No Evidence That an Ebola Outbreak Influenced Voting Preferences in the 2014 Elections After Controlling for Time-Series Autocorrelation: A Commentary on Beall, Hofer, and Schaller (2016)

In a recent article, Beall, Hofer, and Schaller (2016) used observational time-series data to test the hypothesis that the 2014 Ebola outbreak influenced the 2014 U.S. federal elections. This represents one example of a recurring psychological interest in using observational data (a) to assess long-term temporal predictions of psychological theories in naturalistic settings ( Jebb, Tay, Wang, & Huang, 2015) and (b) to examine how psychological theories can predict cross-population variation in attitudes and behavior (Eppig, Fincher, & Thornhill, 2010; Fincher & Thornhill, 2012; Gelfand et al., 2011; Murray, Schaller, & Suedfeld, 2013; Schaller & Murray, 2008). While such nonexperimental designs hold considerable promise, they also introduce analytic challenges that can lead to spurious inferences if left unaddressed (Hackman & Hruschka, 2013; Hruschka & Hackman, 2014; Hruschka & Henrich, 2013; Jebb et al., 2015; Pollet, Tybur, Frankenhuis, & Rickard, 2014). Here, we use Beall et al.’s analyses to illustrate how using observational data without attention to one long-recognized threat to inference in time-series data—temporal autocorrelation—can lead to spurious inferences (Yule, 1926). Beall et al. used the coincidence of the 2014 Ebola epidemic and the 2014 U.S. federal elections (as well as ancillary analyses of Canadian elections) to assess two hypotheses derived from theories of the behavioral immune system (Schaller & Murray, 2008). First, they hypothesized that perceived threat of disease should increase political conservatism. Second, they hypothesized that disease threats may increase conformism and lead to a bandwagon effect, “the phenomenon in which voters show an increased inclination to support whichever political candidate is leading in recent polls” (p. 596). Beall et al. assessed these hypotheses by correlating 2-month time series of (a) online searches for the term “Ebola” and (b) daily polling data for U.S. congressional elections, a month before and a month after the Centers for Disease Control and Prevention’s announcement of the first Ebola case in the United States (September 30, 2014). Beall et al. found strong correlations between daily Ebola search volumes during the months of September and October and support for conservative candidates at national and state levels over that same time period. They interpreted this correlation between time series as support for their first hypothesis. Beall et al. also found that correlations between Ebola searches and Republican support were stronger in states that started off with greater support for Republican candidates and with longstanding Republican voting norms, and they interpreted this result as support for the bandwagon effect. These analyses relied on correlations between two time-series variables—Ebola search volume and daily polling—taken over 2 months. When two variables evolve over time, they can frequently look highly correlated, even without any underlying causal relationship between them (Yule, 1926; see Koplenig & Müller-Spitzer, 2016, for an illustrative example). This results from temporal autocorrelation—greater similarity in data points that are closer to each other in time—and the common existence of long-run trends in time-series data that can create 680396 PSSXXX10.1177/0956797616680396Tiokhin, HruschkaInfections, Elections, and Autocorrelation research-article2017