JohnsonNeyman
Johnson-Neyman, also written Johnson–Neyman, is a statistical procedure used in moderation analysis to identify the values of a moderator variable for which the effect of a predictor on an outcome is statistically significant. It is applied in linear regression models that include an interaction term between a focal predictor X and a moderator Z. The core idea is to determine regions of Z where the conditional effect of X on Y, expressed as the simple slope ∂Y/∂X = b1 + b3Z, is significantly different from zero.
The method works by considering how the standard error of the conditional effect depends on Z. The
Implementation and use: The Johnson–Neyman approach is widely used in psychology, social sciences, and other fields
Limitations: The procedure relies on standard regression assumptions (linearity, normality of errors, homoscedasticity) and model specification.