2. Response Surface Models to Analyze Nonlinear Group Phenomena

Because of the large amount of group data available today (e.g., online groups, archival records, electronic emails, etc.), response surface methodology (RSM), mostly common in the natural and physical sciences, can be a useful tool to analyze nonlinear group phenomena. RSM typically requires multiple observations on each level of each independent variable, which has mainly been the reason for its nonexistent use in experimental group research. However, the main goal of RSM is optimization, a very common line of inquiry in functional group research. In other words, RSM attempts to locate the precise values of independent variables that will predict an optimal (or minimal) response in the dependent variable. RSM does this by fitting a quadratic and interaction-term regression model and uses a canonical analysis to find a solution to the response surface (i.e., the shape of the predicted response). The following chapter describes the basic mathematical logic underlying RSM and provides an example using groups in the online video game EverQuestII. The code using the PROC RSREG feature in SAS is used in the tutorial.