Standard setting is the methodology used to determine the proficiency of candidates and the related cut scores for each exam.
Supported Standard Setting Methods
Angoff
Each item in the item-bank can be assigned an Angoff percentage, representing the percentage of minimally competent candidates* (borderline) that would be expected to know the correct answer to the item.
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Borderline Regression
Similar to borderline group, the examiner specifies a global mark for each item answered by the candidate, which minimally has pass/borderline/fail levels. The pass mark is determined by constructing the regression line through all of the groups.
McManus Borderline Regression
Similar to borderline regression, the examiner specifies a global mark for each item answered by the candidate, which minimally has pass/borderline/fail levels. The pass mark is determined by constructing the regression line through all of the groups and combining with a negative confidence interval.
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Related termsCronbach’s Alpha Standard Error of Measurement |
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It is possible to normalise the marks for each item in an exam to a consistent number. You may want to do this so that each item in an exam is equally weighted, despite having an inconsistent number of marks for each item. Note that the normalisation is on an item basis, and not on an exam basis. It is possible to revert to non-normalised scores.
Notes
*It is important that the assessment function has a clear definition of what a borderline or minimally competent candidate is, and whether these are equivalent
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