Party cues provide citizens with low‐cost information about their representatives’ policy positions. But what happens when elected officials deviate from the party line? Relying on the 2006 Cooperative Congressional Election Study (CCES), we examine citizens’ knowledge of their senators’ positions on seven high‐profile roll‐call votes. We find that although politically interested citizens are the group most likely to know their senator's position when she votes with the party, they are also the group most likely to incorrectly identify their senator's position when she votes against her party. The results indicate that when heuristics “go bad,” it is the norm for the most attentive segment of the public to become the most misinformed, revealing an important drawback to heuristic use.
The use of survey experiments has surged in political science. The most common design is the between-subjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to precisely estimate treatment effects. Alternative designs that involve repeated measurements of the dependent variable promise greater precision, but they are rarely used out of fears that these designs will yield different results than a standard design (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Contrary to common fears, repeated measures designs tend to yield the same results as more common designs while substantially increasing precision. These designs also offer new insights into treatment effect size and heterogeneity. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.
The use of survey experiments has surged in political science as a method for estimating causal effects. By far, the most common design is the betweensubjects design in which the outcome is only measured posttreatment. This design relies heavily on recruiting a large number of subjects to achieve adequate statistical power. Alternative designs that involve repeated measurement of the dependent variable promise greater precision, but are rarely used out of fears that these designs will bias treatment effects (e.g., due to consistency pressures). Across six studies, we assess this conventional wisdom by testing experimental designs against each other. Our results demonstrate that repeated measures designs substantially increase precision, while introducing little to no bias. These designs also offer new insights into the nature of treatment effects. We conclude by encouraging researchers to adopt repeated measures designs and providing guidelines for when and how to use them.
In recent years, legislators from both parties have drawn attention for their public support or opposition to leading figures within their party, such as Donald Trump and Nancy Pelosi. Yet we know relatively little about the extent to which voters care about members' professed loyalty to party leaders, especially when compared to competing considerations such as members' policy positions. In two national survey experiments, we independently manipulate hypothetical Democratic and Republican legislators' ideological reputations and levels of support for a leading party figure. Through our experiments and a supplemental observational analysis, we find that partisans in the electorate do use information about support for or opposition to leading party figures as a basis for evaluating members of Congress. At the same time, the results reaffirm the importance voters attach to policy and ideological factors and suggest these considerations are not overwhelmed by partisan loyalty considerations.
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