Writings

2004

Gary King, Christopher J.L. Murray, Joshua A. Salomon, and Ajay Tandon. 2004. “Enhancing the Validity and Cross-cultural Comparability of Measurement in Survey Research.” American Political Science Review, 98, Pp. 191–207.Abstract

We address two long-standing survey research problems: measuring complicated concepts, such as political freedom or efficacy, that researchers define best with reference to examples and and what to do when respondents interpret identical questions in different ways. Scholars have long addressed these problems with approaches to reduce incomparability, such as writing more concrete questions – with uneven success. Our alternative is to measure directly response category incomparability and to correct for it. We measure incomparability via respondents’ assessments, on the same scale as the self-assessments to be corrected, of hypothetical individuals described in short vignettes. Since actual levels of the vignettes are invariant over respondents, variability in vignette answers reveals incomparability. Our corrections require either simple recodes or a statistical model designed to save survey administration costs. With analysis, simulations, and cross-national surveys, we show how response incomparability can drastically mislead survey researchers and how our approach can fix them.

Article

Gary King. 2004. “Finding New Information for Ecological Inference Models: A Comment on Jon Wakefield, 'Ecological Inference in 2X2 Tables'.” Journal of the Royal Statistical Society, 167, Pp. 437.

Gary King, Ori Rosen, and Martin Tanner. 2004. “Information in Ecological Inference: An Introduction.” In Ecological Inference: New Methodological Strategies, edited by Gary King, Ori Rosen, and Martin Tanner. New York: Cambridge University Press.

Chapter PDF

Jeff Gill and Gary King. 2004. “Schnabel/Eskow Cholesky Factorization”.

Nathaniel Beck, Gary King, and Langche Zeng. 2004. “Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski,” 98, Pp. 379-389.Abstract

We thank Scott de Marchi, Christopher Gelpi, and Jeffrey Grynaviski (2003 and hereinafter dGG) for their careful attention to our work (Beck, King, and Zeng, 2000 and hereinafter BKZ) and for raising some important methodological issues that we agree deserve readers’ attention. We are pleased that dGG’s analyses are consistent with the theoretical conjecture about international conflict put forward in BKZ –- "The causes of conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large stable and replicable whenever the ex ante probability of conflict is large" (BKZ, p.21) –- and that dGG agree with our main methodological point that out-of-sample forecasting performance should always be one of the standards used to judge studies of international conflict, and indeed most other areas of political science. However, dGG frequently err when they draw methodological conclusions. Their central claim involves the superiority of logit over neural network models for international conflict data, as judged by forecasting performance and other properties such as ease of use and interpretation ("neural networks hold few unambiguous advantages... and carry significant costs" relative to logit and dGG, p.14). We show here that this claim, which would be regarded as stunning in any of the diverse fields in which both methods are more commonly used, is false. We also show that dGG’s methodological errors and the restrictive model they favor cause them to miss and mischaracterize crucial patterns in the causes of international conflict. We begin in the next section by summarizing the growing support for our conjecture about international conflict. The second section discusses the theoretical reasons why neural networks dominate logistic regression, correcting a number of methodological errors. The third section then demonstrates empirically, in the same data as used in BKZ and dGG, that neural networks substantially outperform dGG’s logit model. We show that neural networks improve on the forecasts from logit as much as logit improves on a model with no theoretical variables. We also show how dGG’s logit analysis assumed, rather than estimated, the answer to the central question about the literature’s most important finding, the effect of democracy on war. Since this and other substantive assumptions underlying their logit model are wrong, their substantive conclusion about the democratic peace is also wrong. The neural network models we used in BKZ not only avoid these difficulties, but they, or one of the other methods available that do not make highly restrictive assumptions about the exact functional form, are just what is called for to study the observable implications of our conjecture.

