Research on the activities and influence of interest groups in state legislatures faces a data problem: we are missing a comprehensive, systematic dataset of interest groups’ policy preferences on state legislation. We address this gap by introducing the Dataset on Policy Choice and Organizational Representation in the United States (CHORUS). This dataset compiles over 13 million policy positions stated by tens of thousands of interest groups and individuals on bills in 17 state legislatures over the past 25 years. We describe the process used to construct CHORUS and present a new network science technique for analyzing policy position data from interest groups: the layered stochastic block model, which groups similar interest groups and bills together, respectively, based on patterns in the policy positions. Through two demonstrative applications, we show the utility of these data, combined with our novel analytical approach, for understanding interest group configurations in different state legislatures and policy areas.

The development of multilevel regression and poststratification (MRP) has allowed scholars to more accurately estimate subnational public opinion using national polls. However, MRP generally recovers less accurate estimates from polls whose respondents are selected using cluster sampling – also called area-probability sampling. This is in part because cluster-sampled polls rely on a complex form of random sampling focused on national representativeness that may result in small or unrepresentative subsamples in subnational geographies. This has limited MRP’s usefulness in subnational opinion estimation in several contexts, including historical polls in the US, where cluster-sampling was common into the 1980s, and large academic studies in many countries today. In this paper, I propose two approaches to improve estimation from MRP with cluster-sampled polls. The first is pooling data from multiple surveys to produce a larger sample of clusters. The second is clustered MRP (CMRP), which extends MRP by modeling opinion using the geographic information included in a survey’s cluster-sampling procedure. Using simulations, I show that both methods improve upon traditional MRP, and I validate them using historical polls in the US

The Squire Index measuring the professionalization levels of American state legislatures was created three decades ago. In this note, I explain how the 2021 update was compiled. I then document the index’s stability over the years for which it has been measured, present alternative measures to explore a problematic aspect of the index, and finally examine how each of the index’s three components has moved relative to Congress between 1979 and 2021.

We introduce a dynamic dataset of all communications by state election officials (EOs) on social media during the 2022 election cycle and develop metrics to assess the effectiveness of trust-building strategies on voter confidence. We employ quantitative manual content analysis of 10,000 organic posts from 118 state EOs’ accounts on Facebook, Instagram, and Twitter between September 10 and November 30, 2022, and code for the presence of variables that measure EOs’ efforts to combat misinformation and build trusted networks of communications. The measures we present here address two questions: (1) How much coordination was there among states in terms of incorporating the #TrustedInfo2022 campaign, promoted by the National Association of Secretaries of State, in their social media communications, and (2) How much of states’ social media communications explicitly signaled that EOs are trusted sources of information? We demonstrate the applicability of our data on research that evaluates the impact of trust-building campaigns on voter confidence in elections, which is grounded on theories of deliberative democracy and democratic listening.