Applying Bayesian Modeling and Receiver Operating Characteristic Methodologies for Test Utility Analysis

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This study integrated Bayesian hierarchical modeling and receiver operating characteristic analysis (BROCA) to evaluate how interest strength (IS) and interest differentiation (ID) predicted low–socioeconomic status (SES) youth’s interest-major congruence (IMC). Using large-scale Kuder Career Search online-assessment data, this study fit three models, the one-level BROCA, the two-level BROCA, and the ordinal Probit BROCA, to examine the moderating effects of gender and race/ethnicity. Both IS and ID displayed race/ethnicity differences in predicting low-SES females’ IMC. Gender difference was found only on IS in predicting low-SES youth’s IMC. Results suggested that low-SES White males and low-SES minority females may need help the most to develop stronger career interests and to differentiate their interests. This study illustrated that BROCA can be a powerful tool for test evaluation and utility analysis in the field because of its capacity of analyzing continuous, nominal, and ordinal data; its graphical nature of result presentation; multiple statistical test options; and its little requirement of Level 2 sample sizes.