To Disaggregate or Not to Disaggregate: A Focus on Covariates in Multilevel Models

It is routinely recommended that level-1 variables in multilevel models be disaggregated when they are of substantive importance. Yet, the consensus on the disaggregation of level-1 covariates is more mixed. Disaggregation clarifies interpretation and reduces bias in the covariate, though some methodologists argue that it is unnecessary when the covariate itself is not of substantive interest. Our study builds off recent work to explore the tradeoffs between bias and precision when choosing to disaggregate level-1 covariates when the primary interest lies in a level-2 predictor. Using a Monte Carlo simulation, we examine how factors such as the intraclass correlation, the magnitude of the contextual effect, the within- and between-level effect sizes, the correlation among level-2 effects, sample size at both levels, and the method of disaggregation (manifest versus latent) influence bias, precision, and power of a level-2 focal estimate. Our findings suggest that although disaggregation generally improves interpretability and reduces bias, there are conditions where a non-disaggregated approach may yield greater precision. These insights inform best practices for handling lower-level covariates in multilevel models.It is routinely recommended that level-1 variables in multilevel models be disaggregated when they are of substantive importance. Yet, the consensus on the disaggregation of level-1 covariates is more mixed. Disaggregation clarifies interpretation and reduces bias in the covariate, though some methodologists argue that it is unnecessary when the covariate itself is not of substantive interest. Our study builds off the work of Rights et al. to explore the tradeoffs between bias and precision when choosing to disaggregate level-1 covariates when the primary interest lies in a level-2 predictor. Using a Monte Carlo simulation, we examine how factors such as the intraclass correlation, the magnitude of the contextual effect, the within- and between-level effect sizes, the correlation among level-2 effects, sample size at both levels, and the method of disaggregation (manifest versus latent) influence bias, precision, and power of a level-2 focal estimate. Our findings suggest that although disaggregation generally improves interpretability and reduces bias, there are conditions where a non-disaggregated approach may yield greater precision. These insights inform best practices for handling lower-level covariates in multilevel models.