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Rater effect comparability in local independence and rater bundle models.

Edward W Wolfe1, Tian Song

  • 1Pearson, 3974 Roberts Ridge NE, Iowa City, IA 52240, USA, ed.wolfe@pearson.com.

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Summary

The Rasch testlet-based rater bundle model (RBM) offers better detection of rater bias compared to the Rasch rating scale model (RSM). Both models showed similar results for rater centrality and inaccuracy.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Rater effects significantly impact the quality and reliability of rating data in various assessment contexts.
  • Detecting and quantifying rater effects is crucial for accurate interpretation of measurement results.
  • Existing literature offers methods for rater effect detection, but comparative performance across models is less explored.

Purpose of the Study:

  • To compare the diagnostic accuracy of the Rasch rating scale model (RSM) and the Rasch testlet-based rater bundle model (RBM) in detecting rater effects.
  • To evaluate the performance of these models using real-world rating data.
  • To assess the consistency of statistical indicators for rater centrality and inaccuracy across both models.

Main Methods:

  • Calibration of real rating data using two psychometric models: Rasch rating scale model (RSM) and Rasch testlet-based rater bundle model (RBM).
  • Comparison of flag rates and agreement rates to identify rater effects.
  • Analysis of rater severity and leniency under both models.

Main Results:

  • The Rasch testlet-based rater bundle model (RBM) demonstrated superior accuracy in diagnosing rater severity and leniency compared to the Rasch rating scale model (RSM).
  • The RSM, relying on the local independence assumption, showed limitations in distinguishing nuanced rater effects.
  • Statistical indicators for rater centrality and inaccuracy were consistent across both the RSM and RBM.

Conclusions:

  • The RBM is a more effective tool for identifying and understanding rater effects, particularly rater severity and leniency, than the traditional RSM.
  • The findings suggest that the RBM's structure, which accounts for testlet effects, improves the detection of rater behavior.
  • While RBM offers better diagnostic power for specific rater biases, both models provide comparable insights into general rater centrality and inaccuracy.