Analyzing Longitudinal Data With Missing Values.
Missing data methodology has improved dramatically in recent years, and popular computer programs now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, researchers in many disciplines still rely on subpar strategies that either eliminat...
| Publicado en: | Rehabilitation Psychology Vol. 56; no. 4; pp. 267 - 289 |
|---|---|
| Autor principal: | |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
November 2011
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=527662157&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 527662157 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00905550 ILQ jtl: Rehabilitation Psychology issn: 00905550 maglogo: N pubinfo: dt: November 2011 vid: 56 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 527662157 10.1037/a0025579 ppf: 267 ppct: 22 formats: tig: atl: Analyzing Longitudinal Data With Missing Values. aug: au: Enders, Craig K. ab: Missing data methodology has improved dramatically in recent years, and popular computer programs now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, researchers in many disciplines still rely on subpar strategies that either eliminate incomplete cases or impute the missing scores with a single set of replacement values. This article provides readers with a nontechnical overview of some key issues from the missing data literature and demonstrates several of the techniques that methodologists currently recommend. This article begins by describing Rubin's missing data mechanisms. After a brief discussion of popular ad hoc approaches, the article provides a more detailed description of five analytic approaches that have received considerable attention in the missing data literature: maximum likelihood estimation, multiple imputation, the selection model, the shared parameter model, and the pattern mixture model. Finally, a series of data analysis examples illustrate the application of these methods. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|