The article by Bauer and Curran (2005) explores methods for probing interactions in fixed and multilevel regression models, focusing on both inferential and graphical techniques. The authors review existing methods, such as the "pick-a-point" approach and the Johnson-Neyman (J-N) technique, and aim to generalize the J-N technique to a broader class of regression models. They provide detailed mathematical derivations and empirical examples to illustrate how to compute regions of significance and confidence bands for conditional effects. The J-N technique is particularly useful for understanding how the effect of one predictor varies with another, providing more detailed information than simpler methods. The article also discusses the challenges and limitations of applying these techniques in multilevel models, where the test statistics are only approximately distributed, and suggests that caution is needed in interpreting results. Overall, the study aims to enhance the interpretability of interaction effects in regression models, offering practical tools for researchers in various disciplines.The article by Bauer and Curran (2005) explores methods for probing interactions in fixed and multilevel regression models, focusing on both inferential and graphical techniques. The authors review existing methods, such as the "pick-a-point" approach and the Johnson-Neyman (J-N) technique, and aim to generalize the J-N technique to a broader class of regression models. They provide detailed mathematical derivations and empirical examples to illustrate how to compute regions of significance and confidence bands for conditional effects. The J-N technique is particularly useful for understanding how the effect of one predictor varies with another, providing more detailed information than simpler methods. The article also discusses the challenges and limitations of applying these techniques in multilevel models, where the test statistics are only approximately distributed, and suggests that caution is needed in interpreting results. Overall, the study aims to enhance the interpretability of interaction effects in regression models, offering practical tools for researchers in various disciplines.