Article

Jeff Gill and Gary King. 2004. “What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation.” Sociological Methods and Research, 32, Pp. 54-87.Abstract

What should a researcher do when statistical analysis software terminates before completion with a message that the Hessian is not invertable? The standard textbook advice is to respecify the model, but this is another way of saying that the researcher should change the question being asked. Obviously, however, computer programs should not be in the business of deciding what questions are worthy of study. Although noninvertable Hessians are sometimes signals of poorly posed questions, nonsensical models, or inappropriate estimators, they also frequently occur when information about the quantities of interest exists in the data, through the likelihood function. We explain the problem in some detail and lay out two preliminary proposals for ways of dealing with noninvertable Hessians without changing the question asked.

Article

Frederico Girosi and Gary King. 2004. “YourCast”.Abstract

YourCast is (open source and free) software that makes forecasts by running sets of linear regressions together in a variety of sophisticated ways. YourCast avoids the bias that results when stacking datasets from separate cross-sections and assuming constant parameters, and the inefficiency that results from running independent regressions in each cross-section.

2003

Gary King. 2003. “10 Million International Dyadic Events”. Publisher's Version

Christopher Adolph and Gary King. 2003. “Analyzing Second Stage Ecological Regressions.” Political Analysis, 11, Pp. 65-76.

Article

Gary King and Will Lowe. 2003. “An Automated Information Extraction Tool For International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design.” International Organization, 57, Pp. 617-642.Abstract

Despite widespread recognition that aggregated summary statistics on international conflict and cooperation miss most of the complex interactions among nations, the vast majority of scholars continue to employ annual, quarterly, or occasionally monthly observations. Daily events data, coded from some of the huge volume of news stories produced by journalists, have not been used much for the last two decades. We offer some reason to change this practice, which we feel should lead to considerably increased use of these data. We address advances in event categorization schemes and software programs that automatically produce data by "reading" news stories without human coders. We design a method that makes it feasible for the first time to evaluate these programs when they are applied in areas with the particular characteristics of international conflict and cooperation data, namely event categories with highly unequal prevalences, and where rare events (such as highly conflictual actions) are of special interest. We use this rare events design to evaluate one existing program, and find it to be as good as trained human coders, but obviously far less expensive to use. For large scale data collections, the program dominates human coding. Our new evaluative method should be of use in international relations, as well as more generally in the field of computational linguistics, for evaluating other automated information extraction tools. We believe that the data created by programs similar to the one we evaluated should see dramatically increased use in international relations research. To facilitate this process, we are releasing with this article data on 4.3 million international events, covering the entire world for the last decade.

Article

Lee Epstein and Gary King. 2003. “Building An Infrastructure for Empirical Research in the Law.” Journal of Legal Education, 53, Pp. 311–320.Abstract

In every discipline in which "empirical research" has become commonplace, scholars have formed a subfield devoted to solving the methodological problems unique to that discipline’s data and theoretical questions. Although students of economics, political science, psychology, sociology, business, education, medicine, public health, and so on primarily focus on specific substantive questions, they cannot wait for those in other fields to solve their methoodological problems or to teach them "new" methods, wherever they were initially developed. In "The Rules of Inference," we argued for the creation of an analogous methodological subfield devoted to legal scholarship. We also had two other objectives: (1) to adapt the rules of inference used in the natural and social sciences, which apply equally to quantitative and qualitative research, to the special needs, theories, and data in legal scholarship, and (2) to offer recommendations on how the infrastructure of teaching and research at law schools might be reorganized so that it could better support the creation of first-rate quantitative and qualitative empirical research without compromising other important objectives. Published commentaries on our paper, along with citations to it, have focused largely on the first-our application of the rules of inference to legal scholarship. Until now, discussions of our second goal-suggestions for the improvement of legal scholarship, as well as our argument for the creation of a group that would focus on methodological problems unique to law-have been relegated to less public forums, even though, judging from the volume of correspondence we have received, they seem to be no less extensive.

