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Cross-classified random effects modeling (CCREM) is common, but ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) may be better. FE-CRVE is recommended when CCREM assumptions are uncertain.

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Area of Science:

  • Statistics
  • Psychology
  • Education Research

Background:

  • Cross-classified random effects modeling (CCREM) is widely used for complex data structures.
  • Alternative methods like OLS-CRVE and FE-CRVE offer potential advantages due to weaker assumptions.

Purpose of the Study:

  • To compare the performance of CCREM, OLS-CRVE, and FE-CRVE.
  • To evaluate these methods under various assumption violations, including homoscedasticity, exogeneity, and unmodeled random slopes.

Main Methods:

  • A Monte Carlo simulation study was employed.
  • The study systematically varied conditions related to homoscedasticity, exogeneity, and random slopes.

Main Results:

  • CCREM performed best when all its assumptions were met.
  • OLS-CRVE and FE-CRVE showed comparable or superior performance when homoscedasticity was violated.
  • FE-CRVE demonstrated adequate performance when exogeneity was violated, and both OLS-CRVE and FE-CRVE yielded more accurate inferences with unmodeled random slopes.

Conclusions:

  • Two-way FE-CRVE is a robust alternative to CCREM.
  • FE-CRVE is particularly recommended when the assumptions of CCREM (homoscedasticity, exogeneity) may not hold.