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Factorial invariance of the indecision scale of the career decision scale: a multigroup confirmatory factor analysis.

Results of a multigroup confirmatory factor analysis (N = 686) indicated factorial invariance of a 3-factor model of the Indecision scale of the Career Decision Scale (CDS; Osipow, Carney, Winer, Yanico, & Koschier, 1976). Differential item function was not observed when [DELTA]CFI (comparative fit index) was used for comparison of models, thus indicating strong measurement invariance across gender. Men had significantly greater latent means for all 3 dimensions of career indecision. Given the multidimensional structure, use of the CDS may provide an initial step to help practitioners identify possible factors that are responsible for a client's career indecision. Clients may need assistance for initiating a career search, information to help them identify career possibilities for a chosen major, or detailed information concerning several possible careers that are under consideration.

Keywords: career indecision, measurement invariance, confirmatory factor analysis

The Career Decision Scale (CDS; Osipow, Carney, Winer, Yanico, & Koschier, 1976) provides reliable assessment of career indecision, and it has been recommended for assessment prior to determining appropriate interventions for resolving career indecision. The CDS has utility for yielding one total score of indecision (Osipow, 1994); thus, it has been used to provide a global measure of indecision in cluster analytic research (Niles, Erford, Hunt, & Watts, 1997) and criterion validity research (Guerra & Braungart-Ricker, 1999), and it has been used as an outcome measure following an intervention (Jurgens, 2000; Kelly & Pulver, 2003). However, use of multiple CDS indecision scales may be more informative for understanding the antecedents of career indecision (Vondracek, 1991).

There has been some debate concerning the factor structure of the Indecision scale. A four-factor structure was identified initially (Osipow, Carney, & Barak, 1976), which included lack of confidence with decision making, perception of barriers to a career decision, perception of a conflict that involves two or more alternatives, and personal conflict. This structure was supported in other studies (Chartrand & Robbins, 1990; Schulenberg, Shimizu, Vondracek, & Hostetler, 1988; Shimizu, Vondracek, Schulenberg, & Hostetler, 1988; Vondracek, Hostetler, Schulenberg, & Shimizu, 1990), although issues were raised about its dimensional structure without additional theoretical and clinical knowledge (Laolante, Coallier, Sabourin, & Martin, 1994).

Kelly and Lee (2002) investigated the dimensions of career indecision using exploratory factor analysis. Instruments included the CDS and two additional instruments, the Career Decision-Making Difficulties Questionnaire (Gati, Krausz, & Osipow, 1996) and the Career Factors Inventory (Chartrand, Robbins, Morrill, & Boggs, 1990). The sample included 1st-year students who were undecided about a career. Three interpretable factors that included more than one CDS item were identified: Identity Diffusion, Positive Choice Conflict, and Tentative Decision. A recent confirmatory factor analysis (CFA) supports this structure (Feldt et al., 2010) when the CDS is used alone.

Given this structure, the CDS has the potential to do more than simply serve as a global measure of career indecision or as a possible outcome measure. Rather, it has the potential to identify three facets of career indecision. However, its utility depends on the extent to which assessment is invariant across gender, the generalizability aspect of construct validity (Messick, 1995). The present study builds on the previous analysis (Feldt et al., 2010) to determine the extent to which CDS items are invariant across gender. I hypothesized that the CDS Indecision items are invariant across gender.


A total of 686 undergraduate college students (521 women and 165 men) were recruited from introductory psychology, nursing, and sociology classes. The percentage of European American participants exceeded 95%. Age means for women and men were 19.0 (SD = 1.4) and 19.5 (SD = 1.6) years, respectively (p > .05). Gender and grade level were independent (p > .05).


Career indecision was measured with the 16-item Indecision scale of the CDS (Osipow et al., 1976). Respondents rate each item on a 4-point Likert-type scale ranging from 1 (not at all like me) to 4 (exactly like me). Cronbach's coefficient alpha for the full scale ranges from .82 to .90 (Osipow, 1987). The CDS was completed within the classroom.

