Journal of Vocational Behavior 9, 43-52 (1976)

An Investigation of Intelligence, Self-Concept, Socioeconomic Status, Race, and Sex as

Predictors of Career Maturity

WILLIAM LAWRENCE A & T State University

and

DUANE BROWN University of North Carolina

A multiple regression procedure was used to develop a further understanding of the relationship of self-concept (SC), intelhgence (IQ), socioeconomic status (SES), and race, and sex to career maturity as measured by the Career Maturity Inventory (CMI). Subjects included in the study were 266 twelfth-graders 146 black males (BM), 50 black females (BF), 92 white males (WM), and 78 white females (WF)]. The results suggested that when predicting career maturity as measured by the CMI, a separate equation utilizing different predictors, depending on race and sex of subjects should be considered. Results further indicated that socio- economic status and self-concept seem to have a differential effect upon career maturity.

In 1953, Super introduced the concept of career maturity to denote a theoretical point on the continuum of career development. Super charac- terized the ends of the continuum as exploration and decline and hypothesized that the individual moves through a series of developmental stages. Further, Super postulated that certain psychological variables, primarily self-concept and intelligence as well as sociological factors, impinge upon this developmental process. Much research (O’Hara & Tiedeman, 1959; Super & Overstreet, 1960; Crites, 1965; Gibbons & Lohnes, 1969) has been conducted with the intent of ascertaining those variables which do in fact influence career maturity.

Since Super’s (1953) original theory gave centrality to self-concept, a number of studies have been conducted which focused upon this variable

Wiiam Lawrence is Associate Professor of Education at A 8s T State University, Greensboro, NC and Duane Brown is Associate Professor of Education at UNC, Chapel Hill, NC. Requests for reprints should be sent to William Lawrence, A & T State University, School of Education, Greensboro, NC 27411.

43

Copyright @ 1976 by Academic Press, Inc. All rights of reoraduction in anv form reserved.

44 LAWRENCE AND BROWN

(Oppenheimer, 1966; Soares & Soares, 1966; Korman, 1966; Healy, 1968; Greenhaus, 1971; Lefebure, 1971). Likewise, the importance of intelligence in career development and in achieving career maturity has been scrutinized by researchers (Holden, 1961; Davis, Hagan & Strouf, 1962; Chansky, 1965). Generally, research has supported the idea that both self-concept and intelligence contribute to generalized measures of career maturity.

It should be noted that the use of traditional scholastic aptitude measures with minority groups has been challenged (See Cleary et al.,, 197.5; Jackson, 1975; Bernal, 1975). However, because the career maturity literature has often looked at this variable and because the importance of it is still in question, it was deemed advantageous to use a measure of intelligence in this particular study.

Super (1955) also emphasized that cultural factors, such as race and socioeconomic status, could have potential impact upon career maturity. Roe and Seigleman (1963); Chopra (1967) and Grebow (1973) have all attested to the importance of socioeconomic variables. Race as a factor in career development has been studied by Ansell and Hansen (1971). They found that lower-class black and white students did not vary in career maturity as measured by the Readiness for Vocational Planning Scale. Earlier Pallone, Richard, and Hurley (1970) had looked at the factors influencing career preference of black youth. They concluded that persons holding the preferred occupation and parents exercised principal influences over their occupational preferences. Nonetheless, the influence of race and socioeconomic status on career maturity is generally not well understood.

Finally, while Super’s original statement was intended as a global theory, much of the research which has been conducted has used male subjects as the research sample (Super, 1960; Healy, 1968). Even through Shappell, Hall, and Tarrier (197 1) did find that sex is not a factor in differentiating perceptions of careers, there is a need for further illumination of the relationship between sex and career maturity. The primary purpose of the study was to use a multiple regression procedure to develop a further understanding of the relationship of self-concept (SC), intelligence (IQ), socioeconomic status (SES), race, and sex to career maturity as measured by the Cizreer M&&y Inventory (Crites, 1973).

METHOD

Subjects

The subjects included in the study were 266 twelfth graders (46 black males (BM), 50 black females (FM), 92 white males (WM), and 78 white females (WF) ranging in age from 16 to 19 yr. The students attended an

PREDICTORS OF CAREER MATURITY 45

integrated senior high school (65% white; 34% black, and 1% other) in the United States.

