Factorial Invariance of the Indecision Scale of the Career Decision Scale: A Multigroup Confirmatory Factor Analysis

Abstract

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 ΔCFI (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.

Career Indecision Profile-65 Scores: Test–Retest Reliability and Measurement Equivalence in College and Noncollege Samples

“…The noncollege sample was significantly older, and a larger percentage of women comprised the college sample. Research (Feldt, 2013; Schulenberg, Shimizu, Vondracek, & Hostetler, 1988) has revealed the factor-structure of other career-indecision measures to be gender invariant, so we think it is unlikely the differences in our college and noncollege samples are solely the result of the samples’ gender composition, but future research should seek to narrow these demographic differences to more definitively conclude that the differences observed can be attributed to the variable (college vs. noncollege) of real interest. It would also be useful to determine whether there is measurement invariance across racial/ethnic groups within college and noncollege young-adult samples, as this has not yet been investigated.…”

Analysis, Design, Development, Implementation, and Evaluation of a Serious Game Designed to Inform Users on Environmental Issues

“…This statistical tool determined if the model was of good fit, that is, if the items do not overlap and uniquely contributes to its assigned criteria. In a statistical sense, a model was of good fit if it satisfied the following: Comparative-Fit Index >= 0.90, Adjusted goodness-of-fit index (AGFI) >= 0.90, and Root mean square error of approximation (RMSEA) < 0.07 (Hair et al, 2010;Feldt, 2013). The final instrument is shown in Table 2.…”