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

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 departme...

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Bibliographic Details
Published in:Multivariate behavioral research 2016-01, Vol.51 (1), p.120-137
Main Authors: Choi, In-Hee, Wilson, Mark
Format: Article
Language:English
Subjects:
Adolescent
Algorithms
Bayes Theorem
Child
Computer Simulation
Data Interpretation, Statistical
Humans
Item response theory
Korea - epidemiology
longitudinal data analysis
Longitudinal Studies
Markov Chains
Models, Statistical
Monte Carlo Method
Multilevel Analysis - methods
multilevel modeling
school mobility
Schools
Students
Studies
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Summary: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. Two sets of large-scale longitudinal data are analyzed to illustrate applications of the CCMM-MLIRD for each type of school mobility.
ISSN:0027-3171
1532-7906
DOI:10.1080/00273171.2015.1114911