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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.

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composite likelihoodlatent variable models

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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.