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

Katsikatsou, Myrsini, Moustaki, Irini ORCID: 0000-0001-8371-1251 and Md Jamil, Haziq (2022) Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random. British Journal of Mathematical and Statistical Psychology, 75 (1). 23 - 45. ISSN 0007-1102

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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.

Item Type:	Article
Official URL:	https://bpspsychub.onlinelibrary.wiley.com/journal...
Additional Information:	© 2021 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics B Philosophy. Psychology. Religion > BF Psychology
Date Deposited:	02 Mar 2021 15:21
Last Modified:	23 Nov 2024 03:30
URI:	http://eprints.lse.ac.uk/id/eprint/108933

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