Interest Fit & Career Choice Satisfaction: A Large Study Comparing Advanced Methods of Measuring Fit



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Despite the widespread use of vocational interest inventories to assess person-environment fit, there is yet to be a consensus on the most predictive way of operationalizing fit. My study aimed to provide clarity to this discussion by comparing the predictive power of four advanced interest fit measures (i.e., matching scale scores, profile deviance scores, profile correlations, and polynomial regression) using career choice satisfaction as the criterion. I used three analytical approaches (i.e., R2 values reported in linear regression models, the dominance analysis, and the k-fold cross-validation procedure) to evaluate the fit measures in a large and diverse U.S. sample (N = 260,036). Results suggested that the full 30-term polynomial regression model accounted for the most variance in career choice satisfaction (R2 = 0.09), followed by profile correlations (R2 = 0.04), matching scale scores (R2 = 0.02), and profile deviance scores (R2 = 0.00). Polynomial regression also produced models with the least amount of prediction errors across resampled datasets. Overall, these results support the use of both polynomial regression and profile correlations to assess fit. While polynomial regression explained more variance in career choice satisfaction, profile correlations are a simpler and more interpretable method of measuring interest fit. I also discuss theoretical implications and provide recommendations to practitioners using interest inventories to assess fit.



Career choice satisfaction, Interest fit, Predictive validity, Matching scale score, Profile deviance, Profile correlation, Polynomial regression