Instrumentation

The Career Maturity Inventory (CMI) (Crites 1973) is composed of two subscales, the Attitude Scale (Att) and the Competence Test. The Competence Test is subdivided into five subparts: Self Appraisal (Pl), Occupational Information (P2), Goal Selection (P3), Planning (P4), and Problem Solving (P5). The Attitude Scale elicits the feelings, the subjective relations and the dispositions that the individual has toward making a career choice and entering the world of work. Crites (1973) reports that the KR20 coefficients range from .72 to .90 and there is some indication that each of the five subtests measure similar variables. However, for purposes of this research each subtest was utilized as a separate criterion variable.

The Tennessee Self Concept Scale (TSCS) (Fitts, 1965) was selected both because of its high reliability (.92 for the total scale) and because of its widespread useage.

.The Otis-Lennon Mental Ability Test is also a widely used and highly acceptable instrument. Alternate forms of the Otis-Lennon show reliability coefficients above .90 and the validity data is also impressive (Buros, 1972).

Bocedure

The subjects were administered two standardized instruments, the Cureer Maturity Inventory (Attitude Scale and Competence Test) and the Tennessee Self Concept Scale (TSCS) from which their career maturity and their self-concept were inferred. The subjects were also administered a short questionnaire as a means of obtaining certain background data such as race, age, sex, and parents’ or guardians’ occupation. Use was made of the Intelligence scores from the Otis-Lennon Mental Ability Test administered by the school system to eleventh graders 10 months prior to the collection of the other data.

In order to determine socioeconomic status, the occupation of the students’ father or guardian was classified using the Duncan Socioeconomic Index Scale (1961) and information from the questionnaire. A check of 50 randomly selected student records Indicated that the information on the questionnaire concerning parents and students agreed with Information on the student’s school records more than 96% of the time.

The scores obtained using the procedure and instrument described were analyzed and interpreted through the use of Multiple Regression and Analysis of Regression (ANOR) (McNemar, 1957; Bottenberg & Ward, 1963; Kerlinger, 1964).

46 LAWRENCE AND BROWN

RESULTS

The predictor means and standard deviations are presented in Table 1 for each subgroup (based on sex and race) and the total group. The whites’ IQ scores are significantly higher than the blacks’ IQ scores (t = 7.27, p<.Ol). A similar difference is also found when the group is separated on the basis of race and sex. The selfconcept scores are not found to be significantly different when compared on the basis of race alone (t = 1.12, p>.OS). However, when the self-concept (SC) of the black males (BM) is compared with the self-concept of the black females (BF), white males (WM), and white females (WF), significant differences are found (t = 2.77, p<.Ol) for BF; f = 2.03, 6.05 for WM; t = 2.01, 6.05 for WF). Socioeconomic status was found to be significantly higher for whites than blacks, regardless of sex (t = 3.94, p<.Ol).

The criterion means and standard deviations are presented in Table 2 for each subgroup and the total group. The BM scored significantly lower than the WM on each of the criterion variables (t = - 5.86, p<.Ol for Att; t = - 3.86, p<.Ol for Pl; t = 5.28, p<.Ol for P2; t = -4.15, p<.Ol for P3; C = -5.09, p<.Ol for P4; t = -4.03, p<.Ol for P5. Likewise, the BF scored significantly lower than the WF on each of the criterion variables (t = -6.66, p<.Ol for Att; t z-5.10, p<.Ol for Pl; t = -7.33, p<.Ol for P2; t = -6.57, p<.Ol for P3; r =- 11.72, p<.Ol for P4; t = -4.72, p<.Ol for P5). No significant differences were found when the BM were compared with the BF. Neither were there significant differences when the WM were compared with the WF.

A summary of the multiple regression analyses for the total group and each of the four subgroups with Att as the criterion can be found in Table 3. The ANOR revealed that the best set of predictors for the total group with Att as the criterion was IQ, SC, and race (R = .62). The only predictor which contributed significantly to the prediction of Att for the subgroups BM and WF was IQ (R = .32) and (R = .47), respectively. For the subgroup BF, IQ and

TABLE 1 Means and Standard Deviations of Predictors

IQ

Group N M SD

BM 46 87.50 9.83 BF 50 85.52 11.44 WM 92 102.80 12.55 WF 78 102.26 13.39

Total 266 96.75 14.39

SC SES

M SD M SD

318.89 31.56 20.35 11.68 332.14 32.46 24.94 20.77 331.00 33.80 43.23 23.04 329.52 33.12 41.41 24.40

328.69 33.12 35.30 23.45

TABL

E 2

Mea

ns a

nd S

tand

ard

Dev

iatio

ns

of C

riter

ia

ATT

Pl

P2

P3

P4

P5

Gro

up

N

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

BM

46

30.6

1 5.

