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Mangino, Anthony A.; Finch, W. Holmes – Educational and Psychological Measurement, 2021
Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms…
Descriptors: Prediction, Hierarchical Linear Modeling, Classification, Bayesian Statistics
Nagy, Gabriel; Ulitzsch, Esther – Educational and Psychological Measurement, 2022
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Predictor Variables, Classification
Park, Jungkyu; Yu, Hsiu-Ting – Educational and Psychological Measurement, 2016
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…
Descriptors: Hierarchical Linear Modeling, Nonparametric Statistics, Data Analysis, Simulation