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

Oleh:

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

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Journal Of Educational And Behavioral Statistics vol. 31 no. 4 (2006), page 437-448.
Topik: interaction; Johnson–Neyman technique; latent curve analysis; multilevel modeling; multiple regression
Fulltext: 437.pdf (279.84KB)

Isi artikel

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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