Publications

Background: Incomplete information on death certificates makes recorded cause of death data less useful for public health monitoring and planning. Certifying physicians sometimes list only the mode of death (and in particular, list heart failure) without indicating the underlying disease(s) that gave rise to the death. This can prevent valid epidemiologic comparisons across countries and over time. Methods and Results: We propose that coarsened exact matching be used to infer the underlying causes of death where only the mode of death is known; we focus on the case of heart failure in U.S., Mexican and Brazilian death records. Redistribution algorithms derived using this method assign the largest proportion of heart failure deaths to ischemic heart disease in all three countries (53%, 26% and 22%), with larger proportions assigned to hypertensive heart disease and diabetes in Mexico and Brazil (16% and 23% vs. 7% for hypertensive heart disease and 13% and 9% vs. 6% for diabetes). Reassigning these heart failure deaths increases US ischemic heart disease mortality rates by 6%.Conclusions: The frequency with which physicians list heart failure in the causal chain for various underlying causes of death allows for inference about how physicians use heart failure on the death certificate in different settings. This easy-to-use method has the potential to reduce bias and increase comparability in cause-of-death data, thereby improving the public health utility of death records. Key Words: vital statistics, heart failure, population health, mortality, epidemiology

We report the results of several randomized survey experiments designed to evaluate two intended improvements to anchoring vignettes, an increasingly common technique used to achieve interpersonal comparability in survey research.  This technique asks for respondent self-assessments followed by assessments of hypothetical people described in vignettes. Variation in assessments of the vignettes across respondents reveals interpersonal incomparability and allows researchers to make responses more comparable by rescaling them. Our experiments show, first, that switching the question order so that self-assessments follow the vignettes primes respondents to define the response scale in a common way.  In this case, priming is not a bias to avoid but a means of better communicating the question’s meaning.  We then demonstrate that combining vignettes and self-assessments in a single direct comparison induces inconsistent and less informative responses.  Since similar combined strategies are widely employed for related purposes, our results indicate that anchoring vignettes could reduce measurement error in many applications where they are not currently used.  Data for our experiments come from a national telephone survey and a separate on-line survey.

Classic (or "cumulative") case-control sampling designs do not admit inferences about quantities of interest other than risk ratios, and then only by making the rare events assumption. Probabilities, risk differences, and other quantities cannot be computed without knowledge of the population incidence fraction. Similarly, density (or "risk set") case-control sampling designs do not allow inferences about quantities other than the rate ratio. Rates, rate differences, cumulative rates, risks, and other quantities cannot be estimated unless auxiliary information about the underlying cohort such as the number of controls in each full risk set is available. Most scholars who have considered the issue recommend reporting more than just the relative risks and rates, but auxiliary population information needed to do this is not usually available. We address this problem by developing methods that allow valid inferences about all relevant quantities of interest from either type of case-control study when completely ignorant of or only partially knowledgeable about relevant auxiliary population information. This is a somewhat revised and extended version of Gary King and Langche Zeng. 2002. "Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies," Statistics in Medicine, 21: 1409-1427. You may also be interested in our related work in other fields, such as in international relations, Gary King and Langche Zeng. "Explaining Rare Events in International Relations," International Organization, 55, 3 (Spring, 2001): 693-715, and in political methodology, Gary King and Langche Zeng, "Logistic Regression in Rare Events Data," Political Analysis, Vol. 9, No. 2, (Spring, 2001): Pp. 137--63.

A program for analyzing most any feature of district-level legislative elections data, including prediction, evaluating redistricting plans, estimating counterfactual hypotheses (such as what would happen if a term-limitation amendment were imposed). This implements statistical procedures described in a series of journal articles and has been used during redistricting in many states by judges, partisans, governments, private citizens, and many others. The earlier version was winner of the APSA Research Software Award.

Track JudgeIt Changes

The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a given category. Unfortunately, even a method with a high percent of individual documents correctly classified can be hugely biased when estimating category proportions. By directly optimizing for this social science goal, we develop a method that gives approximately unbiased estimates of category proportions even when the optimal classifier performs poorly. We illustrate with diverse data sets, including the daily expressed opinions of thousands of people about the U.S. presidency. We also make available software that implements our methods and large corpora of text for further analysis.

This article led to the formation of Crimson Hexagon

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in these fields have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation. First, we build a multiple imputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we build nonignorable missingness models by enabling analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, since these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also made it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing strategies.

This program is designed to improve causal inference via a method of matching that is widely applicable in observational data and easy to understand and use (if you understand how to draw a histogram, you will understand this method). The program implements the coarsened exact matching (CEM) algorithm, described below. CEM may be used alone or in combination with any existing matching method. This algorithm, and its statistical properties, are described in Iacus, King, and Porro (2008).

This (two-page) article argues that the evidence base of political science and the related social sciences are beginning an underappreciated but historic change.

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

In response to the data-based measures of model dependence proposed in King and Zeng (2006), Sambanis and Michaelides (2008) propose alternative measures that rely upon assumptions untestable in observational data. If these assumptions are correct, then their measures are appropriate and ours, based solely on the empirical data, may be too conservative. If instead and as is usually the case, the researcher is not certain of the precise functional form of the data generating process, the distribution from which the data are drawn, and the applicability of these modeling assumptions to new counterfactuals, then the data-based measures proposed in King and Zeng (2006) are much preferred. After all, the point of model dependence checks is to verify empirically, rather than to stipulate by assumption, the effects of modeling assumptions on counterfactual inferences.

A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals–-such as households, communities, firms, medical practices, schools, or classrooms–-even when the individual is the unit of interest. To recoup the resulting efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, many other studies avoid pairing, in part because of claims in the literature, echoed by clinical trials standards organizations, that this matched-pair, cluster-randomization design has serious problems. We argue that all such claims are unfounded. We also prove that the estimator recommended for this design in the literature is unbiased only in situations when matching is unnecessary and and its standard error is also invalid. To overcome this problem without modeling assumptions, we develop a simple design-based estimator with much improved statistical properties. We also propose a model-based approach that includes some of the benefits of our design-based estimator as well as the estimator in the literature. Our methods also address individual-level noncompliance, which is common in applications but not allowed for in most existing methods. We show that from the perspective of bias, efficiency, power, robustness, or research costs, and in large or small samples, pairing should be used in cluster-randomized experiments whenever feasible and failing to do so is equivalent to discarding a considerable fraction of one’s data. We develop these techniques in the context of a randomized evaluation we are conducting of the Mexican Universal Health Insurance Program.

Social science data are an unusual part of the past, present, and future of digital preservation. They are both an unqualified success, due to long-lived and sustainable archival organizations, and in need of further development because not all digital content is being preserved. This article is about the Data Preservation Alliance for Social Sciences (Data-PASS), a project supported by the National Digital Information Infrastructure and Preservation Program (NDIIPP), which is a partnership of five major U.S. social science data archives. Broadly speaking, Data-PASS has the goal of ensuring that at-risk social science data are identified, acquired, and preserved, and that we have a future-oriented organization that could collaborate on those preservation tasks for the future. Throughout the life of the Data-PASS project we have worked to identify digital materials that have never been systematically archived, and to appraise and acquire them. As the project has progressed, however, it has increasingly turned its attention from identifying and acquiring legacy and at-risk social science data to identifying on going and future research projects that will produce data. This article is about the project's history, with an emphasis on the issues that underlay the transition from looking backward to looking forward.