Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

In this article, we propose a polling accuracy measure for multi-party elections based on a generalization of Martin, Traugott, and Kennedy's two-party predictive accuracy index. Treating polls as random samples of a voting population, we first estimate an intercept only multinomial logit model to provide proportionate odds measures of each party's share of the vote, and thereby both unweighted and weighted averages of these values as a summary index for poll accuracy. We then propose measures for significance testing, and run a series of simulations to assess possible bias from the resulting folded normal distribution across different sample sizes, finding that bias is small even for polls with small samples. We apply our measure to the 2012 French presidential election polls to demonstrate its applicability in tracking overall polling performance across time and polling organizations. Finally, we demonstrate the practical value of our measure by using it as a dependent variable in an explanatory model of polling accuracy, testing the different possible sources of bias in the French data.

Latent variable models can only be compared across groups when these groups exhibit measurement equivalence or “invariance,” since otherwise substantive differences may be confounded with measurement differences. This article suggests examining directly whether measurement differences present could confound substantive analyses, by examining the expected parameter change (EPC)-interest. The EPC-interest approximates the change in parameters of interest that can be expected when freeing cross-group invariance restrictions. Monte Carlo simulations suggest that the EPC-interest approximates these changes well. Three empirical applications show that the EPC-interest can help avoid two undesirable situations: first, it can prevent unnecessarily concluding that groups are incomparable, and second, it alerts the user when comparisons of interest may still be invalidated even when the invariance model appears to fit the data. R code and data for the examples discussed in this article are provided in the electronic appendix (http://hdl.handle.net/1902.1/21816).

A state-dependent dynamic system is one in which (1) the marginal effect of x on y at time t () depends on the prior value of the dependent variable , and (2) the persistence of the dependent variable () depends on xt. We present a methodological strategy for dealing with state-dependent dynamic systems and demonstrate the consequences of ignoring state-dependence. As an applied example, we find evidence of state-dependence in the relationship between presidential approval and economic performance: high unemployment rates are most damaging to presidential approval among presidents with the highest initial approval ratings.

This article presents a new model for scoring alternatives from “contest” outcomes. The model is a generalization of the method of paired comparison to accommodate comparisons between arbitrarily sized sets of alternatives in which outcomes are any division of a fixed prize. Our approach is also applicable to contests between varying quantities of alternatives. We prove that under a reasonable condition on the comparability of alternatives, there exists a unique collection of scores that produces accurate estimates of the overall performance of each alternative and satisfies a well-known axiom regarding choice probabilities. We apply the method to several problems in which varying choice sets and continuous outcomes may create problems for standard scoring methods. These problems include measuring centrality in network data and the scoring of political candidates via a “feeling thermometer.” In the latter case, we also use the method to uncover and solve a potential difficulty with common methods of rescaling thermometer data to account for issues of interpersonal comparability.

This article provides an algorithm to produce a time-series estimate of the political center (or median voter) from aggregate survey data, even when the same questions are not asked in most years. This is compared to the existing Stimson dyad ratios approach, which has been applied to various questions in political science. Unlike the dyad ratios approach, the model developed here is derived from an explicit model of individual behavior—the widely used item response theory model. I compare the results of both techniques using the data on public opinion from the United Kingdom from 1947 to 2005 from Bartle, Dellepiane-Avellaneda, and Stimson. Measures of overall model fit are provided, as well as techniques for testing model's assumptions and the fit of individual items. Full code is provided for estimation with free software WinBUGS and JAGS.

Voting is a fundamental part of any democratic society. But survey-based measures of voting are problematic because a substantial proportion of nonvoters report that they voted. This over-reporting has consequences for our understanding of voting as well as the behaviors and attitudes associated with voting. Relying on the “bogus pipeline” approach, we investigate whether altering the wording of the turnout question can cause respondents to provide more accurate responses. We attempt to reduce over-reporting simply by changing the wording of the vote question by highlighting to the respondent that: (1) we can in fact find out, via public records, whether or not they voted; and (2) we (survey administrators) know some people who say they voted did not. We examine these questions through a survey on US voting-age citizens after the 2010 midterm elections, in which we ask them about voting in those elections. Our evidence shows that the question noting we would check the records improved the accuracy of the reports by reducing the over-reporting of turnout.