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"Multidimensional Democracy examines political representation from the supply (legislator) and demand (constituent) perspectives. Focusing on four dimensions - policy, service, allocation, and descriptive representation - it documents systematic variation in what people want from legislators and what legislators choose to emphasize while in office. It has important implications for the study of representation, as well as normative questions about political inequality in America. The demand-side results show that constituents who are economically advantaged tend to prefer policy-based representation while the disadvantaged place relatively more importance in constituent service and/or allocation. Suggestive results from the legislator data complement this finding; legislators in wealthy, white districts tend to focus more on policy while those representing economically disadvantaged and racially diverse districts may place more emphasis on service and/or allocation. A likely consequence is that the policy choices made by representatives reflect the policy preferences of the economically advantaged because policy representation is what those citizens want"..

American politics scholars typically conceptualize representation narrowly as mass‐elite policy responsiveness, with many studies identifying factors that hinder that relationship. These findings contrast with the high reelection rates in American legislatures. I show that policy is only one of several dimensions through which legislators provide representation. I unify policy, service, allocation, and descriptive representation in a model of legislators' priorities and then test it with survey experiments administered to 1,175 state legislators. I posit that legislators systematically emphasize different dimensions to further the goal of reelection. Results show that legislative institutions, district demand, and individual traits structure legislators' strategic representational priorities.

Political science data often contain grouped observations, which produces unobserved "cluster effects" in statistical models. Typical solutions include (1) ignoring the impact on coefficients and only adjusting the standard errors of generalized linear models (GLM) or (2) addressing clustering in coefficient estimation while relying on a parametric assumption for the cluster effects and/or a large number of clusters for standard errors. I show that both approaches are problematic for inference. Through simulation I demonstrate that multilevel modeling (MLM) and generalized estimating equations (GEE) produce more efficient coefficients than does GLM. Next, I show that commonly-used MLM and GEE standard error methods can be biased downward, while bootstrapping by resampling clusters (BCSE) performs better, even with a misspecified error distribution and/or few clusters. I recommend the use of MLM or GEE to estimate coefficients and BCSE to estimate uncertainty, and show that this approach can produce divergent conclusions in applied research.

AbstractU.S. state politics researchers often analyze data with observations grouped into clusters. This structure commonly produces unmodeled correlation within clusters, leading to downward bias in the standard errors of regression coefficients. Estimating robust cluster standard errors (RCSE) is a common approach to correcting this bias. However, despite their frequent use, recent work indicates that RCSE can also be biased downward. Here the author provides evidence of that bias and offers a potential solution. Through Monte Carlo simulation of an ordinary least squares (OLS) regression model, the author compares conventional standard error (OLS-SE) and RCSE performance to that of a bootstrap method that resamples clusters of observations (BCSE). The author shows that both OLS-SEandRCSE are biased downward, with OLS-SE being the most biased. In contrast, BCSE are not biased and consistently outperform the other two methods. The author concludes with three replications from recent work and offers recommendations to researchers.

ABSTRACTWhat information can I trust? What sources should I include in my paper? Where can I find a quote that fits my argument? Undergraduates ask instructors, classmates, and/or librarians these questions. Meanwhile, instructors bemoan the gap between their expectations for student writing and the finished products. Navigating a large volume of scholarship and critically evaluating potential sources is straightforward for faculty who have long passed key information literacy (IL) thresholds. However, students usually have not reached these thresholds themselves. We offer practical tools—grounded in a new framework for teaching IL—to address these challenges. We demonstrate how instructors can (and should) teach IL skills, with or without direct assistance from librarians. We recommend encouraging students to build context around information sources and slow down as they search. Implementing these tools moves students from passively synthesizing a limited set of (possibly biased) materials to engaging in genuine scholarly inquiry.

The Cox proportional hazards model is a popular method for duration analysis that is frequently the subject of simulation studies. However, no standard method exists for simulating durations directly from its data generating process because it does not assume a distributional form for the baseline hazard function. Instead, simulation studies typically rely on parametric survival distributions, which contradicts the primary motivation for employing the Cox model. We propose a method that generates a baseline hazard function at random by fitting a cubic spline to randomly drawn points. Durations drawn from this function match the Cox model's inherent flexibility and improve the simulation's generalizability. The method can be extended to include time-varying covariates and non-proportional hazards.

The Cox proportional hazards model is a commonly used method for duration analysis in political science. Typical quantities of interest used to communicate results come from the hazard function (for example, hazard ratios or percentage changes in the hazard rate). These quantities are substantively vague, difficult for many audiences to understand and incongruent with researchers' substantive focus on duration. We propose methods for computing expected durations and marginal changes in duration for a specified change in a covariate from the Cox model. These duration-based quantities closely match researchers' theoretical interests and are easily understood by most readers. We demonstrate the substantive improvements in interpretation of Cox model results afforded by the methods with reanalyses of articles from three subfields of political science.