The effects of clustered data on standard error estimates in covariance structure analysis : a field data application Vocino, Andrea 2009, The effects of clustered data on standard error estimates in covariance structure analysis : a field data application, Asia Pacific journal of marketing and logistics, vol. 21, no. 1, pp. 7-18, doi: 10.1108/13555850910926218. Attached Files Name Description MIMEType Size Downloads vocino-theeffects-post-2009.pdf Author post print application/pdf 657.22KB 158 Title The effects of clustered data on standard error estimates in covariance structure analysis : a field data application Author(s) Vocino, Andrea orcid.org/0000-0002-5551-9059 Journal name Asia Pacific journal of marketing and logistics Volume number 21 Issue number 1 Start page 7 End page 18 Total pages 12 Publisher Emerald Group Publishing Place of publication Bradford, England Publication date 2009 ISSN 1355-5855 1758-4248 Keyword(s) structural analysis modelling samples Surveys Summary Purpose – The purpose of this article is to present an empirical analysis of complex sample data with regard to the biasing effect of non-independence of observations on standard error parameter estimates. Using field data structured in the form of repeated measurements it is to be shown, in a two-factor confirmatory factor analysis model, how the bias in SE can be derived when the non-independence is ignored. Design/methodology/approach – Three estimation procedures are compared: normal asymptotic theory (maximum likelihood); non-parametric standard error estimation (naïve bootstrap); and sandwich (robust covariance matrix) estimation (pseudo-maximum likelihood). Findings – The study reveals that, when using either normal asymptotic theory or non-parametric standard error estimation, the SE bias produced by the non-independence of observations can be noteworthy. Research limitations/implications – Considering the methodological constraints in employing field data, the three analyses examined must be interpreted independently and as a result taxonomic generalisations are limited. However, the study still provides “case study” evidence suggesting the existence of the relationship between non-independence of observations and standard error bias estimates. Originality/value – Given the increasing popularity of structural equation models in the social sciences and in particular in the marketing discipline, the paper provides a theoretical and practical insight into how to treat repeated measures and clustered data in general, adding to previous methodological research. Some conclusions and suggestions for researchers who make use of partial least squares modelling are also drawn. Notes Reproduced with the kind permission of the copyright owner. Language eng DOI 10.1108/13555850910926218 Field of Research 010405 Statistical Theory Socio Economic Objective 939902 Education and Training Theory and Methodology HERDC Research category C1 Refereed article in a scholarly journal HERDC collection year 2009 Copyright notice ©2009, Emerald Group Publishing Limited Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30016681 Document type: Journal Article Collections: Faculty of Business and Law School of Management and Marketing Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 16 Jul 2009, 11:44:30 EST Mon, 20 Jul 2009, 11:34:00 EST Mon, 20 Jul 2009, 11:34:56 EST Mon, 27 Jul 2009, 11:14:16 EST Wed, 12 Aug 2009, 16:26:06 EST Fri, 20 Nov 2009, 12:55:02 EST Mon, 01 Oct 2012, 16:41:49 EST Mon, 01 Oct 2012, 16:47:15 EST Mon, 01 Oct 2012, 16:49:00 EST Mon, 01 Oct 2012, 16:52:54 EST Tue, 02 Oct 2012, 15:48:24 EST Tue, 02 Oct 2012, 15:49:21 EST Tue, 02 Oct 2012, 15:51:21 EST Tue, 09 Oct 2012, 13:18:18 EST Mon, 16 Jun 2014, 11:23:13 EST Mon, 30 Jun 2014, 14:56:35 EST Mon, 30 Jun 2014, 14:58:03 EST Mon, 30 Jun 2014, 14:59:02 EST Wed, 21 Oct 2015, 13:30:44 EST Tue, 05 Dec 2017, 06:29:26 EST Fri, 07 Sep 2018, 14:13:35 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 1025 Abstract Views, 163 File Downloads  -  Detailed Statistics Created: Thu, 16 Jul 2009, 11:44:26 EST by Lee-Ann Stones

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.