The practice of aggregating lower level predictors in clustered data: A reflection on reflective variables

The manifest aggregation of scores from both persons and items in multilevel modeling has been previously shown to result in biased estimates of predictor-outcome regression coefficients when used in the context of formative variables-variables in which the level two (L2) aggregate is formed from level one scores but where individuals are still expected to vary within clusters. Solutions to this problem have been offered in the form of partially and fully latent variable modeling specifications. The current study revisits this issue in the context of reflective variables, situations in which level one scores are obtained for the sole purpose of measuring an L2 variable. Under design specifications consistent with an L2 reflective variable, the current study uses population formula-based computations as well as Monte Carlo simulations to show conditions under which researchers may use a manifest aggregation of scores from persons and items for evaluating L2 relationships, thereby overcoming model convergence and identification challenges related to using latent variable modeling. We also highlight instances in which latent aggregations should be preferred. (PsycInfo Database Record (c) 2025 APA, all rights reserved).