Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern
The imputation of missing data is often a crucial step in the analysis of survey data. This study reviews typical problems with missing data and discusses a method for the imputation of missing survey data with a large number of categorical variables which do not have a monotone missing pattern. We...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/368791 |
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| _version_ | 1832565083999305728 |
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| author | Machelle D. Wilson Kerstin Lueck |
| author_facet | Machelle D. Wilson Kerstin Lueck |
| author_sort | Machelle D. Wilson |
| collection | DOAJ |
| description | The imputation of missing data is often a crucial step in the analysis of survey data. This study reviews typical problems with missing data and discusses a method for the imputation of missing survey data with a large number of categorical variables which do not have a monotone missing pattern. We develop a method for constructing a monotone missing pattern that allows for imputation of categorical data in data sets with a large number of variables using a model-based MCMC approach. We report the results of imputing the missing data from a case study, using educational, sociopsychological, and socioeconomic data from the National Latino and Asian American Study (NLAAS). We report the results of multiply imputed data on a substantive logistic regression analysis predicting socioeconomic success from several educational, sociopsychological, and familial variables. We compare the results of conducting inference using a single imputed data set to those using a combined test over several imputations. Findings indicate that, for all variables in the model, all of the single tests were consistent with the combined test. |
| format | Article |
| id | doaj-art-2c386cbd5d3d474c92ffa8bb63a73495 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-2c386cbd5d3d474c92ffa8bb63a734952025-02-03T01:09:23ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/368791368791Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing PatternMachelle D. Wilson0Kerstin Lueck1Department of Public Health Sciences, Division of Biostatistics, University of California, Davis, Davis, CA 95616, USASocial Psychology, The University of Adelaide, Adelaide, SA 5005, AustraliaThe imputation of missing data is often a crucial step in the analysis of survey data. This study reviews typical problems with missing data and discusses a method for the imputation of missing survey data with a large number of categorical variables which do not have a monotone missing pattern. We develop a method for constructing a monotone missing pattern that allows for imputation of categorical data in data sets with a large number of variables using a model-based MCMC approach. We report the results of imputing the missing data from a case study, using educational, sociopsychological, and socioeconomic data from the National Latino and Asian American Study (NLAAS). We report the results of multiply imputed data on a substantive logistic regression analysis predicting socioeconomic success from several educational, sociopsychological, and familial variables. We compare the results of conducting inference using a single imputed data set to those using a combined test over several imputations. Findings indicate that, for all variables in the model, all of the single tests were consistent with the combined test.http://dx.doi.org/10.1155/2014/368791 |
| spellingShingle | Machelle D. Wilson Kerstin Lueck Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern Journal of Applied Mathematics |
| title | Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern |
| title_full | Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern |
| title_fullStr | Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern |
| title_full_unstemmed | Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern |
| title_short | Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern |
| title_sort | working with missing data imputation of nonresponse items in categorical survey data with a non monotone missing pattern |
| url | http://dx.doi.org/10.1155/2014/368791 |
| work_keys_str_mv | AT machelledwilson workingwithmissingdataimputationofnonresponseitemsincategoricalsurveydatawithanonmonotonemissingpattern AT kerstinlueck workingwithmissingdataimputationofnonresponseitemsincategoricalsurveydatawithanonmonotonemissingpattern |