Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package
sjPlot (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called
plot_model served as the basis for the creation of some custom functions. One of these functions is
alias_interaction_plot, which allows the plotting of interactions between a continuous variable and a categorical variable. Importantly, the categorical variable is replaced with an alias variable. This feature allows the back-transformation of the categorical variable to facilitate the communication of the results, for instance, when the categorical variable was sum-coded, which has been recommended for mixed-effects models (Brauer & Curtin, 2018).
Below, we’ll use the function with a model fitted using
lmerTest (Kuznetsova et al., 2022), although the function also works with several other models (see sjPlot manual). The plot can be reproduced using the materials at https://osf.io/gt5uf.
Alias interaction plot
Bernabeu, P. (2022). Language and sensorimotor simulation in conceptual processing: Multilevel analysis and statistical power. Lancaster University. https://doi.org/10.17635/lancaster/thesis/1795
Brauer, M., & Curtin, J. J. (2018). Linear mixed-effects models and the analysis of nonindependent data: A unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychological Methods, 23(3), 389–411. https://doi.org/10.1037/met0000159
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2022). Package ’lmerTest’. CRAN. https://cran.r-project.org/web/packages/lmerTest/lmerTest.pdf
Lüdecke, D. (2022). Package ’sjPlot’. CRAN. https://cran.r-project.org/web/packages/sjPlot/sjPlot.pdf