 1995) that is thought to be associated with various occupations. This focus is not surprising, given that most of these investigations have been carried out by sociologists rather than vocational psychologists. The structural analysis of occupational perceptions seems consistently to reveal a prestige dimension (Reeb, 1959, 1979; Shivy et al., 1996; Shubsachs & Davison, 1979). Although this finding is certainly of interest to vocational psychologists, many other important questions remain unanswered. For example, if occupational perceptions either underlie occupational preferences or are important for personoccupation matching, then perceptions should be stable across time. Although there is a considerable amount of
OCCUPATIONAL PERCEPTIONS
information pertaining to the stability of preference scores (Swanson, in press), there is little or no information available that concerns the stability of vocational preference structures. It seems that researchers and practitioners assume that if preference scores are stable then the structure that underlies them is also stable. There is indirect evidence to support this stability assumption for preferences, as it is rare to find a sample of RIASEC scores that does not yield a circular structure (Dawis, 1992). A very different situation exists, however, with regard to occupational perceptions. We were unable to locate any studies that examined either the stability of occupational perceptions or of occupational perceptions structures. Thus, in this article we also examined the stability of occupational perceptions' structures. In sum, the major goals of the current study were to examine whether naturally occurring occupational perceptions are organized according to theory, and to examine their stability across both time and two samples. We tested whether Holland's spatial model, Prediger's (1982) dimensional model, or Gati's (1979, 1991) classificatory model could be identified in our data. With regard to Holland's (1992) model, we investigated whether occupations were organized according to his hypothesized RIASEC categories, and evaluated the circular-order arrangement of these categories. For Holland's model to be supported, the occupations should not only map onto their respective RIASEC categories but should also distribute themselves, spatially, in the circular fashion of R-I-A-S-E-C. Evidence in support of Prediger's dimensions—working with data, working with ideas, working with things, and working with people— would emerge from our attempts to identify the dimensions associated with the spatial representation of the occupational perceptions. Attempts to identify the dimensions underlying the spatial representation would also allow us to test whether the dimensions of prestige and the related constructs of the masculinity and femininity of occupations (L. S. Gottfredson, 1981; Hesketh, Hesketh, Hansen, & Goranson, 1995) accounted for variance in our scaling solutions. For the occupational perceptions to be organized according to Gati's model, three specific clusters, (R, I), (A, S), and (E, C), should emerge from the analysis. Method
Participants Career development and exploration class. Ninety-one college students from three sections of a career development and exploration (CDE) course offered at a large Midwestern university participated in the study in exchange for course credit. Six students who did not complete Time 2 questionnaires were dropped from the analysis, resulting in a final sample of 85 students. These participants included 31 men (36.5%) and 54 women (63.5%). With respect to ethnic background, 23.5% of the sample was African American, 7.1% was Asian American, 64.7% was Caucasian, 3.5% was Latino/Latina, and 1.2% indicated "other." Initially, 27.4% of individuals who participated in this study indicated that they were "decided" with respect to college major. By the end of the semester this figure had increased to 64.3%. The typical participant in this study had completed 2.1 semesters of collegiate studies (SD - 1.06), was 19.1 years old (SD = 1.17), and had achieved a cumulative
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grade point average of 3.93 on a 5-point scale (SD = 0.60). He or she expressed considerable interest in career issues, with the average participant initially rating his or her level of interest in personal career development issues as being 6.01 on a 7-point scale (SD = 1.09) and, upon Time 2,5.69 (SD = 1.29). Psychology subject pool (PSP). These participants included 51 men (53.1%) and 45 women (46.9%). With respect to ethnic background, 6.2% of the sample was African American, 18.6% was Asian American, 64.9% was Caucasian, 6.2% was Latino/Latina, and 4.1% indicated "other." Seventy-six percent of individuals in this group indicated that they were "decided" with respect to college major. The average PSP participant had completed 2.05 semesters of collegiate studies (SD = 1.63), was 18.9 years old (SD = 1.14), and had achieved a cumulative grade point average of 4.06 on a 5-point scale (SD = 0.65). As a group, these students also expressed considerable interest in personal career development, with a mean rating of 5.26 on a 7-point scale (SD = 1.47).
