Response Surface Models to Analyze Nonlinear Group Phenomena

Abstract

Using Response Surface Methodology (RSM) is a lot like being a chef, mixing together different combinations of ingredients to see which ones come together to make the best dish. In this situation, strict linear thinking no longer applies. For instance, adding just the right amount of salt to a dish can bring out the sweetness in desserts or bump up the taste in more savory dishes. But, too much salt can overwhelm the flavor of a dish, just as too little salt can leave it tasting bland and unsatisfying. Chefs must find that perfect amount of salt that takes their dish from acceptable to exceptional. In addition, chefs must consider how the salt will interact with other ingredients in the dish. For example, salt interacts with the yeast in bread to help create texture, and it helps sausage and other processed meals come together by gelatinizing the proteins. Likewise, RSM helps us find the optimal amount of an outcome variable based on two or more independent variables. This chapter will provide an introduction on how to use RSM to analyze nonlinear group phenomenon. First, the chapter will outline a brief history and background of the approach. Then, the chapter will walk the reader through a tutorial demonstrating how to execute the second-order model using the PROC RSREG function in SAS. Data previously collected from virtual groups in the game EverQuestll will be provided as an example. (PsycINFO Database Record (c) 2017 APA, all rights reserved)