Across two studies, we examined predictors of voters’ worry about the outcome of a political election, thus testing the application of the uncertainty navigation model to political waiting periods. Using a theoretically-grounded set of predictors, we assessed voters who preferred either the Democrats or Republicans to control the House of Representatives following the 2018 U.S. midterm election (N = 376) and Trump and Clinton voters leading up to the 2016 U.S. presidential election (N = 669). Findings generally supported the predictions of the model, such that people worried more as Election Day approached, as did people who saw the election outcome as more important, who believed it was more likely their preferred candidate would lose (Study 2), and who had a set of worry-exacerbating traits. Taken together, the findings provide considerable insight into the dynamics of worry during stressful waiting periods and support the generalizability of the uncertainty navigation model to political contexts.
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To be clear, the uncertainty navigation model is not a model of worry’s nature or function. Instead, it was developed to provide a framework for understanding how people feel and cope when they are waiting for important news. Worry is an inevitable part of that experience, and thus the model makes some predictions about the circumstances under which worry is most likely to arise (within the broader context of uncertain waiting periods). It is these predictions we test in the present paper.
Although this study was run after Study 2, which examined these processes in the 2016 U.S. presidential election, we present the studies in this order in the interest of the flow of the manuscript, such that Study 1 is followed by a larger and more complex Study 2 that included additional predictors.
For this analysis and all other multiple regression analyses in this paper, all variables were entered in the same step using the enter method.
No two predictor variables were correlated greater than r = |.49|, thus providing reassurance against multicollinearity concerns. We also inspected the residual plots for all multiple regression analyses and saw no cause for concern regarding non-normality or non-linear associations. Results were nearly identical when corrected for potential heteroscedasticity.
The possibility of repeat participation was due to a combination of an error on our part in neglecting to prevent repeat responses within the survey and the fact that we had to post several “batches” (essentially, versions of the study) on mTurk.
We tested the possibility that participants may have differed in notable ways across weeks. We ran one-way ANOVAs (continuous variables) and Chi square tests (categorical variables) comparing the eight time-based groups on demographic variables, religiosity, political orientation, and candidate preference. Only education and religiosity differed across groups. Importantly, neither education nor religiosity was notably correlated with worry, rs < .07, ps > .08.
Although we cannot be certain whether the validated properties of the LOT-R were retained with the missing item, a comparison between the results of Studies 1 and 2 provides reassurance that the measure worked in substantively the same way across studies.
These findings were consistent when examining the relationship between each individual social group (friends, family, coworkers, and acquaintances) and worry.
No two predictor variables were correlated greater than r = .43, thus providing reassurance against multicollinearity concerns. We also once again inspected residual plots, finding no cause for concern, and results were nearly identical when correcting for potential heteroscedasticity.
In Study 1, a regression analysis predicting worry from perceived importance (β = .20, p < .001) and political engagement (β = .33, p < .001) suggests that both variables are independent predictors of worry. In Study 2, the same regression analysis revealed that only perceived importance (β = .29, p < .001) and not engagement (β = − .002, p = .96) independently predicted worry.
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Rankin, K., Sweeny, K. Divided we stand, united we worry: Predictors of worry in anticipation of a political election. Motiv Emot 43, 956–970 (2019). https://doi.org/10.1007/s11031-019-09787-5