Model Specification

The three-factor model (Kelly & Lee, 2002) includes Identity Diffusion (Items 5, 7, 8, 10, 13, and 14), Positive Choice Conflict (Items 4 and 15), and Tentative Decision (Items 12, 16, and 18). Items 11 and 17 were loaded on Identity Diffusion and Positive Choice Conflict, respectively, based on results of previous research (Martin, Sabourin, Laplante, & Coallier, 1991; Schulenberg et al., 1988; Shimizu et al., 1988). Items 1, 2, 3, 6, and 9 were excluded from the analysis based on results of previous CFAs.

AMOS 7.0 software was used for the analysis with maximum likelihood estimation, and the input matrix was the covariance matrix. Departure from multivariate normality was detected.

Statistical Analysis

Guidelines provided by Byrne (2001,2004) and Dimitrov (2010) were used. A baseline model was created for each gender without imposing constraints on parameters. The indicator variable that was fixed to 1.0 for each dimension was based on the variable that had the most similar principal component loading across gender (Finch & French, 2008). Following assessment of fit of the baseline configural model (configural invariance), a series of constrained models was generated in which equality constraints were imposed (in order) upon error covariances, factor loadings (metric invariance), item intercepts (scalar invariance), and factor variances and covariances. Metric invariance and scalar invariance were tested in separate nested models; thus, for comparison of nested models, equality constraints of previous models (e.g., factor loadings) were maintained while additional constraints (e.g., item intercepts) were added to subsequent models. A procedure recommended by Dimitrov (2006) was used to examine gender differences between latent variables following a test of measurement invariance. The male group was used as the reference group, with its latent mean constrained to 0.

Fit Indices

The following fit indices were used: the comparative fit index (CFI; Bender, 1990), the root mean square error of approximation (RMSEA; Steiger, 1990), and the standardized root mean square residual. (SRMR). The choice to include these indices is supported by knowledge that chi-square is susceptible to inflation because of large sample sizes, large degrees of freedom, and nonnormal distributions (Raykov, 1998).

The following cutoffs were used to assess model fit: (a) For good fit, CFI [greater than or equal to] .95, RMSEA [greater than or equal to] .05, and SRMR [less than or equal to] .08, and (b) for acceptable fit, CFI [greater than or equal to] .90 but < .95 and RMSEA > .05 but < .08 (Browne & Cudeck, 1993; Hu & Bender, 1999; Tanaka, 1993). The criterion for comparisons of nested models included [DELTA]CFI < -.01 which is recommended because of sufficient power (Cheung & Rensvold, 2002) to detect differential item function (DIF). In addition, results of a recent simulation indicate that it is less susceptible than the chi-square difference test ([DELTA][x.sup.2]) to Type I errors, especially when the number of factors increases and the number of items per factor decreases (French & Finch, 2011).


Correlated uniquenesses (CUs) for Items 7 and 8 and Items 4 and 15 were included following inspection of modification index values and determination that error covariance was due to item redundancy (Byrne, 2001). Items 7 and 8 reflect concerns about the difficulty of making a career decision, and Items 4 and 15 reflect concerns about too many possibilities for making a final decision. Cronbach's coefficient alpha was .89, .77, and .66, for Identity Diffusion, Positive Choice Conflict, and Tentative Decision, respectively.

