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Pairwise likelihood ratio tests and model selection criteria for structural equation models with ordinal variables

Katsikatsou, Myrsini and Moustaki, Irini (2016) Pairwise likelihood ratio tests and model selection criteria for structural equation models with ordinal variables. Psychometrika, 81 (4). pp. 1046-1068. ISSN 0033-3123

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Identification Number: 10.1007/s11336-016-9523-z

Abstract

Correlated multivariate ordinal data can be analysed with structural equation models. Parameter estimation has been tackled in the literature using limited-information methods including three-stage least squares and pseudo-likelihood estimation methods such as pairwise maximum likelihood estimation. In this paper, two likelihood ratio test statistics and their asymptotic distributions are derived for testing overall goodness-of fit and nested models respectively under the estimation framework of pairwise maximum likelihood estimation. Simulation results show a satisfactory performance of type I error and power for the proposed test statistics and also suggest that the performance of the proposed test statistics is similar to that of the test statistics derived under the three-stage diagonally weighted and unweighted least squares. Furthermore, the corresponding, under the pairwise framework, model selection criteria, AIC and BIC, show satisfactory results in selecting the right model in our simulation examples. The derivation of the likelihood ratio test statistics and model selection criteria under the pairwise framework together with pairwise estimation provide a flexible framework for fitting and testing structural equation models for ordinal as well as for other types of data. The test statistics derived and the model selection criteria are used on data on `trust in the police' selected from the 2010 European Social Survey. The proposed test statistics and the model selection criteria have been implemented in the R package lavaan.

Item Type: Article
Official URL: http://link.springer.com/journal/11336
Additional Information: © 2016 The Psychometric Society
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 08 Aug 2016 14:48
Last Modified: 20 Mar 2019 02:58
Projects: ES/L009838/1
Funders: Economic and Social Research Council
URI: http://eprints.lse.ac.uk/id/eprint/67386

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