Spatial conditionally autoregressive (CAR) models in a hierarchical Bayesian framework can be informative for understanding state politics, or any similar population of border-defined observations. This article explains how a hierarchical CAR model is specified and estimated and then uses Monte Carlo analyses to show when the CAR model offers efficiency gains. We apply this model to data structures common to state politics: A cross-sectional example replicates Erikson, Wright and McIver’s (1993) Statehouse Democracy model and a multilevel panel model example replicates Margalit’s (2013) study of social welfare policy preferences. The CAR model fits better in each case and some inferences differ from models that ignore geographic correlation.

We update past work on the democratic deficit, defined as incongruence between majority public preference and public policy in the American states. We reconsider public opinion and state policy on seven issues related to immigration and health questions. Using original data from the 2014 Cooperative Congressional Election Survey as well as new data on state policy and other predictors, we show that these seven issues have distinct qualities from Lax and Phillips’s larger basket of 39 policy questions in different issue areas. From 2008 to 2014, the democratic deficit on these issues diminished somewhat in the presence of a heightened level of issue salience.

This article demonstrates how the party identification of various demographic groups in California and Texas changed in response to the gubernatorial campaigns of Pete Wilson and George W. Bush. Using aggregated time series of Field Poll, Texas Poll, and Gallup data, difference-in-differences results show that Wilson's embrace of Proposition 187 was followed by significant Hispanic movement toward the Democratic Party in California. Time series analysis substantiates that this action led to a long-term 7.1 percentage point Democratic shift among California's Hispanics. This suggests that state-level actors can influence partisan coalitions in their state, beyond what would be expected from national-level factors.

This article describes the current debate on the practice of preregistration in political science—that is, publicly releasing a research design before observing outcome data. The case in favor of preregistration maintains that it can restrain four potential causes of publication bias, clearly distinguish deductive and inductive studies, add transparency regarding a researcher’s motivation, and liberate researchers who may be pressured to find specific results. Concerns about preregistration maintain that it is less suitable for the study of historical data, could reduce data exploration, may not allow for contextual problems that emerge in field research, and may increase the difficulty of finding true positive results. This article makes the case that these concerns can be addressed in preregistered studies, and it offers advice to those who would like to pursue study registration in their own work.

This article considers how a key legislative vote—that is, the August 2011 vote to raise the federal debt ceiling—influenced the 2012 elections for the US House of Representatives. Two outcomes are analyzed: (1) the incumbents’ ability to retain their seats through the 2012 general election, and (2) their share of the two-party vote for members who faced a general-election competitor. In developing this study, the research design was registered and released publicly before the votes were counted in 2012. Therefore, this article also illustrates how study preregistration can work in practice for political science. The findings show that seat retention did not vary with the treatment; however, incumbents who voted against raising the debt ceiling earned an additional 2.4 percentage points of the two-party vote.

We develop a new approach for modeling public sentiment by micro-level geographic region based on Bayesian hierarchical spatial modeling. Recent production of detailed geospatial political data means that modeling and measurement lag behind available information. The output of the models gives not only nuanced regional differences and relationships between states, but more robust state-level aggregations that update past research on measuring constituency opinion. We rely here on the spatial relationships among observations and units of measurement in order to extract measurements of ideology as geographically narrow as measured covariates. We present an application in which we measure state and district ideology in the United States in 2008.

This article asks what shaped immigrant policy in the 50 states between 2005 and 2011. Theoretically, politicians are influenced by electoral considerations as they craft laws. Law-makers consider both current public opinion and how the electorate is likely to change, at least in the near future. Empirically, the article analyses an original dataset on immigrant-related laws enacted by the states with a Bayesian spatial conditionally autoregressive model. The analysis shows that state immigrant policy is affected primarily by legislative professionalism, electoral ideology, state wealth and change in the foreign-born population.