The baseline models for women and the combined group had good fit based on CFI, RMSEA, and SRM_R values, whereas the baseline model for men had acceptable fit (see Table 1). Factor loadings ranged from .68 to .88, .58 to .83, and .49 to .71 for Identity Diffusion, Positive Choice Conflict, and Tentative Decision, respectively. Results indicated invariance of CUs, factor loadings, item intercepts, and factor variances and covariances. Results of structured means analysis indicated significantly greater latent variable means for men (all ps < .01). Estimates (with 95% confidence interval) for Identity Diffusion, Positive Choice Conflict, and Tentative Decision were -.21 [-.33, -.11], -.22 [-.34, -.101, and -.15 [-.27, -.06], respectively.
Models and Fit Statistics for the Three-Factor
Structure of the Indecision Scale of the Career
Decision Scale

Model                   [x.sup.2]   df  CFI  RMSEA  90% CI  SRMR

Baseline model

Women                      141.19   60  .96    .05   [-04,   .03

Men                        117.60   60  .94    .08   [.06,   .05

Combined                   259.03  120  .97    .04   [-03,   .03

Error covariances          260.04  122  .97    .04   [.03,   .03

Factor loadings            272.69  130  .97    .04   [.03,   .03

Item intercepts            320.78  140  .96    .04   [.04,   .03

Item 7 and 11              291.53  138  .96    .04   [.03,   .03
intercepts (estimated)                                .05]

Factor                     323.52  146  .96    .04   [.04,   .04
variances/covariances                                 .05]

Note. CFI = comparative fit index; RMSEA = root mean
square error of approximation; CI = confidence interval;
SRMR = standardized root mean square residual.

Results indicated two noninvariant items according to the Ax2 test (p < .01). Two items for Identity Diffusion with noninvariant intercepts included Items 7 and 11. Estimated intercepts for Item 7 for women and men were 1.56 and 1.74, respectively, and the estimated intercepts for Item 11 were 2.13 and 1.84 for women and men, respectively. The estimated differences between gender means for Identity Diffusion were similar whether these items were included (-.21) or excluded (-.25; both ps = .002).


The purpose of the present study was to examine measurement and structural invariance of the CDS Indecision scale. Given results of tests for scalar invariance, which differ across fit indices, [DELTA]CFI provides a more accurate test given its lower rate of Type I error when models are not perfectly specified (French & Finch, 2011). Rather than omit the items because of the DIF observed in this study based on the [DELTA][x.sup.2] test, the items should be retained based on the results of the [DELTA]CFI. Results indicated invariance of CUs, factor loadings, item intercepts, and factor variances and covariances. Results indicate support for a three-factor model that exhibits strong measurement invariance. The present study provides a more thorough understanding of the three-factor structure of the CDS Indecision scale.

The observed gender differences in this study contrast with no observed difference in a normative sample of college students (Osipow, 1987, p. 21) and additional samples (Guerra & Brawigart-Rieker, 1999; Meyer & Winer, 1993). Although gender means are rarely reported in previous research, one study reported that men were more undecided for a group of college students who failed to report a career choice or alternative possibilities (Slaney, 1980), and Osipow and Winer (1996) cited an unpublished doctoral dissertation that revealed gender differences in career indecision. The difference observed in the present study may be attributed to the relatively large percentage of students in pre-professional programs (i.e., nursing and education). A previous study reported less career indecision in students of preprofessional programs compared with students in social sciences (Guerra & Braungart-Rieker, 1999). Given that the gender difference may be limited to this sample, additional research with cross-discipline representation is warranted.

Use of the CDS Indecision scale facets will provide some understanding of possible factors underlying indecision before considering an appropriate intervention and/or additional assessment. Rather than simply serving as a global measure of career indecision, the CDS could have utility for more refined assessment of one's indecision. Practitioners would benefit by identifying individuals who are undecided for different reasons. Students with high scores on Identity Diffusion are likely to be anxious about a career decision and therefore are likely to avoid a career search. Such students require emotional support and information regarding plausible career possibilities, in addition to interventions designed to help them initiate career search activities. Students with high scores on Tentative Decision have difficulty determining career possibilities based on their major. They need support in the form of knowledge of career possibilities to help them implement their major into a career choice. Students with high scores on Positive Choice Conflict struggle with deciding because several alternatives appeal to them and they hold beliefs that they could experience success with each. Such students require additional information about each possible career path to refine the search process until a final decision is made.