Occupational Stimuli The 18 occupational titles that formed the basis for the judgment tasks were chosen rationally and were intended to be familiar to the participants. After conducting an informal review of the occupational titles used in similar research efforts, we selected 6 occupational titles that varied with respect to Holland code, as presented in Gottfredson and Holland's Dictionary of Holland Occupational Codes (G. D. Gottfredson & Holland, 1989). Each of these titles required an advanced level of educational preparation for entry. We then chose 6 more occupational titles, again 1 from each of Holland's categories, that required a more moderate level of educational preparation for entry. The remaining 6 tides chosen required little formal education. The occupational titles were balanced across second-letter Holland code, such that within Holland category, no two jobs shared the same secondary Holland category. We also attempted to include several occupational titles that were dominated by men (e.g., automobile mechanic) and several that were dominated by women (e.g., nursery school attendant). Hence, our selection of occupational titles can best be described as an iterative process, guided by our concerns with issues of the representativeness and the feasibility (i.e., the number) of the occupational titles (Coxon & Jones, 1974b). There was, however, considerable overlap between our stimuli and those used in previous investigations. The occupations chosen (with threeletter Holland code listed in parentheses) were airplane pilot, commercial (RIS); automobile mechanic (RSE); farm worker (REC); mathematician (IER); surgical technician (ISR); laboratory assistant (IRC); novelist (AES); instrumental musician (ASI); cake decorator (ARE); minister/priest/rabbi (SAI); occupational therapist (SRE); nursery school attendant (SER); lawyer (ESA); manager, golf club (ECS); door-to-door salesperson (ERS); certified public accountant (CRS); bank teller (CSE); and cashier/checker (CER).
Instrumentation Similarities-dissimilarities task (paired comparisons ratings). Participants made judgments of overall similarity between all pairs of occupational titles (N = 153). Ratings were made along a 9-point scale (1 = very similar to 9 = very dissimilar). The presentation of stimulus pairs, which was identical across participants, was made by means of a Wells (1991) ordering so mat the potential for response bias was minimized. To establish an appropriate "set" for this rating task, we instructed participants first to look over all stimulus items and to form an impression regarding the
SfflVY, ROUNDS, AND JONES
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most and least similar stimulus pairs. They were then asked to rate these pairs with a 1 and the least similar pair with a 9, respectively. Unidimensional rating scales. To objectify our efforts to identify the dimensions that may emerge from multidimensional scaling (MDS) analyses, we had participants rate the 18 occupational titles on a number of 9-point attribute scales. We used previous research (L. S. Gottfredson, 1981; Prediger, 1982; Shivy et al., 1996) as a basis for inclusion of the following scales: the degree to which each occupation is prestigious; whether each occupation involves working with data, ideas, people, and things (four scales); and the degree of masculinity and femininity of each occupation (two scales). The stems for the rating scales, adapted from Prediger (1982), were as follows: "Please rate each of the occupational titles with regard to prestige (prestige)"; "Please rate each of the occupational titles with regard to whether the job involves impersonal tasks involving facts, records, files, numbers, and systematic procedures for assisting goods and services consumption by people (data)"; "Please rate each of the occupational titles with regard to whether the job involves intrapersonal tasks involving abstractions, theories, knowledge, insights, and new ways of expressing something (ideas)"; "Please rate each of the occupational titles with regard to whether the job involves interpersonal tasks such as caring for, persuading, entertaining, or directing others (people)"; and "Please rate each of the occupational titles with regard to whether the job involves nonpersonal tasks involving machines, materials, tools, biological mechanisms, etc. (things)." For the masculinity and femininity scales, the general stem was as follows: Please rate each of the occupational titles with regard to whether you think mostly men/mostly women are in this occupation. Note that this is not a question of whether you think men/women should hold the jobs; rather, it is simply a question of whether you think they actually do. It should be noted that the order of presentation of the occupational titles was randomized within each unidimensional scale to minimize response bias.
Procedure For the participants enrolled in the CDE class, materials were distributed both early in the Spring 1995 semester, before participants received exposure to formal vocational theory, and again at semester's end. The PSP participants completed materials only at the end of the Spring semester, 1995. As indicated previously, the questionnaire packet included forms that requested demographic information and career-status variables, the paired comparison similarity judgment task, and the unidimensional rating scales.
