GARY KING (original) (raw)
Recent Papers
. 7/18/2024. “Automated Cognitive Debriefing.” In Society for Political Methodology. Riverside, CA.Abstract
Cognitive debriefing: necessary for researchers & respondents to agree on question meaning but prohibitively expensive, so rarely used.
- Administer survey, then go back & discuss what respondent thinks each question means.
- Universally-recommended best practice.
Our goal: easily & drastically improve question wording through an automated cognitive debriefing tool (ACD tool).
Zachary J. Ward, Rifat Atun, Gary King, Brenda Sequeira Dmello, and Sue J. Goldie. 6/2024. “Global maternal mortality projections by urban/rural locationand education level: a simulation-based analysis.” eClinicalMedicine, 72, Pp. 1-12. Publisher's VersionAbstract
Background
Maternal mortality remains a challenge in global health, with well-known disparities across countries. However, less is known about disparities in maternal health by subgroups within countries. The aim of this study is to estimate maternal health indicators for subgroups of women within each country.
Methods
In this simulation-based analysis, we used the empirically calibrated Global Maternal Health (GMatH) microsimulation model to estimate a range of maternal health indicators by subgroup (urban/rural location and level of education) for 200 countries/territories from 1990 to 2050. Education levels were defined as low (less than primary), middle (less than secondary), and high (completed secondary or higher). The model simulates the reproductive lifecycle of each woman, accounting for individual-level factors such as family planning preferences, biological factors (e.g., anemia), and history of maternal complications, and how these factors vary by subgroup. We also estimated the impact of scaling up women's education on projected maternal health outcomes compared to clinical and health system-focused interventions.
Findings
We find large subgroup differences in maternal health outcomes, with an estimated global maternal mortality ratio (MMR) in 2022 of 292 (95% UI 250–341) for rural women and 100 (95% UI 84–116) for urban women, and 536 (95% UI 450–594), 143 (95% UI 117–174), and 85 (95% UI 67–108) for low, middle, and high education levels, respectively. Ensuring all women complete secondary school is associated with a large impact on the projected global MMR in 2030 (97 [95% UI 76–120]) compared to current trends (167 [95% UI 142–188]), with especially large improvements in countries such as Afghanistan, Chad, Madagascar, Niger, and Yemen.
Interpretation
Substantial subgroup disparities present a challenge for global maternal health and health equity. Outcomes are especially poor for rural women with low education, highlighting the need to ensure that policy interventions adequately address barriers to care in rural areas, and the importance of investing in social determinants of health, such as women's education, in addition to health system interventions to improve maternal health for all women.
Natalie Ayers, Gary King, Zagreb Mukerjee, and Dominic Skinnion. Working Paper. “Statistical Intuition Without Coding (or Teachers)”.Abstract
Two features of quantitative political methodology make teaching and learning especially difficult: (1) Each new concept in probability, statistics, and inference builds on all previous (and sometimes all other relevant) concepts; and (2) motivating substantively oriented students, by teaching these abstract theories simultaneously with the practical details of a statistical programming language (such as R), makes learning each subject harder. We address both problems through a new type of automated teaching tool that helps students see the big theoretical picture and all its separate parts at the same time without having to simultaneously learn to program. This tool, which we make available via one click in a web browser, can be used in a traditional methods class, but is also designed to work without instructor supervision.
Danny Ebanks, Jonathan N. Katz, and Gary King. Working Paper. “How American Politics Ensures Electoral Accountability in Congress”.Abstract
An essential component of democracy is the ability to hold legislators accountable via the threat of electoral defeat, a concept that has rarely been quantified directly. Well known massive changes over time in indirect measures — such as incumbency advantage, electoral margins, partisan bias, partisan advantage, split-ticket voting, and others — all seem to imply wide swings in electoral accountability. In contrast, we show that the (precisely calibrated) probability of defeating incumbent US House members has been surprisingly constant and remarkably high for two-thirds of a century. We resolve this paradox with a generative statistical model of the full vote distribution to avoid biases induced by the common practice of studying only central tendencies, and validate it with extensive out-of-sample tests. We show that different states of the partisan battlefield lead in interestingly different ways to the same high probability of incumbent defeat. Many challenges to American democracy remain, but this core feature remains durable.
