Version 1.0 of the workbook may be found in the Supplementary Material accompanying this article, and future releases may be found at. The target audience is researchers without programming experience who wish to probe interactions.

This article describes CAHOST (a concatenation of the first two letters of the authors' last names), an implementation of the JN technique in a Microsoft Excel 2013 macro-enabled workbook (.xlsm) which produces high-quality publication-ready graphics, requires no programming capabilities, and limits error in data entry (e.g., entering coefficients). One of the tools used in moderation analysis is the Johnson-Neyman (JN) technique. Taking the view that X is the primary variable of interest or the “focal predictor,” the other explanatory variable M is the “moderator.” Thus, the study of how the explanatory variables interact is often called moderation analysis (Cohen et al., 2003 Hayes, 2013b). In such a situation, the two explanatory variables are said to “interact” in their influence on the response variable. For example, a person's blood alcohol content is influenced by the amount of alcohol that person has ingested, but the size of this influence depends on, among other things, the body mass of that person. When a researcher seeks to quantify the linear effect an explanatory variable, X, has on a response variable, Y, the size of that effect may depend on a second explanatory variable, M.

Regression data analysis excel 2013