Adam Herschel Smiley

Hostility and social distance between American Democrats and Republicans is pronounced and problematic. Previous research suggests cross-party contact reduces affective polarization. In two pre-registered studies, we examine whether attending church frequently, and with a greater proportion of out-partisans, predicts lower affective polarization in American Christians. In Study 1 ( N = 503), we find that more frequent church attendance is only marginally associated with lower social distance and affective polarization, though the relationship is significant when excluding policy consistency from the model. Furthermore, we find no significant linear relationship between the proportion of out-partisans at one’s church and social distance and affective polarization. Rather, exploratory analyses suggested a significant quadratic relationship, with social distance and affective polarization lowest among Christians with moderate levels of out-partisans in their congregations. Study 2 ( N = 497) replicated these findings with significant U-shaped relationships between out-party percentage and affective polarization. With high levels of out-partisans at church, respondents reported higher social isolation and political concealment. To understand affective polarization among American Christians it helps to know who they share a pew with at church.

American communities have become increasingly politically homogenous (Bishop & Cushing, 2008), with affective polarization, a form of animosity between political groups, concurrently on the rise (Iyengar et al., 2019). We hypothesized that individuals living in areas with greater proportions of out-partisans would report lower levels of affective polarization. In Study 1, 2016 American National Election Study respondents (N = 3,378) living in counties with more out-partisans averaged lower levels of party-directed affective polarization, controlling for partisan, policy, and demographic factors. In Study 2 (N = 362 via Prolific), similar analyses revealed that American partisans with more out-partisans in their counties and precincts reported lower levels of social distance from out-party members and greater cross-party contact, but not significantly lower voter-directed affective polarization. Cross-party contact mediated the relationship between the percentage of out-partisans in a community and affective polarization, highlighting the intersection of place and psychology in understanding this societal challenge.

American communities have become increasingly politically homogeneous, with affective polarization, a form of animosity between political groups, concurrently on the rise. We hypothesized that individuals living in areas with greater proportions of out-partisans would report lower levels of affective polarization. In Study 1, 2016 American National Election Study respondents ( N = 3,378) living in counties with more out-partisans averaged lower levels of party -directed affective polarization, controlling for partisan, policy, and demographic factors. In Study 2 ( N = 362 via Prolific), similar analyses revealed that American partisans with more out-partisans in their counties and precincts reported lower levels of social distance from out-party members and greater cross-party contact, but not significantly lower voter -directed affective polarization. Cross-party contact mediated the relationship between the percentage of out-partisans in a community and affective polarization, highlighting the intersection of place and psychology in understanding this societal challenge.

From the lens of the self-determination theory, when individuals experience greater job autonomy, they tend to experience greater well-being, work satisfaction, and less job burnout (Autin et al., 2021; Deci & Ryan, 2000; Fernet et al., 2010). However, less is known about how job autonomy relates to family life. We built on previous research and considered the association between job autonomy and parental burnout along with work–family conflict. We hypothesized that greater job autonomy would be associated with less parental burnout, and that strain related to work–family conflict (i.e., hours worked outside the home, being unmarried, number of children under the age of 5, and higher percentages of childcare responsibility) would have a main effect on parental burnout. In an original survey of 243 U.S. parents recruited via Prolific, we found that higher job autonomy (β = –.21, p < .001, 95% CI = [-.39, -.15]) predicted significantly less parental burnout. This pattern remained even when controlling for other measures of work–family conflict. Other measures of work–family conflict did not significantly predict more parental burnout. These findings indicate the importance of job autonomy in the lives of working parents.

Political polarization causes friction in many interpersonal relationships – but are close familial relationships buffered from this divisive force? In three pre-registered studies, we examine whether the relationship between college students and their parents can withstand perceived political difference. We find parent-child relationship quality is generally robust to perceived political differences. The few student perceptions of politics-related relationship impairments we did observe were statistically small, and there was no evidence that parents’ perceptions of political-related differences harmed their relationships with their children. This provides initial evidence that the parent-child relationship may be one that is relatively immune from the harmful effects of political polarization.

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender–career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data.

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data.

Ruling out the possibility that there is absolutely no effect or association between variables may be a good first step, but it is rarely the ultimate goal of science. Yet that is the only inference provided by traditional null hypothesis significance testing (NHST), which has been a mainstay of many scientific fields. Reliance on NHST also makes it difficult to define what it means to replicate a finding, and leads to an uncomfortable quandary in which increasing precision in data reduces researchers' ability to perform theory falsification. To solve these problems, in recent years several alternatives to traditional NHST have been proposed. However, each new test is described using its own terminology and practiced in different fields. We describe a simple, unified framework for conceptualizing all these tests so that it is not necessary to learn them separately. Moreover, the framework allows researchers to conduct any of these tests by asking just one question: is the confidence interval entirely outside the null region(s)? This framework may also help researchers choose the test(s) that best answers their research question when simply ruling out ‘no effect at all’ is not enough.