Missing Data, Measurement Error, Differential Privacy
Statistical methods to accommodate missing information in data sets due to survey nonresponse, missing variables, or variables measured with error or with error added to protect privacy. Applications and software for analyzing electoral, compositional, survey, time series, and time series cross-sectional data.
Differential Privacy
Statistical methods for analyzing differentially private data
Statistically Valid Inferences from Privacy Protected Data.” American Political Science Review. Publisher's VersionAbstract
. Forthcoming. “
Differentially Private Survey Research.” American Journal of Political Science.Abstract
. Forthcoming. “
There’s a simple solution to the latest census fight.” Boston Globe, Pp. A9. Publisher's VersionAbstract
. 7/26/2021. “
Statistically Valid Inferences from Differentially Private Data Releases, with Application to the Facebook URLs Dataset.” Political Analysis, 31, 1, Pp. 1-21. Publisher's VersionAbstract
. 2023. “Missing Data, Measurement Error
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. . 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review, 95, Pp. 49–69.Abstract
Develops multiple imputation methods for when entire survey questions are missing from some of a series of cross-sectional samples. . 1999. “Not Asked and Not Answered: Multiple Imputation for Multiple Surveys.” Journal of the American Statistical Association, 93, Pp. 846–857.Abstract
A general purpose method for analyzing multiparty electoral data. . 1999. “A Statistical Model for Multiparty Electoral Data.” American Political Science Review, 93, Pp. 15–32.Abstract
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. . 2010. “What to do About Missing Values in Time Series Cross-Section Data.” American Journal of Political Science, 54, 3, Pp. 561-581. Publisher's VersionAbstract
A Unified Approach to Measurement Error and Missing Data: Details and Extensions.” Sociological Methods and Research, 46, 3, Pp. 342-369. Publisher's VersionAbstract
. 2017. “
Uses the insights from the above two articles to greatly increase the number of parties that can be analyzed. . 2002. “A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data.” Political Analysis, 10, Pp. 84–100.Abstract
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. . 2017. “A Unified Approach to Measurement Error and Missing Data: Overview and Applications.” Sociological Methods and Research, 46, 3, Pp. 303-341. Publisher's VersionAbstract
Software
- . 2011. “Amelia II: A Program for Missing Data.” Journal of Statistical Software, 45, 7, Pp. 1-47.Abstract
CLARIFY: Software for Interpreting and Presenting Statistical Results.” Journal of Statistical Software.Abstract , which easily combines multiply imputed data in Stata.
. 2003. “
Zelig: Everyone's Statistical Software”. , which easily combines multiply imputed data in R.
. 2006. “How Surveys Work
Pre-Election Survey Methodology: Details From Nine Polling Organizations, 1988 and 1992.” Public Opinion Quarterly, 59, Pp. 98–132.Abstract
. 1995. “