Effect size measures for multilevel models: definition, interpretation, and TIMSS example

Key Points

• To establish clear guidelines and standardized methods for defining, calculating, and interpreting effect size measures across various multilevel model structures.
• Evaluated effect size metrics including the intraclass correlation coefficient (ICC) for random effects and standardized regression coefficients or f2 for fixed effects.
• Analyzed the complexities of utilizing R2 across advanced structures, including three-level and random slopes models.
• Demonstrated calculation and reporting frameworks using empirical data from the Trends in International Mathematics and Science Study (TIMSS).
• Identified the intraclass correlation coefficient (ICC) as the primary appropriate metric for quantifying random-effects variance.
• Established standardized regression coefficients and f2 as robust effect size indicators for fixed effects in hierarchical datasets.
• Outlined specific computational challenges and solutions when interpreting R2 across three-level and random slope model variations.