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Psychometrika|July 31, 2025
Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response ModelsSun-Joo Cho, Goodwin Amanda, Jorge Salas, et al.Applied Psychological Measurement|June 9, 2018
Obtaining Fixed Effects for Between-Within Designs in Explanatory Longitudinal Item Response Models Using MplusSun-Joo Cho, Youngsuk SuhApplied Psychological Measurement|June 9, 2018
A Note on Parameter Estimate Comparability: Across Latent Classes in Mixture IRT ModelingInsu Paek, Sun-Joo ChoThe British Journal of Mathematical and Statistical Psychology|March 31, 2015
Multilevel multidimensional item response model with a multilevel latent covariateSun-Joo Cho, Brian BottgeApplied Psychological Measurement|November 29, 2023
Using Auxiliary Item Information in the Item Parameter Estimation of a Graded Response Model for a Small to Medium Sample Size: Empirical Versus Hierarchical Bayes EstimationMatthew Naveiras, Sun-Joo ChoEducational and Psychological Measurement|May 26, 2018
Measurement Error Correction Formula for Cluster-Level Group Differences in Cluster Randomized and Observational StudiesSun-Joo Cho, Kristopher J PreacherApplied Psychological Measurement|June 9, 2018
A Note on <i>N</i> in Bayesian Information Criterion for Item Response ModelsSun-Joo Cho, Paul De BoeckPsychometrika|April 3, 2016
Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word LearningSun-Joo Cho, Amanda P GoodwinThe British Journal of Mathematical and Statistical Psychology|April 9, 2024
The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested modelSun-Joo Cho, Hao Wu, Matthew NaveirasPageof 7