Examines how different methods of creating interaction terms impact parameter estimates in multilevel models, suggesting optimal approaches.
In multilevel models, interaction terms between two level 1 predictors have different methods of creation due to level 1 predictors containing both within and between variation (Loeys et al., 2018). Previous research on within-level interactions that consist of two level 1 predictors has different suggestions on how to correctly specify this interaction term. The purpose of the study is to understand how parameter estimates and standard errors in multilevel models are impacted by the different methods used to create this interaction term to determine which centering method results in the least amount of bias.
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Erica T. Dorman (2025) studied this question.
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