Interaction Effects in Multiple Regression, Issue 72
Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis.
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African American African American jurors calculate causal change given confidence interval consider continuous predictor corresponds dummy coding dummy variable equals estimated standard error ethnicity European American defendant European American jurors example first-order moderator variable focal independent variable function gender difference given a one-unit group scored guilt attributions hierarchical income intent to vaccinate interaction analysis interaction contrasts interaction effect interaction terms Jaccard latent variable Latinos liberalness-conservativeness linear males mean center mean difference metric multiple regression null hypothesis omnibus interaction omnibus test one-unit increase ordinal interactions outcome variable peer pressure perceived efficacy perceived severity perceived susceptibility predicted to change predictor variables product term qualitative predictors reference group reflects regression analysis regression coefficients regression equation regression lines relevant researcher sample mean scale second-order moderator variable significance test simple effects slope difference squared multiple correlation statistical power statistically significant Table three-way interaction transformation strategy units vari yields zero