File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Statistical approaches to testing the relationships of the built environment with resident-level physical activity behavior and health outcomes in cross-sectional studies with cluster sampling

TitleStatistical approaches to testing the relationships of the built environment with resident-level physical activity behavior and health outcomes in cross-sectional studies with cluster sampling
Authors
Keywordsenvironment-behavior
health
methods
quantitative methods
urban design
Issue Date2011
PublisherSage Publications, Inc. The Journal's web site is located at http://www.sagepub.com/journal.aspx?pid=97
Citation
Journal Of Planning Literature, 2011, v. 26 n. 2, p. 151-167 How to Cite?
AbstractTo achieve valid conclusions, studies exploring associations of the built environment with residents' physical activity and health-related outcomes need to employ statistical approaches accounting for clustered data. This article discusses the following main statistical approaches: analysis of covariance, regression models with robust standard errors, generalized estimating equations, and multilevel generalized linear models. The choice of a statistical method depends on the characteristics of the study and research questions. While the first three approaches are employed to account for clustering in the data, multilevel models can also help unravel more substantive issues within a social ecological theoretical framework of health behavior. © The Author(s) 2011.
Persistent Identifierhttp://hdl.handle.net/10722/139962
ISSN
2015 Impact Factor: 1.765
2015 SCImago Journal Rankings: 1.332
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCerin, Een_HK
dc.date.accessioned2011-09-23T06:03:51Z-
dc.date.available2011-09-23T06:03:51Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Planning Literature, 2011, v. 26 n. 2, p. 151-167en_HK
dc.identifier.issn0885-4122en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139962-
dc.description.abstractTo achieve valid conclusions, studies exploring associations of the built environment with residents' physical activity and health-related outcomes need to employ statistical approaches accounting for clustered data. This article discusses the following main statistical approaches: analysis of covariance, regression models with robust standard errors, generalized estimating equations, and multilevel generalized linear models. The choice of a statistical method depends on the characteristics of the study and research questions. While the first three approaches are employed to account for clustering in the data, multilevel models can also help unravel more substantive issues within a social ecological theoretical framework of health behavior. © The Author(s) 2011.en_HK
dc.languageengen_US
dc.publisherSage Publications, Inc. The Journal's web site is located at http://www.sagepub.com/journal.aspx?pid=97en_HK
dc.relation.ispartofJournal of Planning Literatureen_HK
dc.rightsJournal of Planning Literature. Copyright © Sage Publications, Inc.-
dc.subjectenvironment-behavioren_HK
dc.subjecthealthen_HK
dc.subjectmethodsen_HK
dc.subjectquantitative methodsen_HK
dc.subjecturban designen_HK
dc.titleStatistical approaches to testing the relationships of the built environment with resident-level physical activity behavior and health outcomes in cross-sectional studies with cluster samplingen_HK
dc.typeArticleen_HK
dc.identifier.emailCerin, E: ecerin@hku.hken_HK
dc.identifier.authorityCerin, E=rp00890en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1177/0885412210386229en_HK
dc.identifier.scopuseid_2-s2.0-79960334484en_HK
dc.identifier.hkuros192939en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79960334484&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume26en_HK
dc.identifier.issue2en_HK
dc.identifier.spage151en_HK
dc.identifier.epage167en_HK
dc.identifier.isiWOS:000294162800002-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCerin, E=14522064200en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats