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Exploring Incomplete Rating Designs With Mokken Scale Analysis.

Stefanie A Wind1, Yogendra J Patil1

  • 1The University of Alabama, Tuscaloosa, AL, USA.

Educational and Psychological Measurement
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Summary
This summary is machine-generated.

This study investigates how imputing missing data affects Mokken scale analysis for educational assessment rating quality. Simple imputation methods accurately recover monotonicity and scalability, but invariant ordering results are unpredictable.

Keywords:
Mokken scalingmissing dataperformance assessmentrating quality

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistical Modeling

Background:

  • Mokken scale analysis offers a nonparametric approach to evaluating rating quality in educational performance assessments.
  • A key limitation is the requirement for complete data, which is often impractical in operational assessment systems.
  • Missing data can compromise the validity of rating quality evaluations.

Purpose of the Study:

  • To explore the impact of missing data imputation techniques on Mokken-based rating quality indicators.
  • To assess the effectiveness of imputation in maintaining rater monotonicity, rater scalability, and invariant rater ordering.
  • To provide guidance on using Mokken scale analysis with incomplete rating data.

Main Methods:

  • Utilized simulated and real data from a rater-mediated writing assessment.
  • Introduced varying levels of missingness to the datasets.
  • Applied four different missing data imputation techniques (rater means, student means, etc.).
  • Analyzed the effects of imputation on Mokken scale analysis indicators.

Main Results:

  • Simple imputation methods (rater and student means) generally provided accurate recovery of rater monotonicity indices and rater scalability coefficients.
  • Discrepancies in invariant rater ordering between original and imputed data were unpredictable across different imputation methods.
  • The choice of imputation technique can influence the reliability of certain Mokken scale analysis results.

Conclusions:

  • Simple imputation techniques show promise for addressing missing data in Mokken scale analysis for rating quality.
  • Caution is advised regarding the interpretation of invariant rater ordering when using imputed data.
  • Further research is needed to refine imputation strategies for robust Mokken scale analysis in educational assessments.