Implications of results are to demonstrate the utility of the CDS Indecision scale for providing assessment beyond a global indecision score with invariance across gender. For the practitioner, a more thorough understanding of indecision could be supplemented by use of additional measures, such as the Career Factors Inventory and the Career Decision-Making Difficulties Questionnaire. The choice to include additional instruments would be based on judgment as to which dimensions of career indecision are deemed most important.

Received 06/02/12

Revised 09/17/12

Accepted 09/17/12

DOI: 10.10042161-0045,2013.00053.x

[c] 2013 by the National Career Development Association. All rights reserved.


Bender, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246. doi:10.1037/0033-2909.107.2.238

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Toting structural equation models (pp. 136-162). Newbury Park, CA: Sage.

Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah, NJ: Erlbaum.

Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural Equation Modeling, 11, 272-300. doi:10.1207/s15328007sem1102_8

Chartrand, J. M., & Robbins, S. B. (1990). Using multidimensional career decision instruments to assess career decidedness and implementation. The Career Development Quarterly, 39, 166-177. doi:10.1002/j.2161-0045.1990.tb00837.x

Chartrand, J. M., Robbins, S. B., Morrill, W. H., & Boggs, K. (1990). Development and validation of the Career Factors Inventory. Journal of Counseling Psychology, 37, 491-501. doi:10.1037/0022-0167.37.4.491

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255. doi:10.1207/S15328007SEM0902_5

Dimitrov, D. M. (2006). Comparing groups on latent variables: A structural equation modeling approach. WORK: A Journal of Prevention, Assessment & Rehabilitation, 26, 429-436. Retrieved from

Dimitrov, D. M. (2010). Testing for factorial invariance in the context of construct validation. Measurement and Evaluation in Counseling and Development, 43, 121-149. doi:10.1177/0748175610373459

Feldt, R. C., Ferry, A., Bullock, M., Camarotti-Carvalho, A., Collingwood, M., Eilers, S., ... Woelfel, C. (2010). Factorial structure of the Career Decision Scale: Incremental validity of the five-factor domains. Measurement and Evaluation in Counseling and Development, 42, 235-245. doi:10.1177/0748175609354575

Finch, W. H., & French, B. F. (2008). Using exploratory factor analysis for locating invariant referents in factor invariance studies. Journal of Modern Applied Statistical Methods, 7, 223-233. Retrieved from

French, B. F., & Finch, W. (2011). Model misspecification and invariance testing using confirmatory factor analytic procedures. Journal of Experimental Education, 79, 404-428. doi:10.1080/00220973.2010.517811

Gati, I., Krausz, M., & Osipow, S. H. (1996). A taxonomy of difficulties in career decision making. Journal of Counseling Psychology, 43,510-526. doi:10.107/0022-0167.43.4.510

Guerra, A. L., & Braungart-Rieker, J. M. (1999). Predicting career indecision in college students: The roles of identity formation and parental relationship factors. The Career Development Quarterly, 47, 255-266. doi:10.1002/j.2161-0045.1999.tb00735.x

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. doi:10.1080/10705519909540118

Jurgens, J. C. (2000). The undecided student: Effects of combining levels of treatment parameters on career certainty, career indecision, and client satisfaction. The Career Development Quarterly, 48, 237-250. doi:10.1002/j.2161 -0045.2000.tb00289.x

Kelly, K. R., & Lee, W. (2002). Mapping the domain of career decision problems. Journal of Vocational Behavior, 61, 302-326. doi:10.1006/jvbe.2001.1858

Kelly, K. R., & Pulver, C. A. (2003). Refining measurement of career indecision types: A validity study. Journal of Counseling & Development, 81, 445-454. doi:10.1002/j.1556-6678.2003.tb00271.x

