The Use of Multiple Imputation to Handle Missing Data in Secondary Datasets: Suggested Approaches when Missing Data Results from the Survey Structure.
Secondary datasets are used in healthcare research because of its cost advantages, its convenience, and the size of the datasets. However, missing data can cause problems that are difficult to resolve. This manuscript reviews possible causes for missing data, and how to address them. Many researcher...
| Publicado en: | Inquiry (00469580) pp. 1 - 7 |
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| Formato: | Artículo |
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Sage Publications Inc.
5/3/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=156679949&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 156679949 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 5/3/2022 pid: 344 pub: Sage Publications Inc. artinfo: ui: 156679949 10.1177/00469580221088627 ppf: 1 ppct: 6 formats: tig: atl: The Use of Multiple Imputation to Handle Missing Data in Secondary Datasets: Suggested Approaches when Missing Data Results from the Survey Structure. aug: au: Jo, Soojung affil: College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA su: Statistics Online information services Systematic reviews Database management Medical care research Descriptive statistics Data analysis MEDLINE sug: subj: Statistics Online information services Systematic reviews Database management Medical care research Descriptive statistics Data analysis MEDLINE keyword: method missing data multiple imputation quantitative secondary analysis ab: Secondary datasets are used in healthcare research because of its cost advantages, its convenience, and the size of the datasets. However, missing data can cause problems that are difficult to resolve. This manuscript reviews possible causes for missing data, and how to address them. Many researchers use multiple imputation as a solution, which consists of three phases: (a) the imputation phase, (b) the analysis phase, and (c) the pooling phase. When missing data is caused by a refusal to answer or by insufficient knowledge, multiple imputation works well. However, difficulties arise when there are problems with screening questions. If respondents do not answer a screening question, possible answers could be either "yes" or "no." This paper suggests identifying "yes" responses on the screening question, and setting them aside for use in the analysis. The reasons for this approach are the impossibility of conducting multiple imputation twice, the problem of imputation based on the population after sample weight, and the difficulty of producing logical errors on the estimation in imputation phase. This manuscript uses as an example the techniques used to address missing data from screening questions in a national US dataset. These techniques of multiple imputation using examples from the dataset could be used by researchers in future healthcare research that relies on secondary datasets. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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