The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item Nonresponse
Designs using planned missingness, such as the split questionnaire design, are becoming more and more important in social survey research. To ensure an acceptable questionnaire length, these approaches typically entail large amounts of planned missing data, which can be imputed after data collectio...
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| Language: | English |
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European Survey Research Association
2024-08-01
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| Series: | Survey Research Methods |
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| Online Access: | https://ojs.ub.uni-konstanz.de/srm/article/view/8158 |
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| author | Julian B. Axenfeld Christian Bruch Christof Wolf Annelies G. Blom |
| author_facet | Julian B. Axenfeld Christian Bruch Christof Wolf Annelies G. Blom |
| author_sort | Julian B. Axenfeld |
| collection | DOAJ |
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Designs using planned missingness, such as the split questionnaire design, are becoming more and more important in social survey research. To ensure an acceptable questionnaire length, these approaches typically entail large amounts of planned missing data, which can be imputed after data collection. However, social surveys typically also include other types of missingness such as item nonresponse by survey participants, which need to be imputed as well. This entails a complex imputation task with amounts of missing data larger than initially planned and a potentially non-random, heterogeneous mechanism. Yet, it remains to be studied whether accurate multiple-imputation estimates can be obtained in practice with planned missingness and item nonresponse.
To deal with this research gap, we apply a Monte Carlo simulation study using real social survey data. In this study, we simulate missing data based on item nonresponse with different mechanisms and proportions of item nonresponse as well as different proportions of planned missing data. We find that item nonresponse can jeopardize the quality of estimates after multiple imputation especially when the total amount of missing data from both sources is high or when there is a considerable proportion of item nonresponse that is missing not at random. Therefore, from an imputation perspective, survey designers should incorporate their expectations about item nonresponse on each variable when designing surveys with planned missing data.
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| format | Article |
| id | doaj-art-b9dbfb253daf4a2ea65653a43b38abde |
| institution | Kabale University |
| issn | 1864-3361 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | European Survey Research Association |
| record_format | Article |
| series | Survey Research Methods |
| spelling | doaj-art-b9dbfb253daf4a2ea65653a43b38abde2025-02-09T14:16:10ZengEuropean Survey Research AssociationSurvey Research Methods1864-33612024-08-01182The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item NonresponseJulian B. Axenfeld0https://orcid.org/0000-0003-3728-3828Christian Bruch1https://orcid.org/0000-0003-0926-6609Christof Wolf2https://orcid.org/0000-0002-9364-9524Annelies G. Blom3https://orcid.org/0000-0003-0377-301XUniversity of MannheimGESIS Leibniz Institute for the Social SciencesGESIS Leibniz Institute for the Social SciencesUniversity of Bremen Designs using planned missingness, such as the split questionnaire design, are becoming more and more important in social survey research. To ensure an acceptable questionnaire length, these approaches typically entail large amounts of planned missing data, which can be imputed after data collection. However, social surveys typically also include other types of missingness such as item nonresponse by survey participants, which need to be imputed as well. This entails a complex imputation task with amounts of missing data larger than initially planned and a potentially non-random, heterogeneous mechanism. Yet, it remains to be studied whether accurate multiple-imputation estimates can be obtained in practice with planned missingness and item nonresponse. To deal with this research gap, we apply a Monte Carlo simulation study using real social survey data. In this study, we simulate missing data based on item nonresponse with different mechanisms and proportions of item nonresponse as well as different proportions of planned missing data. We find that item nonresponse can jeopardize the quality of estimates after multiple imputation especially when the total amount of missing data from both sources is high or when there is a considerable proportion of item nonresponse that is missing not at random. Therefore, from an imputation perspective, survey designers should incorporate their expectations about item nonresponse on each variable when designing surveys with planned missing data. https://ojs.ub.uni-konstanz.de/srm/article/view/8158item nonresponseimputationplanned missing datasplit questionnaire design |
| spellingShingle | Julian B. Axenfeld Christian Bruch Christof Wolf Annelies G. Blom The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item Nonresponse Survey Research Methods item nonresponse imputation planned missing data split questionnaire design |
| title | The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item Nonresponse |
| title_full | The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item Nonresponse |
| title_fullStr | The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item Nonresponse |
| title_full_unstemmed | The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item Nonresponse |
| title_short | The Performance of Multiple Imputation in Social Surveys with Missing Data from Planned Missingness and Item Nonresponse |
| title_sort | performance of multiple imputation in social surveys with missing data from planned missingness and item nonresponse |
| topic | item nonresponse imputation planned missing data split questionnaire design |
| url | https://ojs.ub.uni-konstanz.de/srm/article/view/8158 |
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