Laplante, B., Coallier, J., Sabourin, S., & Martin, F. (1994). Dimensionality of the Career Decision Scale: Methodological, cross-cultural, and clinical issues. Journal of Career Assessment, 2, 19-28. doi:10.1177/106907279400200103

Martin, F., Sabourin, S., Laplante, B., & Coallier, J. C. (1991). Diffusion, support, approach, and external barriers as distinct theoretical dimensions of the Career Decision Scale: Disconfirming evidence? Journal of Vocational Behavior, 38, 187-197. doi:10.1016/0001-8791(91)90026-I

Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American Psychologist, 50, 741-749. doi:10.1037/0003-066X.50.9.741

Meyer, B. W., & Winer, J. L. (1993). The Career Decision Scale and neuroticism. Journal of Career Assessment, 1, 171-180. doi:10.1177/106907279300100206

Niles, S. G., Erford, B. T., Hunt, B., & Watts, R. H. (1997). Decision-making styles and career development in college students. Journal of college Student Development, 38, 479-488.

Osipow, S. H. (1987). Manual for Career Decision Scale. Odessa, FL: Psychological Assessment Resources.

Osipow, S. H. (1994). The Career Decision Scale: How good does it have to be? Journal of Career Assessment, 2, 15-18. doi:10.1177/106907279400200102

Osipow, S. H., Carney, C. G., & Barak, A. (1976). A scale of educational--vocational undecidedness: A typological approach. Journal of Vocational Behavior, 9, 233-243. doi:10.1016/0001-8791(76)90081-6

Osipow, S. H., Carney, C. G., Winer, J., Yanico, B., & Koschier, M. (1976). Career Decision Scale (3rd rev. ed.). Lutz, FL: Psychological Assessment Resources.

Osipow, S. H., & Winer, J. L. (1996). The use of the Career Decision Scale in career assessment. Journal of Career Assessment, 4,117-130. doi:10.1177/106907279600400201

Raykov, T. (1998). On the use of confirmatory factor analysis in personality research. Personality and Individual Differences, 24, 291-293. doi:10.1016/S0191-8869(97)00159-1

Schulenberg, J. E., Shimizu, K., Vondracek, F. W., & Hostetler, M. (1988). Factorial invariance of career indecision dimensions across junior high and high school males and females. Journal of Vocational Behavior, 33,63-81. doi:10.1016/0001-8791(88)90034-6

Shimizu, K., Vondracek, F. W., Schulenberg, J. E., & Hostetler, M. (1988). The factor structure of the Career Decision Scale: Similarities across selected studies. Journal of Vocational Behavior, 32, 213-225. doi:10.1016/0001-8791(88)90015-2

Slaney, R. B. (1980). Expressed vocational choice and vocational indecision. Journal of Counseling Psychology, 27, 122-129. doi:10.1037/0022-0167.27.2.122

Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173-180. doi:10.1207/s15327906mbr2502_4

Tanaka, J. S. (1993). Multifaceted conceptions of fit in structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 10-40). Newbury Park, CA: Sage.

Vondracek, F. W. (1991). Osipow on the Career Decision Scale: Some comments. Journal of Counseling & Development, 70, 327. doi:10.1002/j.1556-6676.1991.tb01606.x

Vondracek, F. W., Hostetler, M., Schulenberg, J. E., & Shimizu, K. (1990). Dimensions of career indecision. Journal of Counseling Psychology, 37,98-106. doi:10.1037/0022-0167.37.1.98

Ronald C. Feldt, Department of Psychology, Social Work., Sociology, and International Studies, Mount Mercy University. Correspondence concerning this article should be addressed to Ronald C. Feldt, Department of Psychology, Social Work, Sociology, and International Studies, Mount Mercy University, 1330 Elmhurst Drive NE, Cedar Rapids, IA 52402 (e-mail:
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Title Annotation:Brief Report
Author:Feldt, Ronald C.
Publication:Career Development Quarterly
Geographic Code:1USA
Date:Sep 1, 2013
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