Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

Kristopher J. Preacher, Patrick J. Curran and Daniel J. Bauer

Journal of Educational and Behavioral Statistics, 2006, vol. 31, issue 4, 437-448

Abstract: Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the conditional relations is often a tedious and error-prone task. This article provides an overview of methods used to probe interaction effects and describes a unified collection of freely available online resources that researchers can use to obtain significance tests for simple slopes, compute regions of significance, and obtain confidence bands for simple slopes across the range of the moderator in the MLR, HLM, and LCA contexts. Plotting capabilities are also provided.

Keywords: interaction; Johnson—Neyman technique; latent curve analysis; multilevel modeling; multiple regression (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:31:y:2006:i:4:p:437-448

DOI: 10.3102/10769986031004437

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