Evaluating Non-Confounded Association of an Attribute and a Class Variable Using Partial UniODA

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.

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