Article

Michael Tomz, Jason Wittenberg, and Gary King. 2003. “CLARIFY: Software for Interpreting and Presenting Statistical Results.” Journal of Statistical Software.Abstract

This is a set of easy-to-use Stata macros that implement the techniques described in Gary King, Michael Tomz, and Jason Wittenberg's "Making the Most of Statistical Analyses: Improving Interpretation and Presentation". To install Clarify, type "net from https://gking.harvard.edu/clarify (https://gking.harvard.edu/clarify)" at the Stata command line.

Winner of the Okidata Best Research Software Award. Also try -ssc install qsim- to install a wrapper, donated by Fred Wolfe, to automate Clarify's simulation of dummy variables.

URL: https://scholar.harvard.edu/gking/clarify

Christopher Adolph, Gary King, Kenneth W Shotts, and Michael C Herron. 2003. “A Consensus on Second Stage Analyses in Ecological Inference Models.” Political Analysis, 11, Pp. 86–94.Abstract

Since Herron and Shotts (2003a and hereinafter HS), Adolph and King (2003 andhereinafter AK), and Herron and Shotts (2003b and hereinafter HS2), the four of us have iterated many more times, learned a great deal, and arrived at a consensus on this issue. This paper describes our joint recommendations for how to run second-stage ecological regressions, and provides detailed analyses to back up our claims.

Article

Emmanuela Gakidou and Gary King. 2003. “Determinants of Inequality in Child Survival: Results from 39 Countries.” In Health Systems Performance Assessment: Debates, Methods and Empiricism, edited by Christopher J.L. Murray and David B. Evans, Pp. 497-502. Geneva: World Health Organization.Abstract

Few would disagree that health policies and programmes ought to be based on valid, timely and relevant information, focused on those aspects of health development that are in greatest need of improvement. For example, vaccination programmes rely heavily on information on cases and deaths to document needs and to monitor progress on childhood illness and mortality. The same strong information basis is necessary for policies on health inequality. The reduction of health inequality is widely accepted as a key goal for societies, but any policy needs reliable research on the extent and causes of health inequality. Given that child deaths still constitute 19% of all deaths globally and 24% of all deaths in developing countries (1), reducing inequalities in child survival is a good beginning. total = between + within

The between-group component of total health inequality has been studied extensively by numerous scholars. They have expertly analysed the causes of differences in health status and mortality across population subgroups, defined by income, education, race/ethnicity, country, region, social class, and other group identifiers (2–9).

Chapter

Gary King. 2003. “EI: A Program for Ecological Inference”.

Gary King and Kenneth Benoit. 2003. “EzI: A(n Easy) Program for Ecological Inference”.Abstract

This software is no longer being actively updated. Previous versions and information about the software are archived here.

Gary King. 2003. “The Future of Replication.” International Studies Perspectives, 4, Pp. 443–499.Abstract

Since the replication standard was proposed for political science research, more journals have required or encouraged authors to make data available, and more authors have shared their data. The calls for continuing this trend are more persistent than ever, and the agreement among journal editors in this Symposium continues this trend. In this article, I offer a vision of a possible future of the replication movement. The plan is to implement this vision via the Virtual Data Center project, which – by automating the process of finding, sharing, archiving, subsetting, converting, analyzing, and distributing data – may greatly facilitate adherence to the replication standard.

Article

Jeff Gill and Gary King. 2003. “Numerical Issues Involved in Inverting Hessian Matrices.” In Numerical Issues in Statistical Computing for the Social Scientist, edited by Micah Altman and Michael P. McDonald, Pp. 143-176. Hoboken, NJ: John Wiley and Sons, Inc.

Chapter PDF

Michael Tomz, Gary King, and Langche Zeng. 2003. “ReLogit: Rare Events Logistic Regression.” Journal of Statistical Software, 8. Publisher's Version

Gary King, Michael Tomz, and Langche Zeng. 2003. “ReLogit: Rare Events Logistic Regression”.

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