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    Area of Science:

    • Statistics
    • Quantitative Psychology
    • Econometrics

    Background:

    • Multiple regression analysis is a widely used statistical technique.
    • Detecting and interpreting interaction effects between quantitative variables presents challenges.
    • Previous research suggested additive transformations to mitigate multicollinearity in regression models.

    Purpose of the Study:

    • To discuss issues in detecting and interpreting interaction effects in multiple regression.
    • To evaluate the utility of suggested additive transformations for multicollinearity reduction.
    • To examine the impact of these transformations on the interpretability of regression coefficients.

    Main Methods:

    • Conceptual analysis of multiple regression with product terms.
    • Discussion of additive transformations proposed by Cronbach (1987) and Dunlap and Kemery (1987).
    • Examination of the conditional nature of multiple regression and its implications.

    Main Results:

    • Additive transformations do not alter the overall statistical significance of interaction effects.
    • These transformations can negatively impact the interpretability of individual regression coefficients.
    • Multicollinearity may not be the sole reason for failing to detect interaction effects.

    Conclusions:

    • Additive transformations offer limited benefits for addressing multicollinearity in interaction terms.
    • Researchers should consider factors beyond multicollinearity when interaction effects are not detected.
    • Careful interpretation of regression coefficients is crucial when using interaction terms.