Zachary J. Ward, Rifat Atun, Gary King, Brenda Sequeira Dmello, and Sue J. Goldie. 4/20/2023. “A simulation-based comparative effectiveness analysis of policies to improve global maternal health outcomes.” Nature Medicne. Publisher's VersionAbstract
The Sustainable Development Goals include a target to reduce the global maternal mortality ratio (MMR) to less than 70 maternal deaths per 100,000 live births by 2030, with no individual country exceeding 140. However, on current trends the goals are unlikely to be met. We used the empirically calibrated Global Maternal Health microsimulation model, which simulates individual women in 200 countries and territories to evaluate the impact of different interventions and strategies from 2022 to 2030. Although individual interventions yielded fairly small reductions in maternal mortality, integrated strategies were more effective. A strategy to simultaneously increase facility births, improve the availability of clinical services and quality of care at facilities, and improve linkages to care would yield a projected global MMR of 72 (95% uncertainty interval (UI) = 58–87) in 2030. A comprehensive strategy adding family planning and community-based interventions would have an even larger impact, with a projected MMR of 58 (95% UI = 46–70). Although integrated strategies consisting of multiple interventions will probably be needed to achieve substantial reductions in maternal mortality, the relative priority of different interventions varies by setting. Our regional and country-level estimates can help guide priority setting in specific contexts to accelerate improvements in maternal health.
Zachary J. Ward, Rifat Atun, Gary King, Brenda Sequeira Dmello, and Sue J. Goldie. 4/20/2023. “Simulation-based estimates and projections of global, regional and country-level maternal mortality by cause, 1990–2050.” Nature Medicine. Publisher's VersionAbstract
Maternal mortality is a major global health challenge. Although progress has been made globally in reducing maternal deaths, measurement remains challenging given the many causes and frequent underreporting of maternal deaths. We developed the Global Maternal Health microsimulation model for women in 200 countries and territories, accounting for individual fertility preferences and clinical histories. Demographic, epidemiologic, clinical and health system data were synthesized from multiple sources, including the medical literature, Civil Registration Vital Statistics systems and Demographic and Health Survey data. We calibrated the model to empirical data from 1990 to 2015 and assessed the predictive accuracy of our model using indicators from 2016 to 2020. We projected maternal health indicators from 1990 to 2050 for each country and estimate that between 1990 and 2020 annual global maternal deaths declined by over 40% from 587,500 (95% uncertainty intervals (UI) 520,600–714,000) to 337,600 (95% UI 307,900–364,100), and are projected to decrease to 327,400 (95% UI 287,800–360,700) in 2030 and 320,200 (95% UI 267,100–374,600) in 2050. The global maternal mortality ratio is projected to decline to 167 (95% UI 142–188) in 2030, with 58 countries above 140, suggesting that on current trends, maternal mortality Sustainable Development Goal targets are unlikely to be met. Building on the development of our structural model, future research can identify context-specific policy interventions that could allow countries to accelerate reductions in maternal deaths.
Katherine Clayton, Yusaku Horiuchi, Aaron R. Kaufman, Gary King, and Mayya Komisarchik. Working Paper. “Correcting Measurement Error Bias in Conjoint Survey Experiments”.Abstract
Conjoint survey designs are spreading across the social sciences due to their unusual capacity to estimate many causal effects from a single randomized experiment. Unfortunately, by their ability to mirror complicated real-world choices, these designs often generate substantial measurement error and thus bias. We replicate both the data collection and analysis from eight prominent conjoint studies, all of which closely reproduce published results, and show that a large proportion of observed variation in answers to conjoint questions is effectively random noise. We then discover a common empirical pattern in how measurement error appears in conjoint studies and, with it, introduce an easy-to-use statistical method to correct the bias.
