The article by Bauer and Curran (2005) discusses methods for probing interactions in fixed and multilevel regression models. It introduces the Johnson-Neyman (J-N) technique as a more general approach for evaluating interaction effects compared to the traditional "pick-a-point" method. The J-N technique allows for the identification of regions where the effect of a predictor varies significantly with the level of a moderator, and it provides confidence bands that graphically depict the precision of these effects across the entire range of the moderator. This method is particularly useful for continuous by continuous interactions and has been extended to multilevel models. The article also addresses the limitations of the pick-a-point approach, such as its limited ability to examine the full range of the moderator and its potential for Type I error accumulation when multiple tests are performed. The J-N technique offers a more comprehensive and accurate way to explore interaction effects, especially in complex models with random effects. The authors provide empirical examples to illustrate the application of these techniques in fixed and multilevel regression models.The article by Bauer and Curran (2005) discusses methods for probing interactions in fixed and multilevel regression models. It introduces the Johnson-Neyman (J-N) technique as a more general approach for evaluating interaction effects compared to the traditional "pick-a-point" method. The J-N technique allows for the identification of regions where the effect of a predictor varies significantly with the level of a moderator, and it provides confidence bands that graphically depict the precision of these effects across the entire range of the moderator. This method is particularly useful for continuous by continuous interactions and has been extended to multilevel models. The article also addresses the limitations of the pick-a-point approach, such as its limited ability to examine the full range of the moderator and its potential for Type I error accumulation when multiple tests are performed. The J-N technique offers a more comprehensive and accurate way to explore interaction effects, especially in complex models with random effects. The authors provide empirical examples to illustrate the application of these techniques in fixed and multilevel regression models.