Missing Data

Statistical methods to accommodate missing information in data sets due to scattered unit nonresponse, missing variables, or cell values or variables measured with error. Easy-to-use algorithms and software for multiple imputation and multiple overimputation for surveys, time series, and time series cross-sectional data. Applications to electoral, and other compositional, data.


Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
The methods developed in this paper greatly expands the size and types of data sets that can be imputed without difficulty, for cross-sectional, time series, and time series cross-sectional data.
Gary King, James Honaker, Anne Joseph, and Kenneth Scheve. 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review, 95: 49–69. Abstract

We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one’s explanatory and dependent variables than the methods currently used in applied data analysis. The discrepancy occurs because the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and have demanded considerable expertise. We adapt an algorithm and use it to implement a general-purpose, multiple imputation model for missing data. This algorithm is considerably easier to use than the leading method recommended in statistics literature. We also quantify the risks of current missing data practices, illustrate how to use the new procedure, and evaluate this alternative through simulated data as well as actual empirical examples. Finally, we offer easy-to-use that implements our suggested methods. (Software: AMELIA)

Not Asked and Not Answered: Multiple Imputation for Multiple Surveys
Develops multiple imputation methods for when entire survey questions are missing from some of a series of cross-sectional samples.
Andrew Gelman, Gary King, and Chuanhai Liu. 1999. “Not Asked and Not Answered: Multiple Imputation for Multiple Surveys.” Journal of the American Statistical Association, 93: 846–857. Abstract
We present a method of analyzing a series of independent cross-sectional surveys in which some questions are not answered in some surveys and some respondents do not answer some of the questions posed. The method is also applicable to a single survey in which different questions are asked or different sampling methods are used in different strata or clusters. Our method involves multiply imputing the missing items and questions by adding to existing methods of imputation designed for single surveys a hierarchical regression model that allows covariates at the individual and survey levels. Information from survey weights is exploited by including in the analysis the variables on which the weights are based, and then reweighting individual responses (observed and imputed) to estimate population quantities. We also develop diagnostics for checking the fit of the imputation model based on comparing imputed data to nonimputed data. We illustrate with the example that motivated this project: a study of pre-election public opinion polls in which not all the questions of interest are asked in all the surveys, so that it is infeasible to impute within each survey separately.
A Unified Approach to Measurement Error and Missing Data: Details and Extensions
Matthew Blackwell, James Honaker, and Gary King. 2015. “A Unified Approach to Measurement Error and Missing Data: Details and Extensions.” Sociological Methods and Research, 1-28. Publisher's Version Abstract

We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach’s robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.

A Unified Approach to Measurement Error and Missing Data: Overview and Applications
We extend the algorithm in the previous paper to encompass classic missing data as an extreme version of measurement error, and to correct for both.
Matthew Blackwell, James Honaker, and Gary King. 2015. “A Unified Approach to Measurement Error and Missing Data: Overview and Applications.” Sociological Methods and Research, 1-39. Publisher's Version Abstract

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extensions (Blackwell, Honaker, and King, 2014b).

What to do About Missing Values in Time Series Cross-Section Data
Multiple imputation for missing data had long been recognized as theoretical appropriate, but algorithms to use it were difficult, and applications were rare. This article introduced an easy-to-apply algorithm, making multiple imputation within reach of practicing social scientists. It, and the related software, has been widely used.
James Honaker and Gary King. 2010. “What to do About Missing Values in Time Series Cross-Section Data.” American Journal of Political Science, 3, 54: 561-581. Publisher's Version Abstract

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.

A Statistical Model for Multiparty Electoral Data
A general purpose method for analyzing multiparty electoral data.
Jonathan Katz and Gary King. 1999. “A Statistical Model for Multiparty Electoral Data.” American Political Science Review, 93: 15–32. Abstract
We propose a comprehensive statistical model for analyzing multiparty, district-level elections. This model, which provides a tool for comparative politics research analagous to that which regression analysis provides in the American two-party context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighborhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. We also provide new graphical representations for data exploration, model evaluation, and substantive interpretation. We illustrate the use of this model by attempting to resolve a controversy over the size of and trend in electoral advantage of incumbency in Britain. Contrary to previous analyses, all based on measures now known to be biased, we demonstrate that the advantage is small but meaningful, varies substantially across the parties, and is not growing. Finally, we show how to estimate the party from which each party’s advantage is predominantly drawn.
Uses the insights from the above two articles to greatly increase the number of parties that can be analyzed.
James Honaker, Gary King, and Jonathan N Katz. 2002. “A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data.” Political Analysis, 10: 84–100. Abstract
Katz and King (1999) develop a model for predicting or explaining aggregate electoral results in multiparty democracies. This model is, in principle, analogous to what least squares regression provides American politics researchers in that two-party system. Katz and King applied this model to three-party elections in England and revealed a variety of new features of incumbency advantage and where each party pulls support from. Although the mathematics of their statistical model covers any number of political parties, it is computationally very demanding, and hence slow and numerically imprecise, with more than three. The original goal of our work was to produce an approximate method that works quicker in practice with many parties without making too many theoretical compromises. As it turns out, the method we offer here improves on Katz and King’s (in bias, variance, numerical stability, and computational speed) even when the latter is computationally feasible. We also offer easy-to-use software that implements our suggestions.


Amelia II: A Program for Missing Data
James Honaker, Gary King, and Matthew Blackwell. 2011. “Amelia II: A Program for Missing Data.” Journal of Statistical Software, 7, 45: 1-47. Abstract

Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, thereby including a great deal of potentially valuable and extensive information. It also includes features to accurately impute cross-sectional datasets, individual time series, or sets of time series for different cross-sections. A full set of graphical diagnostics are also available. The program is easy to use, and the simplicity of the algorithm makes it far more robust; both a simple command line and extensive graphical user interface are included.

Amelia II software web site

AMELIA II: A Program for Missing Data
James Honaker, Gary King, and Matthew Blackwell. 2009. “AMELIA II: A Program for Missing Data”. Publisher's Version Abstract
This program multiply imputes missing data in cross-sectional, time series, and time series cross-sectional data sets. It includes a Windows version (no knowledge of R required), and a version that works with R either from the command line or via a GUI.
Michael Tomz, Jason Wittenberg, and Gary King. 2003. “CLARIFY: Software for Interpreting and Presenting Statistical Results.” Journal of Statistical Software. Abstract
, which easily combines multiply imputed data in Stata.
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 http://gking.harvard.edu/clarify" at the Stata command line. The documentation [ HTML | PDF ] explains how to do this. We also provide a zip archive for users who want to install Clarify on a computer that is not connected to the internet. 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.

How Surveys Work

Pre-Election Survey Methodology: Details From Nine Polling Organizations, 1988 and 1992
D. Steven Voss, Andrew Gelman, and Gary King. 1995. “Pre-Election Survey Methodology: Details From Nine Polling Organizations, 1988 and 1992.” Public Opinion Quarterly, 59: 98–132. Abstract

Before every presidential election, journalists, pollsters, and politicians commission dozens of public opinion polls. Although the primary function of these surveys is to forecast the election winners, they also generate a wealth of political data valuable even after the election. These preelection polls are useful because they are conducted with such frequency that they allow researchers to study change in estimates of voter opinion within very narrow time increments (Gelman and King 1993). Additionally, so many are conducted that the cumulative sample size of these polls is large enough to construct aggregate measures of public opinion within small demographic or geographical groupings (Wright, Erikson, and McIver 1985).