72

10.8

5 3.

85

12.1

5 4.

56

9.48

3.

69

9.14

4.

20

8.06

3.

13

BF

50

31.8

2 5.

50

12.0

0 3.

67

13.4

4 3.

84

10.4

0 2.

93

10.1

4 4.

48

9.18

3.

75

WM

92

36

.48

5.48

13

.32

3.44

16

.09

3.91

12

.43

4.06

13

.71

4.39

10

.87

3.94

W

F 78

37

.02

4.81

14

.88

2.71

17

.36

2.31

14

.08

3.19

15

.27

3.01

12

.50

3.17

Tota

l 26

6 34

.75

5.94

13

.10

3.64

15

.28

4.11

12

.02

3.91

12

.80

4.58

10

.54

3.97

48 LAWRENCE AND BROWN

TABLE 3 Summary of Multiple Regression with Attitude (Att) as Criterion

Group Variable Multiple R RZ F Value

Total Group N= 266

Black Males N=46 Black Females N=50 White Males N=92 White Females N=78

IQ 0.56099 SC 0.59696 Race 0.62489 Sex 0.62856 SES 0.63030 IQ 0.32102 SC 0.41119 SES 0.42744 IQ 0.41712 SC 0.52091 SES 0.53026 SC 0.49423 IQ 0.57464 SES 0.57574 IQ 0.47145 SES 0.47278 SC 0.47418

0.31471 121.24** 0.35637 17.02** 0.39048 14.67** 0.39509 1.99 0.39728 0.94 0.10306 5.06* 0.16907 3.42 0.18270 0.70 0.17399 10.11** 0.27135 6.28* 0.28117 0.63 0.24426 29.08** 0.33022 11.42** 0.33147 0.16 0.22226 21.72** 0.22352 0.129 0.22484 0.126

** Indicates F is significant at the .Ol level. * Indicates F is significant at the .05 level.

SC made up the best set of predictors (R = .52), while the best set of predictors for the subgroup WM was SC and IQ (R = .57).

An interpretation of the multiple regression analyses for the total group and each of the four subgroups with Pl as the criterion was made from a summary table similar to Table 3. The ANOR revealed the best set of predictors for the total group and three of the four subgroups with Pl as the criterion. The best set of predictors for the total group was IQ, sex, SC, and race (R = .53). The only predictor which contributed significantly to the subgroups BF and WF was IQ alone (R = .30 and R = .49), respectively. The best set of predictors for the subgroup WM was SC and IQ (R = .49). Interestingly, none of the predictor variables added significantly to the prediction of Pl for BM.

An interpretation of the multiple regression analyses for the total group and each of the four subgroups with P2 as the criterion was made from a summary table similar to Table 3. The ANOR revealed the best set of predictors for the total group and each subgroup with P2 as the criterion. The best set of predictors for the total group was IQ, race, sex and SC (R = .62.). The best set of predictors for the subgroup BM was IQ and SES (R = .54). For the subgroups BF and WF, again IQ alone (R = .49) and (R = .43), respectively, was the only predictor, whereas IQ and SC (R = .52) ad&d significantly to the prediction of P2 for WM.

PREDICTORS OF CAREER MATURITY 49

An interpretation of the multiple regression analyses for the total group and each of the four subgroups with P3 as the criterion was made from a summary table similar to Table 3. The ANOR revealed the best set of predictors for the total group and the four subgroups with P3 as the criterion. The best set of predictors for the total groups was IQ, sex, SC, and race (R = .62). Again the only predictor variable which contributed significantly for the female subgroups was IQ (BF, R = .39; WF, R = S5). The best set of predictors was IQ and SC (R = .57) for WM. Finally none of the variables added significantly to the predictability of P3 for BM (R = .24).

