Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random

Myrsini Katsikatsou, Irini Moustaki and Haziq Md Jamil

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Methods for the treatment of item non-response in attitudinal scales and in large-scale assessments under the pairwise likelihood (PL) estimation framework and under a missing at random (MAR) mechanism are proposed. Under a full information likelihood estimation framework and MAR, ignorability of the missing data mechanism does not lead to biased estimates. However, this is not the case for pseudo-likelihood approaches such as the PL. We develop and study the performance of three strategies for incorporating missing values into confirmatory factor analysis under the PL framework, the complete-pairs (CP), the available-cases (AC) and the doubly robust (DR) approaches. The CP and AC require only a model for the observed data and standard errors are easy to compute. Doubly-robust versions of the PL estimation require a predictive model for the missing responses given the observed ones and are computationally more demanding than the AC and CP. A simulation study is used to compare the proposed methods. The proposed methods are employed to analyze the UK data on numeracy and literacy collected as part of the OECD Survey of Adult Skills.

Keywords: composite likelihood; item non-response; latent variable model; latent variable models (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2022-02-01
New Economics Papers: this item is included in nep-ecm, nep-isf and nep-ore
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Published in British Journal of Mathematical and Statistical Psychology, 1, February, 2022, 75(1), pp. 23 - 45. ISSN: 0007-1102

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:108933

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