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Computerized Adaptive Testing System of Functional Assessment of Stroke
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An inexpensive retrospective standard setting method based on item facilities.

John C McLachlan1, K Alex Robertson2, Bridget Weller3

  • 1University of Central Lancashire, Harrington Building, 11 Victoria St, Preston, PR1 7QS, UK. jcmclachlan1@uclan.ac.uk.

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|January 7, 2021
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Summary
This summary is machine-generated.

A new standard-setting method uses borderline candidate performance across all items and test-takers to establish reliable cut scores. This approach offers a rapid, retrospective alternative to traditional methods like Angoff and Cohen for healthcare assessments.

Keywords:
CostExponentialRapidRetrospectiveStandard-setting

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Area of Science:

  • Medical Education
  • Assessment Methodology
  • Psychometrics

Background:

  • Standard setting in high-stakes healthcare assessments is challenging.
  • Traditional methods like Angoff and Cohen have limitations (e.g., conceptualization, time, reliance on single candidates).
  • A novel method using cohort-wide data is needed.

Purpose of the Study:

  • To develop and validate a new retrospective standard-setting method.
  • To utilize performance data from borderline candidates across all items and the entire cohort.
  • To provide a rapid and efficient alternative for establishing assessment cut scores.

Main Methods:

  • Identified borderline candidates based on annual academic performance.
  • Plotted item scores of borderline candidates against the whole cohort's facility.
  • Fitted exponential curves (y ≈ C * e^(Fx)) to predict borderline performance.

Main Results:

  • An equation y ≈ C * e^(Fx) accurately predicts borderline candidate facility for each item.
  • Constants C and F were estimated (C=12.3, F=0.021) for the study cohort.
  • The average predicted cut score across all items represents the overall assessment cut score.

Conclusions:

  • The derived equation and constants (C=12.3, F=0.021) are stable and applicable.
  • This novel retrospective method is rapid and uses comprehensive data (all items, all candidates).
  • It serves as a valuable alternative to Angoff and Cohen methods, especially for setting cut scores for entire tests or individual items.