To EVALUATE THE INFLUENCE of the presidential campaign on voter decision making, we compare the effect of cross-pressures across levels of campaign exposure or in different campaign contexts. Because the dependent variable is a binary outcome (1 = voted for opposing party candidate, 0 = voted for own party candidate), we have estimated the effects in separate models. We do not estimate an interaction between cross-pressures and campaign exposure because it is not possible in nonlinear models (e.g., logit model) to evaluate an interaction effect simply by looking at the sign, magnitude, or statistical significance of the coefficient on the interaction term. For instance, see discussion in Ai and Norton, “Interaction Terms in Logit and Probit Models.” For easier interpretation of the results, we have estimated the substantive effects across all relevant models for the same respondent type (based on global means and modes) using Clarify software to calculate the 90 percent confidence bounds around those estimates. We also calculate the Wald chi-squared statistic to determine if the difference in estimated coefficients across the low-exposure and high-exposure groups is statistically significant (assuming residual variation is the same across groups).