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Sample Size and Probability Threshold Considerations with the Tailored Data Method.

Adam E Wyse1

  • 1Adam E. Wyse, The American Registry of Radiologic Technologists, 1255 Northland Dr., St. Paul, MN 55120 USA, adam.wyse@arrt.org.

Journal of Applied Measurement
|December 28, 2016
PubMed
Summary
This summary is machine-generated.

The tailored data method using the Rasch model requires careful selection of probability thresholds to ensure sufficient sample size for accurate calibration. Increasing thresholds may reduce data and impact parameter estimates.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • The tailored data method is used with the Rasch model to refine data by removing responses below a probability threshold.
  • Considerations for sample size and probability thresholds are crucial for the validity of this method.

Purpose of the Study:

  • To provide an analytical formula for assessing minimum sample size requirements when using the tailored data method with the Rasch model.
  • To investigate the impact of varying probability thresholds on data completeness and parameter estimation accuracy.

Main Methods:

  • Development and application of a simple analytical formula to check sample size adequacy.
  • Illustration of the formula using real data from a medical imaging licensure exam.
  • Analysis of data with different probability thresholds to observe changes in item responses and parameter estimates.

Main Results:

  • An analytical formula was derived to evaluate sample size requirements for the tailored data method.
  • Increasing the probability threshold led to more item responses being set to missing.
  • Higher probability thresholds generally resulted in increased parameter standard errors and item difficulty estimates.

Conclusions:

  • The choice of probability threshold in the tailored data method significantly impacts data size and the reliability of Rasch model calibrations.
  • It is recommended to carefully consider the interaction between probability thresholds, sample size, and parameter estimate accuracy.
  • The developed formula serves as a practical tool for researchers to ensure adequate sample size when applying the tailored data method.