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Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are
Myrsini Katsikatsou1, Irini Moustaki2, Haziq Jamil3
1Horrothia FX, Falmouth, UK.
Summary
This study introduces methods to handle missing data in surveys using pairwise likelihood (PL) estimation. It compares complete-pairs, available-cases, and doubly-robust approaches for improved accuracy in attitudinal scale analysis.
Area of Science:
- Statistics
- Psychometrics
- Survey Methodology
Background:
- Item non-response is a common challenge in attitudinal scales and large-scale assessments.
- Missing data can lead to biased estimates, particularly in pseudo-likelihood estimation frameworks like pairwise likelihood (PL).
- The missing at random (MAR) mechanism is a key assumption in many statistical analyses of incomplete data.
Purpose of the Study:
- To propose and evaluate methods for treating item non-response under the PL estimation framework and a MAR mechanism.
- To compare the performance of different strategies for incorporating missing values in confirmatory factor analysis (CFA) within the PL framework.
- To apply these methods to real-world survey data, specifically the UK data on adult numeracy and literacy.
Main Methods:
- Development of three strategies for handling missing data in CFA under PL: complete-pairs (CP), available-cases (AC), and doubly-robust (DR) approaches.
- Simulation study to empirically assess and compare the performance of the proposed CP, AC, and DR methods.
- Application of the developed methods to analyze the UK component of the OECD Survey of Adult Skills.
Main Results:
- The study investigates the performance of CP, AC, and DR methods for handling missing data in PL estimation.
- CP and AC methods are computationally simpler and require only a model for observed data.
- DR methods are more computationally intensive but offer potential advantages in certain scenarios.
Conclusions:
- The proposed methods offer viable strategies for addressing item non-response in attitudinal scales and large-scale assessments using PL estimation.
- The choice between CP, AC, and DR methods may depend on computational resources and specific data characteristics.
- The study provides practical insights into analyzing incomplete survey data, demonstrated by its application to adult skills assessment data.
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