Model Specifications We sought to examine the stability of occupational perceptions, and whether Holland's (1992) spatial model, Prediger's (1982) dimensional model, or Gati's (1979) classificatory model could be identified in occupational perception data. We were primarily interested, however, in examining the degree to which Holland's (1992) circular order and Gati's (1979) classificatory model fit the data. Because the methods used to assess the degree of model-data fit are somewhat involved, we introduce our analytic plan in the section below. Holland's (1992) model. Holland's (1992) structural hypothesis, formally called the calculus assumption, states that the six RIASEC types are arranged in a circular order and that the interpoint distances for the types are "inversely proportional to the
theoretical relationships between them" (p. 5). If the circular structure is an appropriate model, then it follows that the similarity among types has a predictable order: The greatest degree of similarity should be observed between the six adjacent types (RI, IA, AS, SE, EC, and CR), which should be greater than the degree of similarity among the six alternate types (RA, AE, ER, IS, SC, and CI). In turn, the degree of similarities among the alternate types should be greater than the degree of similarity for the three opposite types (RS, IE, and AC). Finally, and by implication, the similarities for the adjacent types should be greater than the similarities for the opposite types. As Tracey and Rounds (1993) discussed, the circular-order model generates a total of 72 order predictions. Gati's (1979) classificatory model. In contrast, Gati (1979) represented the relations among the six personality types by means of the single, three-group partition [(R, I), (A, S), (E, C)]. According to Gati (1982, p. 172), the similarity among the six types has a predictable order: The degree of similarity for RI, AS, and EC should be greater than similarities for the remaining 12 pairs of types (RA, RS, RE, RC, IA, IS, IE, IC, AE, AC, SE, and SC). The three-group partition model generates, according to Gati's claim, 36 order predictions (see Rounds & Tracey, 1996, for a list and discussion of Holland's and Gati's predictions).
Randomization Test of Hypothesized Order Relations Hubert and Arabie's (1987) randomization test was used to evaluate the fit of Holland's and Gati's models. First, the occupation similarity values from the original two-way, one-mode matrix (i.e., 18 occupational titles X 18 occupational titles—introduced in the Occupational Stimuli section of this article) were summed for each RIASEC type, yielding a 6 X 6 aggregated RIASEC matrix of (dis)similarities. Then a randomization test of hypothesized order relations (Hubert & Arabie, 1987) was used to evaluate the fit of Holland's and Gati's models to this aggregate RIASEC matrix. In the randomization test, the fit of the hypothesized set of order predictions to the original RIASEC matrix is compared with the fit attainable from all possible rearrangements (called relabeling) of the columns and rows of the original matrix. The comparison of the observed number of confirmed predictions for the original matrix with the distribution of confirmed predictions generated by the relabeling strategy yields an exact probability that the objects can be relabeled at random (i.e., a p value, in the usual sense) for the observed index under the null random relabeling conjecture. The application of the randomization test to RIASEC data was wellexplained, and its utility was demonstrated, in Rounds, Tracey, and Hubert (1992). Hubert and Arabie (1987) also proposed a correspondence index as an indicator of relative model-data fit within a proximity matrix. The correspondence index is the difference between the proportions of agreements and disagreements, that is, the number of order predictions met (agreements) minus the number of violations of the order predictions (disagreements), each divided by the total number of order predictions made. The correspondence index has several desired properties: It is a normalized descriptive statistic indicating the degree to which the ordered predictions are satisfied and it varies from —1 to + 1 . This index was used as a means of comparing Holland's circular order model and Gati's three-group partition.
Results Assessing Dimensionality of the Spatial Solutions Exploratory MDS techniques were used to generate the spatial representation of participants' occupational percep-
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OCCUPATIONAL PERCEPTIONS
tions. The first step in this process, and initial evidence that can be brought to bear regarding the appropriateness of the given models, pertains to the dimensionality of the scaling solution. Holland's (1992) model is a two-dimensional circular model. Hence, for Holland's (1992) model to receive support, a two-dimensional scaling solution should be obtained. In this section we assess the dimensionality of the spatial solutions. CDE class. To justify our decision to treat the three different class sections as one CDE sample, we performed several preliminary analyses. First, separate KYST-2A (Kruskal, Young, & Seery, 1977) scaling solutions were computed from each class section's aggregated dissimilarities data, and these were compared across class sections, at Time 1 and Time 2. Second, mean ratings of the occupational titles on all of the unidimensional scales were also obtained and compared across class sections by running several analyses of variance. Given that no substantial differences were found with regard to either the scaling solutions or the unidimensional scales, we felt confident that we could treat our three class sections as the single CDE sample. Nonmetric MDS techniques were used for the major analyses. Specifically, the KYST-2A (Kruskal et al., 1977) program was used to scale the two-way, one-mode matrices (i.e., 18 occupational titles X 18 occupational titles) of dissimilarity data. Scaling solutions were computed in two through six dimensions, and 10 random starts were used to reduce the probability of obtaining local minima. Stress is considered to be a "badness-of-fit" index with larger values indicating a poorer solution. We relied on stress values and solution interpretability to guide our choice of appropriate dimensionality (Jones & Koehly, 1993). For the Time 1 condition, the stress values for the two- through sixdimensional solutions were, respectively, .223, .129, .081, .056, and .038. At Time 2 the stress values were .221, .127, .080, .056, and .042. We also compared our results with Monte Carlo studies (Spence & Olgivie, 1973) that sug-
gested that random stress values for a three-dimensional solution with 18 stimuli should approximate .198. Our observed stress for both conditions was more than 3 SDs (SD = 0.009) below the Monte Carlo value. Also, because the differences in stress values between the four-dimensional and three-dimensional solutions for both the Time 1 and Time 2 groups were both below .05, we believed that the improvement in model fit following the addition of another dimension was not appreciable. We therefore chose the three-dimensional scaling solutions as the "best fit" solutions for both the Time 1 and Time 2 conditions. PSP. Nonmetric MDS techniques were again used for the analyses. The two-way, one-mode matrix of dissimilarity data was scaled and solutions were computed in two through six dimensions. Stress values for the two- through sixdimensional solutions were, respectively, .233, .142, .086, .052, and .039. Observed stress was again more than 3 SDs (SD = 0.009) below the Monte Carlo value, and the difference in stress values between the four-dimensional and three-dimensional solutions was .056. We therefore chose the three-dimensional scaling solution as the best fit for the PSP sample.
