A contribution on the nature and treatment of missing data in large market surveys
Nonresponse (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlyi...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Routledge
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/38048 |
| _version_ | 1848755213871087616 |
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| author | Madden, Gary Vicente, M. Rappoport, P. Banerjee, A. |
| author_facet | Madden, Gary Vicente, M. Rappoport, P. Banerjee, A. |
| author_sort | Madden, Gary |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Nonresponse (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this article is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed missing completely at random, missing at random and not missing at random processes, with data removed. |
| first_indexed | 2025-11-14T08:52:44Z |
| format | Journal Article |
| id | curtin-20.500.11937-38048 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:52:44Z |
| publishDate | 2016 |
| publisher | Routledge |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-380482017-09-13T15:36:42Z A contribution on the nature and treatment of missing data in large market surveys Madden, Gary Vicente, M. Rappoport, P. Banerjee, A. Nonresponse (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this article is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed missing completely at random, missing at random and not missing at random processes, with data removed. 2016 Journal Article http://hdl.handle.net/20.500.11937/38048 10.1080/00036846.2016.1234699 Routledge restricted |
| spellingShingle | Madden, Gary Vicente, M. Rappoport, P. Banerjee, A. A contribution on the nature and treatment of missing data in large market surveys |
| title | A contribution on the nature and treatment of missing data in large market surveys |
| title_full | A contribution on the nature and treatment of missing data in large market surveys |
| title_fullStr | A contribution on the nature and treatment of missing data in large market surveys |
| title_full_unstemmed | A contribution on the nature and treatment of missing data in large market surveys |
| title_short | A contribution on the nature and treatment of missing data in large market surveys |
| title_sort | contribution on the nature and treatment of missing data in large market surveys |
| url | http://hdl.handle.net/20.500.11937/38048 |