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Reducing Calibration Bias for Person Fit Assessment by Mixture Model Expansion.

Johan Braeken1, Saskia van Laar2

  • 1University of Oslo, Oslo, Norway.

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Summary
This summary is machine-generated.

This study introduces a novel one-stage method for assessing person fit in surveys, improving measurement appropriateness. The new approach overcomes limitations of traditional two-stage methods, offering a more accurate evaluation of individual response patterns.

Keywords:
measurement appropriatenessmixtureperson fitrandom responders

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Measurement appropriateness is crucial for valid individual assessment.
  • Aberrant response patterns indicate a poor fit between a person and a test/survey.
  • Traditional person fit statistics (e.g., Lz) suffer from calibration bias due to two-stage estimation.

Purpose of the Study:

  • To propose and evaluate a one-stage person fit assessment method.
  • To address the limitations of existing two-stage estimation procedures.
  • To assess person fit in challenging conditions, such as short polytomous scales.

Main Methods:

  • Developed a one-stage solution by calibrating a model expansion with a mixture component for aberrant response patterns.
  • Conducted a simulation study to evaluate the proposed method.
  • Focused on short polytomous survey scales, common in large-scale assessments.

Main Results:

  • The one-stage approach offers a viable alternative to traditional methods.
  • The simulation study demonstrated the method's effectiveness under unfavorable conditions.
  • This method potentially reduces calibration bias inherent in two-stage procedures.

Conclusions:

  • The proposed one-stage person fit method provides a more robust assessment of measurement appropriateness.
  • This approach is particularly valuable for short polytomous scales in large-scale educational assessments.
  • Future research should explore its application across diverse assessment contexts.