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Below results based on the criteria 'EMis'
Total number of records returned: 3

1
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.

2
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.

3
Paper
What to do About Missing Values in Time Series Cross-Section Data
King, Gary
Honaker, James

Uploaded 07-14-2006
Keywords Missing data
multiple imputation
EM
IP
EMis
time series
cross-section
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 made it possible for us to implement our methods in new open source software which, unlike all existing multiple imputation packages, virtually never crashes.


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