Paul R. Yarnold
Optimal Data Analysis, LLC
Partial UniODA is a two-step procedure for: (a) identifying the exact statistical model that explicitly maximizes accuracy (normed against chance) achieved for the sample by using an attribute to classify observations’ actual class categories; while (b) simultaneously “controlling for” (eliminating) the effect of a confounding variable. Step One drops observations correctly classified using the confounder to predict class category: observations in the reduced sample weren’t correctly predicted by the confounder. Step Two investigates the non-confounded relationship underlying attribute and class variable using the reduced sample.