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Experimental political science has transformed in the last decade. The use of experiments has dramatically increased throughout the discipline, and technological and sociological changes have altered how political scientists use experiments. We chart the transformation of experiments and discuss new challenges that experimentalists face. We then outline how the contributions to this volume will help scholars and practitioners conduct high-quality experiments.
Experimental political science has changed. In two short decades, it evolved from an emergent method to an accepted method to a primary method. The challenge now is to ensure that experimentalists design sound studies and implement them in ways that illuminate cause and effect. Ethical boundaries must also be respected, results interpreted in a transparent manner, and data and research materials must be shared to ensure others can build on what has been learned. This book explores the application of new designs; the introduction of novel data sources, measurement approaches, and statistical methods; the use of experiments in more substantive domains; and discipline-wide discussions about the robustness, generalizability, and ethics of experiments in political science. By exploring these novel opportunities while also highlighting the concomitant challenges, this volume enables scholars and practitioners to conduct high-quality experiments that will make key contributions to knowledge.
One of the strongest findings across the sciences is that publication bias occurs. Of particular note is a “file drawer bias” where statistically significant results are privileged over nonsignificant results. Recognition of this bias, along with increased calls for “open science,” has led to an emphasis on replication studies. Yet, few have explored publication bias and its consequences in replication studies. We offer a model of the publication process involving an initial study and a replication. We use the model to describe three types of publication biases: (1) file drawer bias, (2) a “repeat study” bias against the publication of replication studies, and (3) a “gotcha bias” where replication results that run contrary to a prior study are more likely to be published. We estimate the model’s parameters with a vignette experiment conducted with political science professors teaching at Ph.D. granting institutions in the United States. We find evidence of all three types of bias, although those explicitly involving replication studies are notably smaller. This bodes well for the replication movement. That said, the aggregation of all of the biases increases the number of false positives in a literature. We conclude by discussing a path for future work on publication biases.
Affective polarization – partisans’ dislike and distrust of those from the other party – has reached historically high levels in the United States. While numerous studies estimate its effect on apolitical outcomes (e.g., dating and economic transactions), we know much less about its effects on political beliefs. We argue that those who exhibit high levels of affective polarization politicize ostensibly apolitical issues and actors. An experiment focused on responses to COVID-19 that relies on pre-pandemic, exogenous measures of affective polarization supports our expectations. Partisans who harbor high levels of animus towards the other party do not differentiate the “United States’” response to COVID-19 from that of the Trump administration. Less affectively polarized partisans, in contrast, do not politicize evaluations of the country’s response. Our results provide evidence of how affective polarization, apart from partisanship itself, shapes substantive beliefs. Affective polarization has political consequences and political beliefs stem, in part, from partisan animus.