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Paper

Tests of the Validity of Complete-Unit Analysis in Surveys Subject to Item Nonresponse or Attrition Sherman, Robert P. Uploaded 03-12-1999 Keywords MCAR MAR item nonresponse attrition odds-ratios Abstract nalysts of cross-sectional or panel surveys often base inferences about relationships between variables on complete units, excluding units that are incomplete due to item nonresponse or attrition. This practice is justifiable if exclusion is ignorable in an appropriate sense. This paper characterizes certain types of ignorable exclusion in surveys subject to item nonresponse and develops tests based on these characterizations. These tests are applied to data from several National Election Study (NES) panels and evidence is found of violations of assumptions of ignorable exclusion. Characterizations and tests of ignorable attrition in standard panel surveys are also presented.

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Paper

Listwise Deletion is Evil: What to Do About Missing Data in Political Science King, Gary Honaker, James Joseph, Anne Scheve, Kenneth Uploaded 07-13-1998 Keywords missing data imputation IP EM EMs EMis data augmentation MCMC importance sampling item nonresponse Abstract We address a substantial discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing data problems based on the concept of ``multiple imputation,'' but most researchers in our field and other social sciences still use far inferior methods. Indeed, we demonstrate that the threats to validity from current missing data practices rival the biases from the much better known omitted variable problem. This discrepancy is not entirely our fault, as 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 demanding of considerable expertise on the part of the user (indeed, even experts disagree on how to use them). In this paper, we adapt an existing algorithm, and use it to implement a general-purpose, multiple imputation model for missing data. This algorithm is between 20 and 100 times faster than the leading method recommended in the statistics literature and is very easy to use. We also quantify the considerable risks of current political science missing data practices, illustrate how to use the new procedure, and demonstrate the advantages of our approach to multiple imputation through simulated data as well as via replications of existing research.

3

Paper

Listwise Deletion is Evil: What to Do About Missing Data in Political Science (revised) King, Gary Honaker, James Joseph, Anne Scheve, Kenneth Uploaded 08-19-1998 Keywords missing data imputation IP EM EMs EMis data augmentation MCMC importance sampling item nonresponse Abstract We propose a remedy to the substantial discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing data problems based on the concept of ``multiple imputation,'' but most researchers in our field and other social sciences still use far inferior methods. Indeed, we demonstrate that the threats to validity from current missing data practices rival the biases from the much better known omitted variable problem. As it turns out, this discrepancy is not entirely our fault, as 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 demanding of considerable expertise on the part of the user (even experts disagree on how to use them). In this paper, we adapt an existing algorithm, and use it to implement a general-purpose, multiple imputation model for missing data. This algorithm is between 65 and 726 times faster than the leading method recommended in the statistics literature and is very easy to use. We also quantify the considerable risks of current political science missing data practices, illustrate how to use the new procedure, and demonstrate the advantages of our approach to multiple imputation through simulated data as well as via replications of existing research. We also offer easy-to-use public domain software that implements our approach.