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Below results based on the criteria 'nonresponse'
Total number of records returned: 5

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
Not Asked and Not Answered: Multiple Imputation for Multiple Surveys
Gelman, Andrew
King, Gary
Liu, Chuanhai

Uploaded 10-27-1997
Keywords Bayesian inference
cluster sampling
diagnostics
hierarchical models
ignorable nonresponse
missing data
political science
sample surveys
stratified sampling
multiple imputation
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 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 were 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 to non-imputed 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 each survey separately.

4
Paper
Designing and Analyzing Randomized Experiments
Horiuchi, Yusaku
Imai, Kosuke
Taniguchi, Naoko

Uploaded 07-05-2005
Keywords Bayesian inference
causal inference
noncompliance
nonresponse
randomized block design
Abstract In this paper, we demonstrate how to effectively design and analyze randomized experiments, which are becoming increasingly common in political science research. Randomized experiments provide researchers with an opportunity to obtain unbiased estimates of causal effects because the randomization of treatment guarantees that the treatment and control groups are on average equal in both observed and unobserved characteristics. Even in randomized experiments, however, complications can arise. In political science experiments, researchers often cannot force subjects to comply with treatment assignment or to provide the information necessary for the estimation of causal effects. Building on the recent statistical literature, we show how to make statistical adjustments for these noncompliance and nonresponse problems when analyzing randomized experiments. We also demonstrate how to design randomized experiments so that the potential impact of such complications is minimized.

5
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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