Questions about people’s perceptions of politicians or other political actors are of central interest in a wide variety of research areas. But measuring these perceptions is difficult in part because respondents may use survey response scales in different ways. In a classic article, Aldrich and McKelvey (1977) introduce a method adjusting for such differential item functioning by assuming that all respondents perceive political stimuli identically. I propose a modeling approach built on the Aldrich and McKelvey framework but incorporating anchoring vignettes. This approach allows for scale use adjustments without assuming that all respondents perceive a given politician identically. I apply this model to data on Americans’ perceptions of parties, elected officials, and other political actors, showing that, contrary to previous arguments, most variation in ideology ratings is due not to differing scale use, but to differences in underlying perceptions. Specifically, while perceptions of Republican politicians and the Republican party show no significant differences by respondent partisanship, Democratic and Republican respondents differ strongly in their perceptions of the ideology of Democratic political actors as well as the Supreme Court.

Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic and tone. Here, we provide a guide to help researchers navigate the consequential decisions they need to make before any measure can be produced from the text. We consider, both theoretically and empirically, the effects of such choices using as a running example efforts to measure the tone of New York Times coverage of the economy. We show that two reasonable approaches to corpus selection yield radically different corpora and we advocate for the use of keyword searches rather than predefined subject categories provided by news archives. We demonstrate the benefits of coding using article segments instead of sentences as units of analysis. We show that, given a fixed number of codings, it is better to increase the number of unique documents coded rather than the number of coders for each document. Finally, we find that supervised machine learning algorithms outperform dictionaries on a number of criteria. Overall, we intend this guide to serve as a reminder to analysts that thoughtfulness and human validation are key to text-as-data methods, particularly in an age when it is all too easy to computationally classify texts without attending to the methodological choices therein.

Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic parse trees to identify legally relevant provisions, as well as agents and delegated actions. We undertake two applications. First, we produce a measure of bureaucratic discretion by looking at the level of legislative detail for US states and find that this measure increases after reforms giving agencies more independence. This effect is consistent with an agency cost model, where a more independent bureaucracy requires more specific instructions (less discretion) to avoid bureaucratic drift. Second, we construct measures of delegation to governors in state legislation. Consistent with previous estimates using non-text metrics, we find that executive delegation increases under unified government.

Given the increasing quantity and impressive placement of work on Bayesian process tracing, this approach has quickly become a frontier of qualitative research methods. Moreover, it has dominated the process-tracing modules at the Institute for Qualitative and Multi-Method Research (IQMR) and the American Political Science Association (APSA) meetings for over five years, rendering its impact even greater. Proponents of qualitative Bayesianism make a series of strong claims about its contributions and scope of inferential validity. Four claims stand out: (1) it enables causal inference from iterative research, (2) the sequence in which we evaluate evidence is irrelevant to inference, (3) it enables scholars to fully engage rival explanations, and (4) it prevents ad hoc hypothesizing and confirmation bias. Notwithstanding the stakes of these claims and breadth of traction this method has received, no one has systematically evaluated the promises, trade-offs, and limitations that accompany Bayesian process tracing. This article evaluates the extent to which the method lives up to the mission. Despite offering a useful framework for conducting iterative research, the current state of the method introduces more bias than it corrects for on numerous dimensions. The article concludes with an examination of the opportunity costs of learning Bayesian process tracing and a set of recommendations about how to push the field forward.

Conjoint experiments are popular, but there is a paucity of research on respondents’ underlying decision-making processes. We leverage eye-tracking methodology and a series of conjoint experiments, administered to university students and local community members, to examine how respondents process information in conjoint surveys. There are two main findings. First, attribute importance measures inferred from the stated choice data are correlated with attribute importance measures based on eye movement. This validation test supports the interpretation of common conjoint metrics, such as average marginal component effects (AMCEs), as measures of attribute importance. Second, when we experimentally increase the number of attributes and profiles in the conjoint table, respondents view a larger absolute number of cells but a smaller fraction of the total cells displayed. Moving from two to three profiles, respondents search more within-profile, rather than within-attribute, to build summary evaluations. However, respondents’ stated choices remain fairly stable regardless of the number of attributes and profiles in the conjoint table. Together, these patterns speak to the robustness of conjoint experiments and are consistent with a bounded rationality mechanism. Respondents adapt to complexity by selectively incorporating relevant new information to focus on important attributes, while ignoring less relevant information to reduce cognitive processing costs.

We model the conditional distribution of seats given vote shares induced by national electoral systems using a stochastic threshold of representation and a disproportionality parameter that regulates allocation for parties above the threshold. We establish conditions for the parameters of this model to be identified from observed seats/votes data, and develop a Maximum a Posteriori Expectation-Maximization (MAP-EM) algorithm to estimate them. We apply the procedure to 116 electoral systems used in 417 elections to the lower house across 36 European countries since WWII. We reject a test of model fit in only 5 of those systems, while a simpler model without thresholds is rejected in favor of our estimated model in 49 electoral systems. We find that the two modal electoral system configurations involve higher thresholds with seat allocation for parties exceeding thresholds that does not statistically differ from perfectly proportional allocation (32.76% of all systems); and systems for which we cannot reject the absence of a national threshold but exhibit disproportional seat allocation for parties eligible for seats (38.79% of all systems). We also develop procedures to test for significant changes in electoral institutions and/or the distribution of seats.