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

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  • Katsikatsou, Myrsini
  • Moustaki, Irini
  • Md Jamil, Haziq

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.

Suggested Citation

  • Katsikatsou, Myrsini & Moustaki, Irini & Md Jamil, Haziq, 2022. "Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random," LSE Research Online Documents on Economics 108933, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:108933
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    References listed on IDEAS

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    More about this item

    Keywords

    composite likelihood; item non-response; latent variable model; latent variable models;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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