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

Open access
Preacher, Kristopher J., Curran, Patrick J., Bauer, Daniel J.·2006·Journal of Educational and Behavioral Statistics

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.

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Research works printed but not linked: 12

  1. Aiken LS, 1991, Multiple regression: Testing and interpreting interactions
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Metadata

Authors
Preacher, K. J., Curran, P. J., & Bauer, D. J.
Venue
Journal of Educational and Behavioral Statistics
Published
2006-12
Volume
31
Issue
4
Pages
437-448
ISSN
  • 1076-9986 (ISSN-L)
  • 1935-1054
OpenAlex
W2141125801
Cited by
5,034
References
1
FWCI
51.86
Type
journal article
Language
en
Open access
green
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986031004437

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.