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Evaluating the Effects of Missing Data Handling Methods on Scale Linking Accuracy.

Tong Wu1,2, Stella Y Kim1, Carl Westine1

  • 1University of North Carolina at Charlotte, USA.

Educational and Psychological Measurement
|November 17, 2023
PubMed
Summary
This summary is machine-generated.

Handling missing data in large-scale assessments is crucial for accurate scale linking. Response function imputation, multiple imputation, and full information likelihood estimation methods performed best, minimizing errors in item response theory scale linking.

Keywords:
common item nonequivalent group designitem response theorymissing datascale linking

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistics

Background:

  • Large-scale assessments frequently encounter missing data, impacting the reliability of results.
  • Item response theory (IRT) is widely used, but its application with missing data in scale linking remains underexplored.
  • Scale linking ensures comparability across test forms, essential for longitudinal studies and program evaluation.

Purpose of the Study:

  • To evaluate the impact of six different missing data handling methods on the accuracy of IRT scale linking.
  • To compare the performance of these methods under various simulation conditions and missing data mechanisms.
  • To identify the most effective strategies for addressing missing responses in common items during scale linking.

Main Methods:

  • Simulated data were generated under a common-item nonequivalent group design (CINEG).
  • Six methods were tested: listwise deletion (LWD), treating missing as incorrect (IN), corrected item mean imputation (CM), response function imputation (RF), multiple imputation (MI), and full information maximum likelihood (FIML).
  • Linking accuracy was assessed based on the errors in estimated linking coefficients.

Main Results:

  • Response function imputation (RF), multiple imputation (MI), and full information maximum likelihood (FIML) demonstrated superior performance, yielding the lowest linking errors.
  • Listwise deletion (LWD) resulted in the highest linking errors across all tested conditions.
  • The choice of missing data handling method significantly influenced the accuracy of scale linking.

Conclusions:

  • RF, MI, and FIML are recommended for handling missing data in IRT scale linking to ensure accurate and reliable results.
  • Listwise deletion should be avoided due to its detrimental effect on scale linking accuracy.
  • Effective missing data strategies are vital for maintaining the validity and comparability of large-scale assessments.