Widespread belief in misinformation circulating online is a critical challenge for modern societies. While research to date has focused on psychological and political antecedents to this phenomenon, few studies have explored the role of digital media literacy shortfalls. Using data from preregistered survey experiments conducted around recent elections in the United States and India, we assess the effectiveness of an intervention modeled closely on the world’s largest media literacy campaign, which provided “tips” on how to spot false news to people in 14 countries. Our results indicate that exposure to this intervention reduced the perceived accuracy of both mainstream and false news headlines, but effects on the latter were significantly larger. As a result, the intervention improved discernment between mainstream and false news headlines among both a nationally representative sample in the United States (by 26.5%) and a highly educated online sample in India (by 17.5%). This increase in discernment remained measurable several weeks later in the United States (but not in India). However, we find no effects among a representative sample of respondents in a largely rural area of northern India, where rates of social media use are far lower.

In principle, experiments offer a straightforward method for social scientists to accurately estimate causal effects. However, scholars often unwittingly distort treatment effect estimates by conditioning on variables that could be affected by their experimental manipulation. Typical examples include controlling for posttreatment variables in statistical models, eliminating observations based on posttreatment criteria, or subsetting the data based on posttreatment variables. Though these modeling choices are intended to address common problems encountered when conducting experiments, they can bias estimates of causal effects. Moreover, problems associated with conditioning on posttreatment variables remain largely unrecognized in the field, which we show frequently publishes experimental studies using these practices in our discipline's most prestigious journals. We demonstrate the severity of experimental posttreatment bias analytically and document the magnitude of the potential distortions it induces using visualizations and reanalyses of real‐world data. We conclude by providing applied researchers with recommendations for best practice.

It is conventional to speak of voting as ''habitual.'' But what does this mean? In psychology, habits are cognitive associations between repeated responses and stable features of the performance context. Thus, ''turnout habit'' is best measured by an index of repeated behavior and a consistent performance setting. Once habit associations form, the response can be cued even in the absence of supporting beliefs and motivations. Therefore, variables that form part of the standard cognitive-based accounts of turnout should be more weakly related to turnout among those with a strong habit. We draw evidence from a large array of ANES surveys to test these hypotheses and find strong support. Keywords Habit Á Voter turnout Á AutomaticityTurnout to vote is one of the fundamental acts of democratic politics. As such, there has been a huge literature seeking to understand it-and a great deal has been learned. Even though a wide panoply of factors are, as hypothesized, related to turnout, those that are also related to candidate choice are almost invariably more strongly related to vote choice than to the decision to turnout. For example, Campbell et al. found that the Electronic supplementary material The online version of this article (

Since 2016, there has been an explosion of interest in misinformation and its role in elections. Research by news outlets, government agencies, and academics alike has shown that millions of Americans have been exposed to dubious political news online. However, relatively little research has focused on documenting the effects of consuming this content. Our results suggest that many claims about the effects of exposure to false news may be overstated, or, at the very least, misunderstood.

Political science researchers typically conduct an idiosyncratic search of possible model configurations and then present a single specification to readers. This approach systematically understates the uncertainty of our results, generates fragile model specifications, and leads to the estimation of bloated models with too many control variables. Bayesian model averaging (BMA) offers a systematic method for analyzing specification uncertainty and checking the robustness of one's results to alternative model specifications, but it has not come into wide usage within the discipline. In this paper, we introduce important recent developments in BMA and show how they enable a different approach to using the technique in applied social science research. We illustrate the methodology by reanalyzing data from three recent studies using BMA software we have modified to respect statistical conventions within political science.