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AbstractPolitical scientists often find themselves analyzing data sets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in "big data," as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high‐dimensional data sets. In this article, we introduce a family of tree‐based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in data sets with many (potentially irrelevant) covariates. We introduce the basic logic of tree‐based models, provide an overview of the most prominent methods in the literature, and conduct three analyses that illustrate how the methods can be implemented while highlighting both their advantages and limitations.

Scholars are increasingly utilizing online workforces to encode latent political concepts embedded in written or spoken records. In this letter, we build on past efforts by developing and validating a crowdsourced pairwise comparison framework for encoding political texts that combines the human ability to understand natural language with the ability of computers to aggregate data into reliable measures while ameliorating concerns about the biases and unreliability of non-expert human coders. We validate the method with advertisements for U.S. Senate candidates and with State Department reports on human rights. The framework we present is very general, and we provide free software to help applied researchers interact easily with online workforces to extract meaningful measures from texts.

Prominent accounts of public opinion argue that citizens' preferences are unstable, with stated desires on policies varying wildly from survey to survey, and ideologically incoherent, with preferences on multiple policies evidencing little or no structure. In the aggregate, these findings suggest that many voters are not capable of fulfilling their normative role in the democratic system. In this article, we challenge this conventional view and argue that the apparent instability and incoherence among the public are both overstated and outdated. Using panel surveys from the 1970s, 1990s, and 2010s, we conduct a multi-trait multi-method (MTMM) confirmatory factor analysis of citizen preferences in multiple issue areas. Our results reveal a surprising degree of preference stability in all three time periods across many policy domains. Furthermore, our results reveal increasing levels of ideological thinking over time and that these patterns of stability and coherence hold across subpopulations defined by levels of sophistication.

Modern American political campaigns are typically conceptualized as 'candidate-centered' and treated as conditionally independent in quantitative analyses. In reality, however, these campaigns are linked by professional consulting firms, which are important agents of campaign strategy diffusion within the extended party networks of the contemporary era. To test our hypothesis that consultants disseminate campaign strategies among their clients, we analyze new data on U.S. House elections derived from Federal Election Commission records. Using spatial autoregressive models, we find that candidates who share consultants are more likely to use similar campaign strategies than we would otherwise expect, conditional on numerous explanatory variables. These results, which largely withstand an extensive series of robustness and falsification tests, suggest that consultants play a key role in diffusing strategies among congressional campaigns. Adapted from the source document.

Survey researchers avoid using large multi-item scales to measure latent traits due to both the financial costs and the risk of driving up nonresponse rates. Typically, investigators select a subset of available scale items rather than asking the full battery. Reduced batteries, however, can sharply reduce measurement precision and introduce bias. In this article, we present computerized adaptive testing (CAT) as a method for minimizing the number of questions each respondent must answer while preserving measurement accuracy and precision. CAT algorithms respond to individuals' previous answers to select subsequent questions that most efficiently reveal respondents' positions on a latent dimension. We introduce the basic stages of a CAT algorithm and present the details for one approach to item selection appropriate for public opinion research. We then demonstrate the advantages of CAT via simulation and empirically comparing dynamic and static measures of political knowledge.

Political science researchers typically conduct an idiosyncratic search of possible model configurations and then present a single specification to readers. This approach systematically understates the uncertainty of our results, generates fragile model specifications, and leads to the estimation of bloated models with too many control variables. Bayesian model averaging (BMA) offers a systematic method for analyzing specification uncertainty and checking the robustness of one's results to alternative model specifications, but it has not come into wide usage within the discipline. In this paper, we introduce important recent developments in BMA and show how they enable a different approach to using the technique in applied social science research. We illustrate the methodology by reanalyzing data from three recent studies using BMA software we have modified to respect statistical conventions within political science.

The goal of this Element is to provide a detailed introduction to adaptive inventories, an approach to making surveys adjust to respondents' answers dynamically. This method can help survey researchers measure important latent traits or attitudes accurately while minimizing the number of questions respondents must answer. The Element provides both a theoretical overview of the method and a suite of tools and tricks for integrating it into the normal survey process. It also provides practical advice and direction on how to calibrate, evaluate, and field adaptive batteries using example batteries that measure variety of latent traits of interest to survey researchers across the social sciences.

Abstract
One of the most difficult tasks facing survey researchers is balancing the imperative to keep surveys short with the need to measure important concepts accurately. Not only are long batteries prohibitively expensive but lengthy surveys can also lead to less informative answers from respondents. Yet, scholars often wish to measure traits that require a multi-item battery. To resolve these contradicting constraints, we propose the use of adaptive inventories. This approach uses computerized adaptive testing methods to minimize the number of questions each respondent must answer while maximizing the accuracy of the resulting measurement. We provide evidence supporting the utility of adaptive inventories through an empirically informed simulation study, an experimental study, and a detailed case study using data from the 2016 American National Election Study (ANES) Pilot. The simulation and experiment illustrate the superior performance of adaptive inventories relative to fixed-reduced batteries in terms of precision and accuracy. The ANES analysis serves as an illustration of how adaptive inventories can be developed and fielded and also validates an adaptive inventory with a nationally representative sample. Critically, we provide extensive software tools that allow researchers to incorporate adaptive inventories into their own surveys.

John Aldrich is a positive scientist—in both the scholastic and colloquial sense. A progeny of the Rochester school, Aldrich's research displays a commitment to the tenets of positive political theory. He derives internally consistent propositions and subjects those claims to empirical testing, all in an attempt to explain scientifically phenomena and institutions at the heart of democratic theory. As a mentor and builder of academic institutions, Aldrich has shown unswerving kindness, modesty, and a commitment to foster new generations of political scientists. He is a positive influence on his students, his colleagues, and the political science discipline at large.