Novometric Analysis Predicting Voter Turnout: Race, Education, and Organizational Membership Status

Paul R. Yarnold

Optimal Data Analysis, LLC

Prior research modeled voter turnout (“not voted”=0; “voted”=1) as a function of race (“white”=1; “black”=2), education (“less than high school”=1; “high school graduate”=2; “college”=3), and memberships in organizations (“none”=0; “one or more”=1) via log-linear analysis. Results revealed: “…in the absence of a confirmatory analysis with another sample and in the absence of any compelling theoretical argument for expecting the particular three-variable interaction, (our own preference) would be to choose the more parsimonious model 28, {MER}{MV}{EV}. That model gives a satisfactory fit to the full crosstabulation (L2=4.76, df=5, p<0.45) without resort to a complex three-variable interaction. It also omits the race-turnout effect which is known to be trivial, but which would have to be included in model 34 because it is subsumed in hierarchical relation to the {ERV} term” (p. 40). Exploratory novometric analysis is used to model voter turnout (binary class variable) as a function of race (a categorical attribute), education and number of organizational memberships (both treated as ordered attributes measured on categorical ordinal scales).

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