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Sun-Joo Cho

Showing results (1-10 of 55) with videos related to

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Psychometrika|April 24, 2026
AN ITERATIVE GLMM-XGBOOST ALGORITHM WITH GROUP-AWARE CONDITIONAL PERMUTATION IMPORTANCE FOR EXPLAINING MULTILEVEL ITEM RESPONSE DATASun-Joo Cho
Applied Psychological Measurement|June 9, 2018
Obtaining Fixed Effects for Between-Within Designs in Explanatory Longitudinal Item Response Models Using MplusSun-Joo Cho, Youngsuk Suh
Psychometrika|April 10, 2026
MIXED-EFFECTS XGBOOST WITH GROUP-AWARE PERMUTATION IMPORTANCE AND CROSS-VALIDATION FOR MULTILEVEL CROSS-CLASSIFIED CONTINUOUS OUTCOMESSun-Joo Cho, Sophia Mueller
Applied Psychological Measurement|June 9, 2018
A Note on Parameter Estimate Comparability: Across Latent Classes in Mixture IRT ModelingInsu Paek, Sun-Joo Cho
The British Journal of Mathematical and Statistical Psychology|March 31, 2015
Multilevel multidimensional item response model with a multilevel latent covariateSun-Joo Cho, Brian Bottge
Applied 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 Cho
Psychometrika|June 5, 2021
Not all DIF is shaped similarlyPaul De Boeck, Sun-Joo Cho
Educational 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 Preacher
Applied Psychological Measurement|June 9, 2018
A Note on <i>N</i> in Bayesian Information Criterion for Item Response ModelsSun-Joo Cho, Paul De Boeck
Psychometrika|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 Goodwin
Pageof 6

Showing results (1-10 of 55) with videos related to

Sort By:
Pageof 6
Psychometrika|April 24, 2026
AN ITERATIVE GLMM-XGBOOST ALGORITHM WITH GROUP-AWARE CONDITIONAL PERMUTATION IMPORTANCE FOR EXPLAINING MULTILEVEL ITEM RESPONSE DATASun-Joo Cho
Applied Psychological Measurement|June 9, 2018
Obtaining Fixed Effects for Between-Within Designs in Explanatory Longitudinal Item Response Models Using MplusSun-Joo Cho, Youngsuk Suh
Psychometrika|April 10, 2026
MIXED-EFFECTS XGBOOST WITH GROUP-AWARE PERMUTATION IMPORTANCE AND CROSS-VALIDATION FOR MULTILEVEL CROSS-CLASSIFIED CONTINUOUS OUTCOMESSun-Joo Cho, Sophia Mueller
Applied Psychological Measurement|June 9, 2018
A Note on Parameter Estimate Comparability: Across Latent Classes in Mixture IRT ModelingInsu Paek, Sun-Joo Cho
The British Journal of Mathematical and Statistical Psychology|March 31, 2015
Multilevel multidimensional item response model with a multilevel latent covariateSun-Joo Cho, Brian Bottge
Applied 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 Cho
Psychometrika|June 5, 2021
Not all DIF is shaped similarlyPaul De Boeck, Sun-Joo Cho
Educational 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 Preacher
Applied Psychological Measurement|June 9, 2018
A Note on <i>N</i> in Bayesian Information Criterion for Item Response ModelsSun-Joo Cho, Paul De Boeck
Psychometrika|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 Goodwin
Pageof 6