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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.

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Title The effects of clustered data on standard error estimates in covariance structure analysis : a field data application
Author(s) Vocino, Andrea
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
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30016681

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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.