Novometric Analysis vs. GenODA vs. Log-Linear Model: Temporal Stability of the Association of Presidential Vote Choice and Party Identification

Paul R. Yarnold

Optimal Data Analysis, LLC

Prior research modeled presidential vote (democrat=1; republican=2) as a function of political party identification (democrat=1; independent=2; republican=3) and time (1972=72; 1976=76) via nonsaturated log-linear analysis. Results revealed: “The model {TP}{TV}{PV} has L2=1.88 with df=2. Hence, the best-fitting log-linear model need not include the interaction effect, TPV, thus indicating no significant change in the PV relationship over time… The two bivariate relationships, TP and TV, have substantive interpretations. They indicate that it is the marginal distributions of the vote choice and party identification (within categories of the other variables) which change between times of measurement” (p. 48). Exposition first uses exploratory novometric analysis to model presidenial vote (binary class variable) as a function of political party identification (multicategorical attribute) and time (an ordered attribute). The GenODA algorithm is then used to assess the temporal stability (time is the Gen variable) of the relationship between presidential vote (class variable) and political party identification (categorical attribute).

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