Two-step analysis of hierarchical data

Abstract

In this article, we describe the package twostep, a bundle of programs to perform analyses of hierarchical data applying the two-step approach. We consider a two-level data setup in which “microlevel” units are nested within “macrolevel” units. One-step models (which can be fit using, for example, mixed) are the most common approach to modeling two-level data. The two-step approach is an alternative in which parameters associated with microlevel and macrolevel predictors are estimated separately for each level. It can be used as an alternative to one-step models if the estimand is a cross-level interaction. We also show how the two-step approach usefully complements one-step approaches by providing exploratory data analysis, descriptive graphs, and regression diagnostics.