Assessing Stability of Occupational Perceptions Given that the three-dimensional solutions best fit the data at Time 1, Time 2, and for the PSP group, we then determined whether the scaling solutions and, thus, participants' perceptions of the occupational stimuli, were stable both across time and the two samples. To compare the solutions, we first performed an orthogonal rotation of both the Time 1 scaling solution and the PSP solution to maximal congruence with the Time 2 solution (ten Berge, 1977). We then examined the correlations (Pearson rs) among the three dimensions of each solution. These correlations are presented in Table 1. The patterning of dimensional associations, which reveals strong and significant correlations along the main diagonal(s) and nonsignificant correlations else-
Table 1 Intercorrelations Between the Three Scaling Solutions Scaling solution CDE Time 1 Dim 1 Dim 2 Dim 3 CDE Time 2 Dim 1 Dim 2 Dim 3 PSP Dim 1 Dim 2 Dim 3
CDE class time l a Dim 1
Dim 2
Dim 3
1.00 -.08 .02
1.00 -.00
1.00
.99* -.05 .01
-.05 .99* .00
.99* -.05 .02
-.06 .90* -.04
CDE class time 2a Diml
Dim 2
Dim 3
.01 .00
1.00 .00 .00
1.00 .00
1.00
.01
.99* -.02 .01
-.02 .89* .03
.01 -.03 .92*
.98* -.02 .93*
PSP" Dim 1
Dim 2
Dim 3
1.00 -.05 .01
1.00 .05
1.00
Note. CDE = career development and exploration class; PSP = psychology subject pool; Dim : dimension. *p < .01.
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where, suggests that occupational perceptions were quite stable, with correlations ranging from .89 to .99. These results imply that the ordering of the occupational stimuli on the dimensions was virtually the same at Time 1 and Time 2 for the CDE sample, as well as for the CDE and PSP samples. In addition to using Pearson rs, we also used a congruence coefficient (Tucker, 1951) to assess the stability of the Time 1 and Time 2 scaling solutions at the level of the modelderived distances. Again, our results suggested no differences, with an obtained congruence coefficient of rc = .99. Thus, it may not be an overstatement to say that participants' naturally occurring occupational perceptions were remarkably stable across samples and time. Because of this stability in scaling solutions and, hence, in our participants' perceptions of occupations, we decided to simplify our analyses and we therefore present and discuss the classificatory, spatial, and dimensional representations from only one sample, the CDE Time 1 sample, rather than from all the representations.
