Comparing three approaches for handling a fourth level of nesting structure in cluster-randomized trials

This study compared three approaches for handling a fourth level of nesting when analyzing cluster-randomized trial (CRT) data. Although CRT data analyses may include repeated measures, individual, and cluster levels, there may be an additional fourth level that is typically ignored. This study exam...

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Bibliographic Details
Published inThe Journal of experimental education Vol. 90; no. 3; pp. 712 - 730
Main Authors Glaman, Ryan, Chen, Qi, Henson, Robin K.
Format Journal Article
LanguageEnglish
Published Washington Routledge 28.04.2022
Taylor & Francis Inc
Subjects
Cluster analysis
Cluster-randomized trial
Data Analysis
Design
Educational research
Educational tests & measurements
Hierarchical Linear Modeling
latent growth curve modeling
multilevel modeling
nested data
Randomized Controlled Trials
Simulation
Statistical Bias
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Summary:This study compared three approaches for handling a fourth level of nesting when analyzing cluster-randomized trial (CRT) data. Although CRT data analyses may include repeated measures, individual, and cluster levels, there may be an additional fourth level that is typically ignored. This study examined the impact of ignoring this fourth level, accounting for it using a model-based approach, and accounting for it using a design-based approach on parameter and standard error (SE) estimates. Several fixed effect and random effect variance parameters and SEs were biased across all three models. Results suggest if a meaningful fourth level exists, researchers should acknowledge it using a design-based approach. If the fourth level is not practically important, researchers may ignore it altogether, resulting in more accurate parameter and SE estimates.
ISSN:0022-0973
1940-0683
DOI:10.1080/00220973.2020.1814684