In multilevel data, units at level 1 are nested in clusters at level 2, which in turn may be nested in even larger clusters at level 3, and so on. For continuous data, several authors have shown how to model multilevel data in a ‘wide’ or ‘multivariate’ format approach. We provide a general framework to analyze random intercept multilevel SEM in the ‘wide format’ (WF) and extend this approach for discrete data. In a simulation study, we vary response scale (binary, four response options), covariate presence (no, between-level, within-level), design (balanced, unbalanced), model misspecification (present, not present), and the number of clusters (small, large) to determine accuracy and efficiency of the estimated model parameters. With a small number of observations in a cluster, results indicate that the WF approach is a preferable approach to estimate multilevel data with discrete response options.

Additional Metadata
Keywords Discrete data, structural equation modeling, multilevel, random intercepts
Persistent URL dx.doi.org/10.1080/10705511.2019.1689366, hdl.handle.net/1765/123424
Journal Structural Equation Modeling
Citation
Barendse, M.T., & Rosseel, Y. (2020). Multilevel Modeling in the ‘Wide Format’ Approach with Discrete Data: A Solution for Small Cluster Sizes. Structural Equation Modeling, 0, 1–26. doi:10.1080/10705511.2019.1689366