How preregistration can help increase youth voter turnout

In the 2014 midterm elections, less than a quarter of those aged 18-29 voted, half the number of those who did who were aged 45-64. How can this poor level of youth turnout be addressed? In new research, John B. Holbein & D. Sunshine Hillygus examine the effects of preregistration laws in states like California and Florida, which allow those who are 16 or 17 to register before they are eligible to vote. By comparing the rates of those who preregister to vote with those who register traditionally, they find that preregistration can increase turnout by up to 13 percent, and that this effect is consistent for both Republicans and Democrats

Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples

Panel studies typically suffer from attrition, which reduces sample size and can result in biased inferences. It is impossible to know whether or not the attrition causes bias from the observed panel data alone. Refreshment samples - new, randomly sampled respondents given the questionnaire at the same time as a subsequent wave of the panel - offer information that can be used to diagnose and adjust for bias due to attrition. We review and bolster the case for the use of refreshment samples in panel studies. We include examples of both a fully Bayesian approach for analyzing the concatenated panel and refreshment data, and a multiple imputation approach for analyzing only the original panel. For the latter, we document a positive bias in the usual multiple imputation variance estimator. We present models appropriate for three waves and two refreshment samples, including nonterminal attrition. We illustrate the three-wave analysis using the 2007-2008 Associated Press-Yahoo! News Election Poll.Comment: Published in at http://dx.doi.org/10.1214/13-STS414 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Reporting guidelines for experimental research: A report from the experimental research section standards committee.

The standards committee of the Experimental Research section was charged with preparing a set of reporting guidelines for experimental research in political science. The committee defined its task as compiling a set of guidelines sufficient to enable the reader or reviewer to follow what the researcher had done and to assess the validity of the conclusions the researcher had drawn. Although the guidelines do request the reporting of some basic statistics, they do not attempt to weigh in on statistical controversies. Rather, they aim for something more modest but nevertheless crucial: to ensure that scholars clearly describe what it is they did at each step in their research and clearly report what their data show. In this paper, we discuss the rationale for reporting guidelines and the process used to formulate the specific guidelines we endorse. The guidelines themselves are included in Appendix 1

Replication Data for: "Campaign Effects and the Dynamics of Turnout Intention in Election 2000"

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Republication Data for "All the Best Polls Agree with Me"

Republication files for conditionally accepted manuscript

Replication data for: Semi-parametric Selection Models for Potentially Non-ignorable Attrition in Panel Studies with Refreshment Samples

Panel studies typically suffer from attrition. Ignoring the attrition can result in biased inferences if the missing data is systematically related to outcomes of interest. Unfortunately, panel data alone cannot inform the extent of bias due to attrition. Many panel studies also include refreshment samples, which are data collected from a random sample of new individuals during the later waves of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by non-ignorable attrition while reducing reliance on strong assumptions about the attrition process. We present a Bayesian approach to handle attrition in two wave panels with one refreshment sample and many categorical survey variables. The approach includes (i) an additive non-ignorable selection model for the attrition process, and (ii) a Dirichlet process mixture of multinomial distributions for the categorical survey variables. We present Markov chain Monte Carlo algorithms for sampling from the posterior distribution of model parameters and missing data. We apply the model to correct attrition bias in an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study