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

In: Dependent Data in Social Sciences Research

Author

Listed:
  • Bixi Zhang

    (City University of New York, The Graduate Center)

  • Francis L. Huang

    (University of Missouri, Educational, School and Counseling Psychology)

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.

Suggested Citation

  • Bixi Zhang & Francis L. Huang, 2024. "Investigating the Use of Robust Standard Errors to Account for Two-Way Clustering in Cross-Classified Data Structures," Springer Books, in: Mark Stemmler & Wolfgang Wiedermann & Francis L. Huang (ed.), Dependent Data in Social Sciences Research, edition 2, chapter 0, pages 355-375, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-56318-8_14
    DOI: 10.1007/978-3-031-56318-8_14
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