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

Autor/inn/en	Wang, Qiu; Diemer, Matthew A.; Maier, Kimberly S.
Titel	Applying Bayesian Modeling and Receiver Operating Characteristic Methodologies for Test Utility Analysis
Quelle	In: Educational and Psychological Measurement, 73 (2013) 2, S.275-292 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164412455027
Schlagwörter	Bayesian Statistics; Socioeconomic Status; Student Interests; Gender Differences; Racial Differences; Vocational Interests; Statistical Inference; Interest Inventories; Regression (Statistics); Statistical Analysis; High School Students; Majors (Students); Hierarchical Linear Modeling; Disadvantaged Youth; Poverty + Suchen Sie Ihr Suchwort? Socio-economic status; Sozioökonomischer Status; Studieninteresse; Geschlechterkonflikt; Rassenunterschied; Berufsinteresse; Inferential statistics; Schließende Statistik; Interest profile; Interessenprofil; Regression; Regressionsanalyse; Statistische Analyse; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Benachteiligter Jugendlicher; Armut
Abstract	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. (Contains 2 tables and 2 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10