An interpretation of the multiple regression analyses for the total group and each of the four subgroups with P4 as the criterion was made from a summary table similar to Table 3. The ANOR revealed the best set of predictors for the total group and each subgroup with P4 as the criterion. The best set of predictors for the total group was IQ, race, and sex (R = -64). When the subgroups were examined only IQ contributed significantly to the predicability of P4 with multiple Rs ranging from .34 for BF to .54 for WM.

An interpretation of the multiple regression analyses for the total group and each of the four subgroups with P5 as the criterion was made from a summary table similar to Table 3. The ANOR revealed that the best set of predictors for the total group with P5 as the criterion was IQ and sex (R = .56). As was the case with P4, only IQ contributed significantly to the predictability of P5.

DISCUSSION AND IMPLICATIONS

In light of Super’s hypothesis (1953), vocational choice is simply an extension of one’s self-concept or perhaps more precisely, the implementation of one’s perception of self in.a real life situation. It was expected that by knowing the level of self-concept for a group of twelfth graders, prediction of their career maturity could be improved regardless of the race or sex of the subjects. However, the results of this study indicated that self-concept appears to have a different impact on career maturity for twelfth graders depending upon the race and sex of the subjects. The results further indicated that self-concept was a significant predictor for only certain aspects of career maturity as measured by the CMI. It appears that Super’s selfconcept theory has more validity when referring to white males than when referring to females or to blacks, in general, but more data need to be collected. If this finding does in fact hold up in subsequent research, it means that career counselors cannot depend upon self-concept as an indicator of career maturity in groups other than white males.

Super and Overstreet (1960) and Crites (1965) reported from their studies that vocational maturity was associated with living in an intellectually

50 LAWRENCE AND BROWN

and culturally stimulating environment. Others (Hyman, 1956; Sewell, Haller & Straus, 1957; Pavalko, 1965) have also related scioeconomic status to various aspects of career development. In a more recent study, Shappell et al; (1971) found that perceptions of the world of work were differentiated by socioeconomic status. On the basis of the foregoing, it was expected that by knowing the scioeconomic status of twelfthgraders, the prediction of their career maturity could be improved. However, this was not the case, for socioeconomic status did not significantly improve the prediction of career maturity for the total group nor three of the four subgroups on the six dependent variables. It did improve the prediction of scores on one criterion (occupational information) for the black males. Since, socioeconomic status failed to improve prediction of career maturity as expected, it is suggested that instead of looking at the counselee’s socioeconomic status, counselors should pay more attention to persons in the environment who may serve as role models and/or information providers. This suggestion is supported by the earlier findings of Pallone et al. (1970).

Some results of the study did seem to support the findings of other researchers. Whitty and Lehman. 1931; Wrenn, 1935; Holden, 1961; Davis et al., 1962; Crites, 1969; and Super and Bohn, 1970 concluded from their research that general aptitude or intelligence was related to several aspects of career maturity. Likewise, results of this study indicated that intelligence was significantly correlated with career maturity as measured by the CMI. Nonetheless, it is important to keep in mind that some aspects of career maturity are more highly related to intelligence than other aspects. For example, planning and problem solving are more highly correlated with intelligence than self-appraisal, which would probably be expected. More important, users of the CM1 and interpreters of these data should be aware that the results of this study indicated that the CM1 is highly correlated with traditional measures of intelligence (Crites, 1973). Therefore, when using the CM1 as a measure of career maturity, one should not be surprised to find that the less career mature may be less intellectually bright.

Finally, there is some support for the idea that factors which have traditionally been viewed as facilitators and/or depressors of the career development process may have different influences on different subgroups of twelfthgraders. While this finding has definite implications for future theory building, it also indicates that practicing counselors may need to revise their present career development practices in terms of the experiences of the particular subgroup they are dealing with.

CONCLUSION

It was concluded from this study that when predicting career maturity as measured by the CMI, a separate equation utilizing different predictors,

PREDICTORS OF CAREER MATURITY 51

depending on race and sex of subjects, should be considered. Further conclusions from this study were that socioeconomic status and self-concept appear to have a lesser effect upon career maturity than intelligence, race, or sex. However, this finding should be judged as tentative until further research can be done with a larger sample. Finally, career maturity as measured by the CM1 is highly correlated with intelligence test scores.

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Received: September 8, 1975.