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Pairwise likelihood estimation for factor analysis models with ordinal data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
London School of Economics, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2012 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 56, no 12, 4243-4258 p.Article in journal (Refereed) Published
Abstract [en]

A pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data and fitted both in an exploratory and confirmatory set-up. The performance of the method is studied via simulations and comparisons with full information maximum likelihood (FIML) and three-stage limited information estimation methods, namely the robust unweighted least squares (3S-RULS) and robust diagonally weighted least squares (3S-RDWLS). The advantage of PML over FIML is mainly computational. Unlike PML estimation, the computational complexity of FIML estimation increases either with the number of factors or with the number of observed variables depending on the model formulation. Contrary to 3S-RULS and 3S-RDWLS estimation, PML estimates of all model parameters are obtained simultaneously and the PML method does not require the estimation of a weight matrix for the computation of correct standard errors. The simulation study on the performance of PML estimates and estimated asymptotic standard errors investigates the effect of different model and sample sizes. The bias and mean squared error of PML estimates and their standard errors are found to be small in all experimental conditions and decreasing with increasing sample size. Moreover, the PML estimates and their standard errors are found to be very close to those of FIML.

Place, publisher, year, edition, pages
2012. Vol. 56, no 12, 4243-4258 p.
Keyword [en]
Composite maximum likelihood, Factor analysis, Ordinal data, Pairwise likelihood, Three-stage estimation, Item response theory approach
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:uu:diva-181384DOI: 10.1016/j.csda.2012.04.010ISI: 000307483100034OAI: oai:DiVA.org:uu-181384DiVA: diva2:557710
Available from: 2012-09-28 Created: 2012-09-24 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Composite Likelihood Estimation for Latent Variable Models with Ordinal and Continuous, or Ranking Variables
Open this publication in new window or tab >>Composite Likelihood Estimation for Latent Variable Models with Ordinal and Continuous, or Ranking Variables
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The estimation of latent variable models with ordinal and continuous, or ranking variables is the research focus of this thesis. The existing estimation methods are discussed and a composite likelihood approach is developed. The main advantages of the new method are its low computational complexity which remains unchanged regardless of the model size, and that it yields an asymptotically unbiased, consistent, and normally distributed estimator.

The thesis consists of four papers. The first one investigates the two main formulations of the unrestricted Thurstonian model for ranking data along with the corresponding identification constraints. It is found that the extra identifications constraints required in one of them lead to unreliable estimates unless the constraints coincide with the true values of the fixed parameters.

In the second paper, a pairwise likelihood (PL) estimation is developed for factor analysis models with ordinal variables. The performance of PL is studied in terms of bias and mean squared error (MSE) and compared with that of the conventional estimation methods via a simulation study and through some real data examples. It is found that the PL estimates and standard errors have very small bias and MSE both decreasing with the sample size, and that the method is competitive to the conventional ones.

The results of the first two papers lead to the next one where PL estimation is adjusted to the unrestricted Thurstonian ranking model. As before, the performance of the proposed approach is studied through a simulation study with respect to relative bias and relative MSE and in comparison with the conventional estimation methods. The conclusions are similar to those of the second paper.

The last paper extends the PL estimation to the whole structural equation modeling framework where data may include both ordinal and continuous variables as well as covariates. The approach is demonstrated through an example run in R software. The code used has been incorporated in the R package lavaan (version 0.5-11).

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. 31 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Social Sciences, ISSN 1652-9030 ; 86
Keyword
latent variable models, factor analysis, structural equation models, Thurstonian model, item response theory, composite likelihood estimation, pairwise likelihood estimation, maximum likelihood, weighted least squares, ordinal variables, ranking variables, lavaan
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:uu:diva-188342 (URN)978-91-554-8571-9 (ISBN)
Public defence
2013-02-15, Hörsal 2, Ekonomikum, Kyrkogårdsgatan 10, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2013-01-23 Created: 2012-12-15 Last updated: 2013-02-11Bibliographically approved

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Katsikatsou, MyrsiniYang-Wallentin, FanJöreskog, Karl G.

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