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Detecting Rater Effects under Rating Designs with Varying Levels of Missingness.

Rose E Stafford1, Edward W Wolfe, Jodi M Casablanca

  • 1Rose E. Stafford, Department of Educational Psychology - Quantitative Methods, University of Texas at Austin, 1 University Station D5800, Austin, TX 78712, USA, rose.stafford@utexas.edu.

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Summary
This summary is machine-generated.

Missing data in rating designs has a negligible impact on detecting rater severity and centrality effects using partial credit model (PCM) estimates. Both detection methods maintained low error rates, ensuring accurate identification of rater biases.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Partial Credit Model (PCM) estimates are utilized for detecting rater effects, specifically severity and centrality.
  • The impact of missing data, common in double-scoring rating designs, on rater effect detection remains underexplored.
  • Understanding rater effects is crucial for ensuring the reliability and validity of assessment outcomes.

Purpose of the Study:

  • To evaluate the influence of missing data on the detection of rater severity and centrality.
  • To assess the performance of different methods for identifying extreme scores on rater effect indices.
  • To determine the robustness of PCM-based rater effect detection in the presence of incomplete data.

Main Methods:

  • A simulation study was conducted, generating data with varying rater pool quality, rater effect prevalence/magnitude, and missingness levels.
  • Raters were flagged using rater location (severity) and the standard deviation of rater thresholds (centrality).
  • Two distinct methods for identifying extreme scores on these indices were compared.

Main Results:

  • Both methods for identifying extreme scores demonstrated low Type I and Type II error rates.
  • The presence of missing data had a negligible impact on the accurate detection of severe and central raters.
  • The chosen indices effectively distinguished between raters with and without significant effects.

Conclusions:

  • Rater severity and centrality effects can be reliably detected using PCM estimates, even with missing data.
  • The simulation results suggest that missingness in double-scoring designs does not significantly compromise rater effect detection.
  • The findings support the use of PCM-based indices for quality control in rating processes.