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Nalborczyk, Ladislas; Batailler, Cédric; Lœvenbruck, Hélène; Vilain, Anne; Bürkner, Paul-Christian – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. This tutorial introduces Bayesian multilevel modeling for the specific analysis of speech data, using the brms package developed in R. Method: In this tutorial, we provide a practical…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Gender Differences, Vowels
Schmidt, Susanne; Zlatkin-Troitschanskaia, Olga; Fox, Jean-Paul – Journal of Educational Measurement, 2016
Longitudinal research in higher education faces several challenges. Appropriate methods of analyzing competence growth of students are needed to deal with those challenges and thereby obtain valid results. In this article, a pretest-posttest-posttest multivariate multilevel IRT model for repeated measures is introduced which is designed to address…
Descriptors: Foreign Countries, Pretests Posttests, Hierarchical Linear Modeling, Item Response Theory
Wang, Qiu; Diemer, Matthew A.; Maier, Kimberly S. – Educational and Psychological Measurement, 2013
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…
Descriptors: Bayesian Statistics, Socioeconomic Status, Student Interests, Gender Differences