One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here, we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information for temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the party landscape while reducing the risk of arbitrary classifications. To illustrate the potential contribution of this score, we use it as a proxy for parties’ levels of populism, analyzing average trends in six European countries from the early 2000s for nearly two decades.

In the literatures on public opinion and legislative behavior, there are debates over (1) how constrained preferences are and (2) whether they are captured by a single left–right spectrum or require multiple dimensions. But insufficient formalization has led scholars to equate a lack of constraint with multidimensional preferences. In this paper, we refine the concepts of constraint and dimensionality in a formal framework and describe how they translate into separate observable implications for political preferences. We use this discussion to motivate a cross-validation estimator that measures constraint and dimensionality in the context of canonical ideal point models. Using data from the public and politicians, we find that American political preferences are one-dimensional, but there is more constraint among politicians than among the mass public. Furthermore, we show that differences between politicians and the public are not explained by differences in agendas or the incentives faced by the actors.

Recent advances in the study of voting behavior and the study of legislatures have relied on ideal point estimation for measuring the preferences of political actors, and increasingly, these applications have involved very large data matrices. This has proved challenging for the widely available approaches. Limitations of existing methods include excessive computation time and excessive memory requirements on large datasets, the inability to efficiently deal with sparse data matrices, inefficient computation of standard errors, and ineffective methods for generating starting values. I develop an approach for estimating multidimensional ideal points in large-scale applications, which overcomes these limitations. I demonstrate my approach by applying it to a number of challenging problems. The methods I develop are implemented in an r package (ipe).

Political scientists are paying increasing attention to understanding the role of sexist attitudes on predicting vote choices and opinions on issues. However, the research in this area measures sexist attitudes with a variety of different items and scales. In this paper, I evaluate some of the most prominent contemporary measures of sexism and develop an approach for identifying optimal items based on (1) convergent validity, (2) predictive validity, and (3) distance from politics. I find that a subset of items from the hostile sexism scale exhibit the most desirable measurement properties and I conclude by recommending a simple two- to five-item reduced hostile sexism battery that will allow scholars to efficiently, validly, and consistently measure sexism.

To reduce strategic misreporting on sensitive topics, survey researchers increasingly use list experiments rather than direct questions. However, the complexity of list experiments may increase nonstrategic misreporting. We provide the first empirical assessment of this trade-off between strategic and nonstrategic misreporting. We field list experiments on election turnout in two different countries, collecting measures of respondents’ true turnout. We detail and apply a partition validation method which uses true scores to distinguish true and false positives and negatives for list experiments, thus allowing detection of nonstrategic reporting errors. For both list experiments, partition validation reveals nonstrategic misreporting that is: undetected by standard diagnostics or validation; greater than assumed in extant simulation studies; and severe enough that direct turnout questions subject to strategic misreporting exhibit lower overall reporting error. We discuss how our results can inform the choice between list experiment and direct question for other topics and survey contexts.

We introduce a fine-grained measure of the extent to which electoral districts combine and split local communities of co-partisans in unnatural ways. Our indicator—which we term Partisan Dislocation—is a measure of the difference between the partisan composition of a voter’s geographic nearest neighbors and that of her assigned district. We show that our measure is a good local and global indicator of district manipulation, easily identifying instances in which districts carve up clusters of co-partisans (cracking) or combine them in unnatural ways (packing). We demonstrate that our measure is related to but distinct from other approaches to the measurement of gerrymandering, and has some clear advantages, above all as a complement to simulation-based approaches, and as a way to identify the specific neighborhoods most affected by gerrymandering. It can also be used prospectively by district-drawers who wish to create maps that reflect voter geography, but according to our analysis, that goal will sometimes be in conflict with the goal of partisan fairness.

Political scientists routinely face the challenge of assessing the quality (validity and reliability) of measures in order to use them in substantive research. While stand-alone assessment tools exist, researchers rarely combine them comprehensively. Further, while a large literature informs data producers, data consumers lack guidance on how to assess existing measures for use in substantive research. We delineate a three-component practical approach to data quality assessment that integrates complementary multimethod tools to assess: (1) content validity; (2) the validity and reliability of the data generation process; and (3) convergent validity. We apply our quality assessment approach to the corruption measures from the Varieties of Democracy (V-Dem) project, both illustrating our rubric and unearthing several quality advantages and disadvantages of the V-Dem measures, compared to other existing measures of corruption.

Punctuated Equilibrium Theory posits that policy-making is generally characterized by long periods of stability that are interrupted by short periods of fundamental policy change. The literature converged on the measure of kurtosis and L-kurtosis to assess these change patterns. In this letter, we critically discuss these measures and propose the Gini coefficient as a (1) comparable, but (2) more intuitive, and (3) more precise measure of “punctuated” change patterns.

Racial identification is a critical factor in understanding a multitude of important outcomes in many fields. However, inferring an individual’s race from ecological data is prone to bias and error. This process was only recently improved via Bayesian improved surname geocoding (BISG). With surname and geographic-based demographic data, it is possible to more accurately estimate individual racial identification than ever before. However, the level of geography used in this process varies widely. Whereas some existing work makes use of geocoding to place individuals in precise census blocks, a substantial portion either skips geocoding altogether or relies on estimation using surname or county-level analyses. Presently, the trade-offs of such variation are unknown. In this letter, we quantify those trade-offs through a validation of BISG on Georgia’s voter file using both geocoded and nongeocoded processes and introduce a new level of geography—ZIP codes—to this method. We find that when estimating the racial identification of White and Black voters, nongeocoded ZIP code-based estimates are acceptable alternatives. However, census blocks provide the most accurate estimations when imputing racial identification for Asian and Hispanic voters. Our results document the most efficient means to sequentially conduct BISG analysis to maximize racial identification estimation while simultaneously minimizing data missingness and bias.