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dc.contributor.authorBarnett, Adrian G
dc.contributor.authorMcElwee, Paul
dc.contributor.authorNathan, Andrea
dc.contributor.authorBurton, Nicola W
dc.contributor.authorTurrell, Gavin
dc.date.accessioned2020-10-26T04:03:09Z
dc.date.available2020-10-26T04:03:09Z
dc.date.issued2017
dc.identifier.issn2044-6055en_US
dc.identifier.doi10.1136/bmjopen-2017-01728en_US
dc.identifier.urihttp://hdl.handle.net/10072/398688
dc.description.abstractObjectives: To examine whether respondents to a survey of health and physical activity and potential determinants could be grouped according to the questions they missed, known as 'item missing'. Design: Observational study of longitudinal data. Setting: Residents of Brisbane, Australia. Participants: 6901 people aged 40-65 years in 2007. Materials and methods: We used a latent class model with a mixture of multinomial distributions and chose the number of classes using the Bayesian information criterion. We used logistic regression to examine if participants' characteristics were associated with their modal latent class. We used logistic regression to examine whether the amount of item missing in a survey predicted wave missing in the following survey. Results: Four per cent of participants missed almost one-fifth of the questions, and this group missed more questions in the middle of the survey. Eighty-three per cent of participants completed almost every question, but had a relatively high missing probability for a question on sleep time, a question which had an inconsistent presentation compared with the rest of the survey. Participants who completed almost every question were generally younger and more educated. Participants who completed more questions were less likely to miss the next longitudinal wave. Conclusions: Examining patterns in item missing data has improved our understanding of how missing data were generated and has informed future survey design to help reduce missing data.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.language.isoeng
dc.publisherBMJ Publishing Groupen_US
dc.publisher.placeUnited Kingdom
dc.relation.ispartofpagefrom1en_US
dc.relation.ispartofpageto9en_US
dc.relation.ispartofissue10en_US
dc.relation.ispartofjournalBMJ Openen_US
dc.relation.ispartofvolume7en_US
dc.subject.fieldofresearchPublic Health and Health Services not elsewhere classifieden_US
dc.subject.fieldofresearchClinical Sciencesen_US
dc.subject.fieldofresearchPublic Health and Health Servicesen_US
dc.subject.fieldofresearchOther Medical and Health Sciencesen_US
dc.subject.fieldofresearchcode111799en_US
dc.subject.fieldofresearchcode1103en_US
dc.subject.fieldofresearchcode1117en_US
dc.subject.fieldofresearchcode1199en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsLife Sciences & Biomedicineen_US
dc.subject.keywordsMedicine, General & Internalen_US
dc.subject.keywordsGeneral & Internal Medicineen_US
dc.subject.keywordsMULTIPLE IMPUTATIONen_US
dc.titleIdentifying patterns of item missing survey data using latent groups: an observational studyen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationBarnett, AG; McElwee, P; Nathan, A; Burton, NW; Turrell, G, Identifying patterns of item missing survey data using latent groups: an observational study, BMJ Open, 2017, 7 (10), pp. 1-9en_US
dcterms.licensehttp://creativecommons.org/licenses/by-nc/4.0/en_US
dc.date.updated2020-10-25T23:59:39Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© The Author(s) 2017. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.en_US
gro.hasfulltextFull Text
gro.griffith.authorBurton, Nicola W.


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