Investigating the Use of Robust Standard Errors to Account for Two-Way Clustering in Cross-Classified Data Structures

Bixi Zhang () and Francis L. Huang
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Bixi Zhang: City University of New York, The Graduate Center
Francis L. Huang: University of Missouri, Educational, School and Counseling Psychology

Chapter Chapter 14 in Dependent Data in Social Sciences Research, 2024, pp 355-375 from Springer

Abstract: Abstract Traditional multilevel models are commonly used to analyze multilevel data with a pure nested structure. However, cross-classified data structures are present when observations are nested within two higher-level structures which in turn are not nested within each other. To capture the variations from both clustering dimensions, cross-classified random effects models (CCREMs) were developed as an extension to standard multilevel modeling. Alternatively, simpler linear regression models with cluster-robust standard errors (CRSEs) can also be effective when analyzing cross-classified data structures. However, CRSEs can be underestimated when the number of clusters is small. The present chapter extends the use of a small sample adjustment approach (i.e., CR2) of CRSEs when used with cross-classified data structures (i.e., two-way OLS-CR2) and investigates its performance under various conditions. Simulation results showed that two-way OLS-CR2 can be used for both complete and partial cross-classified data with at least 30 clusters in one of two clustering dimensions. An applied data example showed similar results using the two-way OLS-CR2 and CCREMs.

Keywords: Two-way clustering; Cross-classified models; CCREM (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-56318-8_14

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DOI: 10.1007/978-3-031-56318-8_14

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