To Log or Not to Log: Bootstrap as an Alternative to the Parametric Estimation of Moderation Effects in the Presence of Skewed Dependent Variables

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Nonlinear Transformations in Organizational Research: Possible Problems and Potential Solutions (2018, background): “For example, in their Monte Carlo simulation, Russell and Dean (2000) demonstrated that log transformations can severely decrease estimates of true moderator effects, substantially increasing Type II error (incorrectly concluding that an effect is nonsignificant).”
Bootstrapped Confidence Intervals as an Approach to Statistical Inference (2005, methodology): “Studies evaluating their use (e.g., Russell & Dean, 2000) tend to focus on the second and third of the three potential advantages listed above: the fact that they do not require restrictive, and often unrealistic, assumptions (e.g., about measurements being normally distributed); or the fact that…”
Child Regulation of Negative Emotions and Depressive Symptoms: The Moderating Role of Parental Emotion Socialization (2015, methodology): “This form of resampling has been used in previous analyses to adjust for bias in datasets (Hayes 2009; Russell and Dean 2000).”
Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects (2021, background): “The lack of a significant interaction term thus does not mean a moderation effect would not exist in absolute terms (Russell & Dean, 2000); it is simply presented in an alternative form, as Figure 11 shows.”