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Multilevel modeling in the 'wide format' approach with discrete data : a solution for small cluster sizes

Mariska Barendse (UGent) and Yves Rosseel (UGent)
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Abstract
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.
Keywords
CONFIRMATORY FACTOR-ANALYSIS, MAXIMUM-LIKELIHOOD-ESTIMATION, STRUCTURAL EQUATION MODELS, ITEM FACTOR-ANALYSIS, LINEAR-MODELS, ORDINAL VARIABLES, GENERAL-MODEL, GROWTH-CURVES, LEVEL, INVARIANCE, Discrete data, structural equation modeling, multilevel, random intercepts

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MLA
Barendse, Mariska, and Yves Rosseel. “Multilevel Modeling in the ‘wide Format’ Approach with Discrete Data : A Solution for Small Cluster Sizes.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, vol. 27, no. 5, 2020, pp. 696–721, doi:10.1080/10705511.2019.1689366.
APA
Barendse, M., & Rosseel, Y. (2020). Multilevel modeling in the “wide format” approach with discrete data : a solution for small cluster sizes. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 27(5), 696–721. https://doi.org/10.1080/10705511.2019.1689366
Chicago author-date
Barendse, Mariska, and Yves Rosseel. 2020. “Multilevel Modeling in the ‘wide Format’ Approach with Discrete Data : A Solution for Small Cluster Sizes.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 27 (5): 696–721. https://doi.org/10.1080/10705511.2019.1689366.
Chicago author-date (all authors)
Barendse, Mariska, and Yves Rosseel. 2020. “Multilevel Modeling in the ‘wide Format’ Approach with Discrete Data : A Solution for Small Cluster Sizes.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 27 (5): 696–721. doi:10.1080/10705511.2019.1689366.
Vancouver
1.
Barendse M, Rosseel Y. Multilevel modeling in the “wide format” approach with discrete data : a solution for small cluster sizes. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL. 2020;27(5):696–721.
IEEE
[1]
M. Barendse and Y. Rosseel, “Multilevel modeling in the ‘wide format’ approach with discrete data : a solution for small cluster sizes,” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, vol. 27, no. 5, pp. 696–721, 2020.
@article{8687602,
  abstract     = {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.},
  author       = {Barendse, Mariska and Rosseel, Yves},
  issn         = {1070-5511},
  journal      = {STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL},
  keywords     = {CONFIRMATORY FACTOR-ANALYSIS,MAXIMUM-LIKELIHOOD-ESTIMATION,STRUCTURAL EQUATION MODELS,ITEM FACTOR-ANALYSIS,LINEAR-MODELS,ORDINAL VARIABLES,GENERAL-MODEL,GROWTH-CURVES,LEVEL,INVARIANCE,Discrete data,structural equation modeling,multilevel,random intercepts},
  language     = {eng},
  number       = {5},
  pages        = {696--721},
  title        = {Multilevel modeling in the 'wide format' approach with discrete data : a solution for small cluster sizes},
  url          = {http://dx.doi.org/10.1080/10705511.2019.1689366},
  volume       = {27},
  year         = {2020},
}

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