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Confirmatory Factor Analysis of Ordinal Data Using Full-Information Adaptive Quadrature
Loyola Univ, Dept Psychol, 1032 W Sheridan Rd, Chicago, IL 60660 USA..
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)In: Australian & New Zealand journal of statistics (Print), ISSN 1369-1473, E-ISSN 1467-842X, Vol. 58, no 2, 173-196 p.Article in journal (Refereed) PublishedText
Abstract [en]

We conducted confirmatory factor analysis (CFA) of responses (N=803) to a self-reported measure of optimism, using full-information estimation via adaptive quadrature (AQ), an alternative estimation method for ordinal data. We evaluated AQ results in terms of the number of iterations required to achieve convergence, model fit, parameter estimates, standard errors (SE), and statistical significance, across four link-functions (logit, probit, log-log, complimentary log-log) using 3-10 and 20 quadrature points. We compared AQ results with those obtained using maximum likelihood, robust maximum likelihood, and robust diagonally weighted least-squares estimation. Compared to the other two link-functions, logit and probit not only produced fit statistics, parameters estimates, SEs, and levels of significance that varied less across numbers of quadrature points, but also fitted the data better and provided larger completely standardised loadings than did maximum likelihood and diagonally weighted least-squares. Our findings demonstrate the viability of using full-information AQ to estimate CFA models with real-world ordinal data.

Place, publisher, year, edition, pages
2016. Vol. 58, no 2, 173-196 p.
Keyword [en]
methods of estimation, link functions
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-301047DOI: 10.1111/anzs.12154ISI: 000380043100002OAI: oai:DiVA.org:uu-301047DiVA: diva2:953597
Available from: 2016-08-18 Created: 2016-08-17 Last updated: 2016-08-18Bibliographically approved

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Jöreskog, Karl Gustav
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