Implementing Automated Nonparametric Statistical Analysis on Functional Analysis Data: A Guide for Practitioners and Researchers

Using Risk Ratios to Quantify Potential Behavior-Environment Relations

Behavior-environment functional relations are the units of explanation in applied behavior analysis (ABA). Whether hypothesized experimentally or descriptively, quantification of putative functional relations improves our ability to predict and influence behavior. Risk ratios are an accessible, straightforward quantitative analysis that can serve this purpose. They have been employed to great effect in other fields (e.g., medicine, public health), but are rarely used within ABA. In this tutorial, we describe risk ratios and how they are calculated, discuss why risk ratios are well suited for quantifying behavior-environment relations, and illustrate their utility and applicability across five demonstrations from real clinical cases. Recommendations for the use of risk ratios in research and practice are discussed.

Does the choice of a linear trend-assessment technique matter in the context of single-case data?

Trend is one of the data aspects that is an object of assessment in the context of single-case experimental designs. This assessment can be performed both visually and quantitatively. Given that trend, just like other relevant data features such as level, immediacy, or overlap does not have a single operative definition, a comparison among the existing alternatives is necessary. Previous studies have included illustrations of differences between trend-line fitting techniques using real data. In the current study, I carry out a simulation to study the degree to which different trend-line fitting techniques lead to different degrees of bias, mean square error, and statistical power for a variety of quantifications that entail trend lines. The simulation involves generating both continuous and count data, for several phase lengths, degrees of autocorrelation, and effect sizes (change in level and change in slope). The results suggest that, in general, ordinary least squares estimation performs well in terms of relative bias and mean square error. Especially, a quantification of slope change is associated with better statistical results than quantifying an average difference between conditions on the basis of a projected baseline trend. In contrast, the performance of the split-middle (bisplit) technique is less than optimal.