Comparing methods of measuring interest fit: A large prediction study with career choice satisfaction

Abstract

Vocational interest inventories are widely used in both research and practice to help match people to well-fitting work environments. However, because there are many different methods to operationalize interest fit, a debate remains regarding the best ways to do so. To empirically inform this debate, our study compared the predictive power of four widely used interest fit indices (i.e., matching scale scores, profile deviance scores, profile correlations, and polynomial regression scores) for predicting career choice satisfaction. Using a large and diverse U.S. sample (N = 257,320), results indicated that among the three single-term interest fit measures, profile correlations (R² = .04) explained more variance in career choice satisfaction than matching scale scores (R² = .02) and profile deviance scores (R² = .00). By comparison, the full 30-term polynomial regression model explained the most variance in career choice satisfaction (R² = .09); in this case, however, the nonlinear terms that capture fit effects only accounted for about 22% (R² = .02) of the total variance explained by the model. Overall, these results indicate that researchers and practitioners should be cautious of the greater criterion-related validity of polynomial regression models as fit information may not be a substantial contributor to their predictive capacities. In addition, our findings support the use of profile correlations as a predictive, single-term measure of interest fit.