Interaction analyses — Interpreting effect sizes (part 2)

A simple example — binary moderator

Extending to a continuous moderator

Quartiles Slope Correlation
2 1.63 0.99
3 2.21 1.00
4 2.57 1.00
5 2.83 1.00
6 3.03 1.00
7 3.19 1.00
8 3.32 1.00
9 3.44 0.99
10 3.54 0.99
11 3.63 0.99
12 3.71 0.99
13 3.78 0.99
14 3.85 0.99
15 3.91 0.99
Call:
lm(formula = Slope~ log(Quartiles), data = quart_comparison)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.00341 0.05489 18.28 3.97e-10 ***
log(Quartiles) 1.09602 0.02641 41.49 2.49e-14 ***
Multiple R-squared: 0.9931, Adjusted R-squared: 0.9925
F-statistic: 1722 on 1 and 12 DF, p-value: 2.488e-14

Final thoughts and next steps

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PhD in Neuroscience, Postdoc at U Pittsburgh. https://twitter.com/david_baranger

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David Baranger

David Baranger

PhD in Neuroscience, Postdoc at U Pittsburgh. https://twitter.com/david_baranger

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