Two-step analysis of hierarchical data

Johannes Giesecke () and Ulrich Kohler
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Johannes Giesecke: Humboldt-University Berlin

Stata Journal, 2024, vol. 24, issue 2, 213-249

Abstract: In this article, we describe the package twostep, a bundle of pro- grams 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 alterna- tive 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.

Keywords: twostep; hierarchical model; mixed model; multilevel analysis; two-step modeling; two-stage regression; estimated dependent variable regression; EDV; exploratory data analysis; EDA; cross-level interaction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1177/1536867X241257801

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