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

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,...

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Bibliographic Details
Published in:Journal of educational and behavioral statistics 2006-12, Vol.31 (4), p.437-448
Main Authors: Preacher, Kristopher J., Curran, Patrick J., Bauer, Daniel J.
Format: Article
Language:English
Subjects:
Coefficients
Computer analysis
Hyperactivity
Linear regression
Mathematical models
Mathematics
Modeling
Multilevel models
Regression analysis
Regression coefficients
Social interaction
Teacher's Corner
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Summary: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.
ISSN:1076-9986
1935-1054
DOI:10.3102/10769986031004437