When trying to determine the effect of one variable upon another, it is often the case that the strength of the effect depends on a third variable. For example, it is well known that alcohol has an effect on reaction time, but the size of that effect depends greatly on the body mass of the individual. These types of situations are the subject of moderation analysis, also known as interaction analysis. One of the techniques for studying these interactions is the Johnson-Neyman technique. While the statistical concepts are fairly simple, the computations are tedious and involved, and most software packages do not include a standard implementation. Furthermore, even if a software package does contain functions implementing the Johnson-Neyman technique, it may require an expensive license or programming abilities many researchers do not possess. Our goal is to produce a freely-available spreadsheet that makes implementing the Johnson-Neyman technique as simple as possible for researchers in the social sciences. The end-user need only enter the original data and the significance level. The spreadsheet will automate the data transformations, estimation of regression coefficients, covariance calculations, and creation of two high-quality figures for describing the interaction. In this presentation, we will describe the underlying statistics, give an overview of how the spreadsheet implements the Johnson-Neyman technique, and show the resulting figures from an example data set.