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Moralized issues, such as abortion and same‐sex marriage, are some of the most polarizing and divisive issues in politics. These topics motivate political engagement but present a barrier to democratic resolution. Yet we know little about how some issues become "moral issues" and others do not. In this article, I argue that exposure to persuasive frames, particularly those eliciting anger and disgust, serves to moralize and polarize public opinion. I test these hypotheses across three experiments on emerging debates over food politics. The results consistently show that persuasive frames increase issue moralization and, in turn, facilitate polarization. A panel analysis demonstrates that the effect of a single exposure lasts at least two weeks. Mediation analyses suggest that feelings of disgust and anger help explain how persuasive frames moralize political attitudes, while anger alone seems to explain the polarizing effects of framing. Overall, the findings provide new insight into framing, emotion, and the development of moral issues.

Abstract
When researchers design an experiment, they usually hold potentially relevant features of the experiment constant. We call these details the "topic" of the experiment. For example, researchers studying the impact of party cues on attitudes must inform respondents of the parties' positions on a particular policy. In doing so, researchers implement just one of many possible designs . Clifford, Leeper, and Rainey (2023. "Generalizing Survey Experiments Using Topic Sampling: An Application to Party Cues." Forthcoming in Political Behavior. https://doi.org/10.1007/s11109-023-09870-1) argue that researchers should implement many of the possible designs in parallel—what they call "topic sampling"—to generalize to a larger population of topics. We describe two estimators for topic-sampling designs: First, we describe a nonparametric estimator of the typical effect that is unbiased under the assumptions of the design; and second, we describe a hierarchical model that researchers can use to describe the heterogeneity. We suggest describing the heterogeneity across topics in three ways: (1) the standard deviation in treatment effects across topics, (2) the treatment effects for particular topics, and (3) how the treatment effects for particular topics vary with topic-level predictors. We evaluate the performance of the hierarchical model using the Strengthening Democracy Challenge megastudy and show that the hierarchical model works well.

AbstractSocial scientists are frequently interested in who is most responsive to a treatment. By necessity, such moderation experiments often rely on observed moderators, such as partisan identity. These designs have led to an ongoing debate about where to measure moderators—immediately prior to the treatment, after the treatment, or in a prior wave of a panel survey. Measuring a moderator prior to the treatment is the most efficient and avoids posttreatment bias, but it raises concerns about priming. We contribute to this debate by systematically studying whether measuring moderators prior to an experiment affects the results. Across six different experiments, each involving a commonly used moderator, we find little evidence of priming effects, even when a moderator is placed immediately before the experiment. Our findings thus help resolve the debate, suggesting that researchers should measure moderators pretreatment. We conclude with advice on designing well‐powered moderation experiments.