Incorporating Mobility in Growth Modeling for Multilevel and Longitudinal Item Response Data

Multivariate Behavioral Research
In-Hee Choi, Mark Wilson

Abstract

Multilevel data often cannot be represented by the strict form of hierarchy typically assumed in multilevel modeling. A common example is the case in which subjects change their group membership in longitudinal studies (e.g., students transfer schools; employees transition between different departments). In this study, cross-classified and multiple membership models for multilevel and longitudinal item response data (CCMM-MLIRD) are developed to incorporate such mobility, focusing on students' school change in large-scale longitudinal studies. Furthermore, we investigate the effect of incorrectly modeling school membership in the analysis of multilevel and longitudinal item response data. Two types of school mobility are described, and corresponding models are specified. Results of the simulation studies suggested that appropriate modeling of the two types of school mobility using the CCMM-MLIRD yielded good recovery of the parameters and improvement over models that did not incorporate mobility properly. In addition, the consequences of incorrectly modeling the school effects on the variance estimates of the random effects and the standard errors of the fixed effects depended upon mobility patterns and model specifications. Tw...Continue Reading

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Citations

Jun 20, 2019·Multivariate Behavioral Research·Sun-Joo ChoMatthew Naveiras
Nov 25, 2020·The British Journal of Mathematical and Statistical Psychology·Audrey J LerouxDavid R J Fikis

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Software Mentioned

Mplus
MLwiN
R R Core Team
WinBUGS

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