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
Multilevel modelComputer scienceSimple linear regressionLinear regressionSimple (philosophy)Range (aeronautics)Regression analysisLinear modelConfidence intervalRegressionTask (project management)StatisticsMachine learningMathematicsEngineering
Affiliated Institutions
Related Publications
Simple means to improve the interpretability of regression coefficients
Summary 1. Linear regression models are an important statistical tool in evolutionary and ecological studies. Unfortunately, these models often yield some uninterpretable estima...
A Comparison of Least Squares and Latent Root Regression Estimators
Miilticollinesrity among the columns of regressor variables is known to cause severe distortion of the least squares estimates of the parameters in a multiple linear regression ...
Partial least squares regression and projection on latent structure regression (PLS Regression)
Abstract Partial least squares (PLS) regression ( a.k.a. projection on latent structures) is a recent technique that combines features from and generalizes principal component a...
On the misuse of residuals in ecology: regression of residuals vs. multiple regression
1 Residuals from linear regressions are used frequently in statistical analysis, often for the purpose of controlling for unwanted effects in multivariable datasets. This paper ...
Repeatability for Gaussian and non‐Gaussian data: a practical guide for biologists
Repeatability (more precisely the common measure of repeatability, the intra‐class correlation coefficient, ICC) is an important index for quantifying the accuracy of measuremen...
Publication Info
- Year
- 2006
- Type
- article
- Volume
- 31
- Issue
- 4
- Pages
- 437-448
- Citations
- 4880
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
4880
OpenAlex
Cite This
Kristopher J. Preacher,
Patrick J. Curran,
Daniel J. Bauer
(2006).
Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis.
Journal of Educational and Behavioral Statistics
, 31
(4)
, 437-448.
https://doi.org/10.3102/10769986031004437
Identifiers
- DOI
- 10.3102/10769986031004437