You may be interested in software (in progress) that implements all the suggestions in our paper: "Projoint: The One-Stop Conjoint Shop".
Presentations
Is Survey Instability Due to Respondents who Don't Understand Politics or Researchers Who Don't Understand Respondents? (Caltech), at California Institute of Technology, Wednesday, March 13, 2024:
For over 75 years, survey researchers have observed disturbingly large proportions of respondents changing answers when asked the same question again later, even if no material changes have taken place. This “survey instability” is central to substantive debates in many scholarly fields and, more generally, for choosing the data generation process underlying all survey data analysis methods. By building on developments in neuroscience, cognitive psychology, and statistical measurement, we construct an encompassing model of the survey response, narrow competing hypotheses to a single data... Read more about Is Survey Instability Due to Respondents who Don't Understand Politics or Researchers Who Don't Understand Respondents? (Caltech)
Books
Gary King, Robert O. Keohane, and Sidney Verba. 2021. Designing Social Inquiry: Scientific Inference in Qualitative Research, New Edition. 2nd ed. Princeton: Princeton University Press. Publisher's VersionAbstract
"The classic work on qualitative methods in political science"
Designing Social Inquiry presents a unified approach to qualitative and quantitative research in political science, showing how the same logic of inference underlies both. This stimulating book discusses issues related to framing research questions, measuring the accuracy of data and the uncertainty of empirical inferences, discovering causal effects, and getting the most out of qualitative research. It addresses topics such as interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. The book only uses mathematical notation to clarify concepts, and assumes no prior knowledge of mathematics or statistics.
Featuring a new preface by Robert O. Keohane and Gary King, this edition makes an influential work available to new generations of qualitative researchers in the social sciences.
Replication data at the Harvard Dataverse: https://doi.org/10.7910/DVN/YHZG5M.
Gary King, Kay Schlozman, and Norman Nie. 2009. The Future of Political Science: 100 Perspectives. New York: Routledge Press.Read more
Federico Girosi and Gary King. 2008. Demographic Forecasting. Princeton: Princeton University Press.Abstract
We introduce a new framework for forecasting age-sex-country-cause-specific mortality rates that incorporates considerably more information, and thus has the potential to forecast much better, than any existing approach. Mortality forecasts are used in a wide variety of academic fields, and for global and national health policy making, medical and pharmaceutical research, and social security and retirement planning.
As it turns out, the tools we developed in pursuit of this goal also have broader statistical implications, in addition to their use for forecasting mortality or other variables with similar statistical properties. First, our methods make it possible to include different explanatory variables in a time series regression for each cross-section, while still borrowing strength from one regression to improve the estimation of all. Second, we show that many existing Bayesian (hierarchical and spatial) models with explanatory variables use prior densities that incorrectly formalize prior knowledge. Many demographers and public health researchers have fortuitously avoided this problem so prevalent in other fields by using prior knowledge only as an ex post check on empirical results, but this approach excludes considerable information from their models. We show how to incorporate this demographic knowledge into a model in a statistically appropriate way. Finally, we develop a set of tools useful for developing models with Bayesian priors in the presence of partial prior ignorance. This approach also provides many of the attractive features claimed by the empirical Bayes approach, but fully within the standard Bayesian theory of inference.
Replication data at the Harvard Dataverse: https://doi.org/10.7910/DVN/ZVN8XQ.
Gary King, Ori Rosen, Martin Tanner, Gary King, Ori Rosen, and Martin A Tanner. 2004. Ecological Inference: New Methodological Strategies. New York: Cambridge University Press.Abstract
Ecological Inference: New Methodological Strategies brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half decade has witnessed an explosion of research in ecological inference – the attempt to infer individual behavior from aggregate data. The uncertainties and the information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but such inferences are required in many academic fields, as well as by legislatures and the courts in redistricting, by businesses in marketing research, and by governments in policy analysis.