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How Does Calibration Timing and Seasonality Affect Item Parameter Estimates?

Adam E Wyse1, Ben Babcock1

  • 1The American Registry of Radiologic Technologists, St. Paul, MN, USA.

Educational and Psychological Measurement
|May 26, 2018
PubMed
Summary
This summary is machine-generated.

Seasonality in test data can negatively impact item response theory (IRT) calibration and equating, leading to poor item parameter estimates. Some programs can calibrate early, but others must wait for complete data to ensure accuracy.

Keywords:
IRTRasch modelcalibrationcalibration timingcertificationcontinuous testingcredentialinglicensureseasonality

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Continuously administered examinations, especially credentialing programs, often exhibit seasonal variations in examinee ability distributions.
  • These seasonal fluctuations can compromise the accuracy of statistical procedures like item response theory (IRT) item calibration and equating.
  • The need for pre-equated test forms in continuous testing adds complexity to managing these seasonal effects.

Purpose of the Study:

  • To investigate the impact of seasonality in testing data on Rasch IRT item parameter estimates.
  • To determine if and when item calibration can be performed without compromising parameter accuracy in the presence of seasonality.
  • To provide guidance on optimal timing for item calibration in continuously administered credentialing programs.

Main Methods:

  • Analysis of data from four credentialing examination programs with varying seasonality and examinee volumes.
  • Application of Rasch item response theory (IRT) models to estimate item parameters.
  • Comparison of item parameter estimates obtained at different points within the examination cycle.

Main Results:

  • Seasonal variations in examinee ability can significantly degrade the quality of item parameter estimates.
  • The timing of item calibration is critical; calibrating at certain times resulted in notably poor parameter estimates.
  • The necessity of waiting for the full examination cycle before calibration depends on program-specific characteristics.

Conclusions:

  • Continuous testing programs must account for seasonality to ensure accurate psychometric analyses.
  • For some programs, early calibration is feasible, while others require data from the entire testing cycle.
  • Careful consideration of data seasonality is essential for maintaining the validity and reliability of credentialing examinations.