Spatial Representations of Occupational Perceptions To evaluate Holland's (1992) model, we examined the three-dimensional MDS solution at Time 1. As mentioned previously, Holland's model is a two-dimensional circular model. Hence, the first challenge to Holland's spatial model pertains to the dimensionality of the spatial representation of the data, which was found to be three-dimensional. Although Holland's (1985) model was not supported in terms of the dimensionality of the scaling solution, some support for Holland's (1985,1992) categories did seem evident from the dimension-by-dimension scatterplots that we constructed
Farm Worker
from the data. Note that Figures 1 and 2 depict, respectively, the 1 X 2 and the 1 X 3 planes of the three-dimensional scaling solution. To facilitate visual inspection, we plotted not only the 18 occupations, but also the averaged location of occupations within a particular Holland type. For example, in Figure 1, farm worker (REC), automobile mechanic (RSE), and airplane pilot (RIS) were reasonably proximate and, in this study, define the Holland R category. Furthermore, and also in Figure 1, bank teller (CSE), certified public accountant (CRS), cashier/checker (CER), and mathematician (IER) were also quite proximate and, with the exception of mathematician, define the Holland C category. Although there appeared to be some support for the existence of the Holland categories (indicated, in the figures, by the uppercase italicized letters R, I, A, S, E, and C), Holland's hypothesized circular order was not apparent. However, simple visual inspection of MDS configurations is inadequate from an empirical perspective. Hence, we applied the randomization test to the 6 X 6 aggregated RIASEC matrix. The results of the randomization test were nonsignificant (p = .27, CI = .17), with only 42 of the 72 predictions being met. Thus, we concluded that Holland's (1985, 1992) spatial model did not fit our data, although there seemed to be some support for the existence of his RIASEC categories.
Dimensional Representation of Occupational Perceptions Given that Holland's (1992) spatial model did not seem to fit the data, it seemed unlikely that Prediger's (1982) dimensions would be supported. However, Shivy et al.
Auto Mechanic
g
Things
Airplane Pilot
• *
R Cake Decorator Instrumental Musician
Laboratory Assistant , Golf Club
Data
Mathematician
Nursery School Attendant
•Cashier Novelist
restige
•E Door-to-dooj Salesperson
• Certified Public Accountant Bank Teller
Lawyer Minister / Priest / Rabbi Figure 1.
1 X 2 plane of the three-dimensional Time 1 multidimensional scaling solution.
213
OCCUPATIONAL PERCEPTIONS Door-to-doorSalesperson Manager, Golf Club Farm Worker Cashier 2ash
Nursery School Attendant Cake Decorator *
E
Bank Teller
•c Certified Public Accountant
A Minister / Priest / Rabbi Novelist*
Data AirplaneTilot
MTthematician
Instrumental Musician
Idea Laboratory Assistant Occupational Therapist Surgical Technician Figure 2.
1 X 3 plane of the three-dimensional Time 1 multidimensional scaling solution.
(1996) did find some support for the people/things, data/ ideas dimensions in three-dimensional MDS solutions of card sorting data. Thus, to evaluate whether Prediger's model accurately fit the data, we ran a series of multiple linear regressions of the unidimensional scales onto the configural dimensions (Jones & Koehly, 1993). This regression procedure, which is often termed property vector fitting, treats the coordinates from the MDS solution (in this case three vectors of coordinates, because we have a threedimensional scaling solution) as predictor variables, and the vector of the mean ratings of the 18 occupations on a given property (e.g., a specific unidimensional attribute scale, such as prestige) as the criterion variable. Application of standard multiple regression procedures then yields regression weights that may be used to plot the property vectors into the MDS scaling solution. This procedure also yields regression coefficients (i.e., Rh) that index the degree to which each occupation's location on a unidimensional scale (such as prestige) is predicted by that occupation's location in the MDS configuration. In short, the results of these analyses allow us to determine whether hypothesized property vectorsfitinto the occupational perceptions space as defined by the MDS scaling solution. As before, we limit our interpretive discussion to the Time 1 solution. The regression analyses, which are presented in Table 2, indicated that several of our property vectors seemed to be captured in the occupational perception space. In particular,
Table 2 Beta f/3) Weights and R2sfor Property Vectors' Fit Into Rotated Three-Dimensional KYST-2A Scaling Solutions for the CDE Class, at Time 1 and Time 2 Dependent variable
Dim 1
Prestige Women in job Men in job Data Ideas Things People
.088 -.112 .295 .950 -.581 .440 -.091
Prestige Women in job Men in job Data Ideas Things People
.119 -.178 .308 .938 -.615 .236 .084
Dim 2 Time 1 -.320 -.248 .097 .059 -.357 .825 -.598
Dim 3
R2
-.720 -.043 -.214 -.190 -.555 -.203 .171
.63* .07 .14 .93** .75** .86** .40
-.670 -.018 -.159 -.207 -.449 -.145 .221
.59* .10 .13 .92** .76** .81** .46
Time 2 -.359 -.254 .086 -.013 -.423 .858 -.633
Note. TV = 18. CDE = career development and exploration class; Dim = dimension. Dimensional coordinates for the CDE Time 1 solution have been rotated to maximal congruence with dimensional coordinates for the Time 2 solution. *p
