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Manufacturers, suppliers and others provide what you see here, and we have not verified it. See our disclaimer. Specifications Publisher Routledge, Taylor and Francis. Customer Reviews. Write a review. Ask a question. More advanced methods of this type provide the panellists with some psychometric parameters with which to facilitate or improve their judgement [ 7 , 8 , 9 , 10 ]. Alternative methods do not use panellists, but use the student examination marks to generate cut-scores, without any additional judgement [ 11 , 12 , 13 , 14 ].
The extent to which standards rely on, or are independent of, assessment data can vary even within implementations of the same method. To date no conclusive evidence is available to suggest which method is more accurate at identifying the cut-scores that best distinguish the competent from the incompetent examinees [ 4 ]. This is despite compelling evidence suggesting that whenever two or more different methods are applied to the same examination data the cut-score are almost always different [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ].
It is evident and well documented that different methods yield different cut-scores for the very same examination results with no evidence provided to suggest which method is superior to others [ 21 ]. Consequently, the quality of standard setting methods is commonly measured by the level of subjective agreement among judges, the reliability of the results, or the error of measurement of the yielded cut-scores [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ].
This type of research does not measure natural or observed phenomena but rather measures only the accuracy of a standard setting technique under a defined set of assumptions; as part of an evidence-based approach [ 32 , 33 , 34 ]. Overall, it is a challenge to find a standard setting model applicable to observed data, yet providing a measure of accuracy beyond just the agreement of judges.
The current paper introduces a method which provides a partial solution to the abovementioned challenge. The distributions are normalised, and the cut point is at the optimal interface between the two distributions with the same z-scores for cF and cP. In a usual Angoff procedure, each person in a panel of judges reviews the examination, and estimates the proportion of minimally competent examinees likely to give the correct answer to each of the items. The scores across items and judges are then averaged to determine the cut-score.
In our new method, the judges may also review each item; however, this is done to allow them to gauge the overall impression of the exam difficulty.
With their knowledge of the examination difficulty, the student level and the curriculum, the judges answer the following two questions for the examination as a whole:. What would be the lowest score that indicates the examinee is without any doubt, clearly competent in the topics assessed? What would be the highest score that indicates the examinee is without any doubt, clearly incompetent in the topics assessed?
The only data used in this method are the scores independently given by the judges in response to the above two questions.
The following equation is used to identify the Z score Z which would apply to both confidence intervals of X L and X H when they interface. A Z-score table is then used to identify the statistical confidence of the cut-score. In other words, this indicates the level of confidence one may have in that cut-score being correct. A demonstration of the method is presented below in Figs. However, unlike in Fig. In this example it is clear that the examiners reached greater agreement about the value of L than about the value of H. It is noteworthy that although the CS is not in the middle between L and H, it is still in the optimal location which yields the same confidence for competence and incompetence for scores above and below respectively the CS.
Setting Performance Standards: Concepts, Methods, and Perspectives by Gregory J. Cizek
The third example Fig. Similar to previous examples Figs. However, in this example Fig.
In this example it is clear that the examiners reached greater agreement about the value of H than about the value of L. Also, although the CS is not in the middle between L and H, it is still in the optimal location which yields the same confidence for competence and incompetence for scores above and below respectively the CS. Of note is that the CS is always located in a place that equates the confidence that a score just under the CS indicates incompetence with the confidence that a score just above the CS indicated competence.
The new method does not assume that the agreement among the assessors would be the same about thresholds for clear pass and clear fail; and no previous study supporting such an assumption of equity was identified. This assumption is reasonable should the judges composition be balanced [ 30 ]. Possible, yet partial remedies for heavily skewed scores are either to remove the extreme scores or applying bootstrapping to calculate robust SEs [ 38 , 39 ].
These techniques may be useful also for other standard setting methods relying on SEs [ 26 , 29 , 39 , 40 ]. For this feasibility study, we used 20 multiple choice questionnaire MCQ items taken from the final written examination used for medical students at an Australian university. This examination is set at the medical programme graduate level and the items were placed into a web-based survey.
Setting Performance Standards : Foundations, Methods, and Innovations
Experienced clinical teachers who were familiar with the expected level of medical programme graduates were invited to participate in the study. The respondents provided information on their level of training, gender and age, as well as responses to the following questions:. What would be the lowest score for the entire examination that would indicate that the examinee is without any doubts, clearly competent in the topic assessed? What would be the highest score for the entire examination that would indicate that the examinee is without any doubts, clearly incompetent in the topic assessed?
Participants consented to complete the survey and then review the draft paper containing collated anonymised data. Seventeen participants participated in the questionnaire.
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This is an acceptable size for traditional Angoff processes applied in medical education [ 41 , 42 , 43 , 44 ]. Using eq. In the absence of external valid information about student abilities, there is no other way to increase the confidence of both. Given that one could be either competent or incompetent but cannot be neither or both it is clear that H- 1. Nonetheless from the data in this study, the cut-score of This study describes a new and feasible way to determine cut-scores using a panel of judges.
It is different from Angoff and modified Angoff methods in one major way. The Angoff method and its variants ask judges about the proportion of minimally competent examinees who would give a correct answer to each of the items.
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This is a complex cognitive process that requires the judges to make several decisions: identify what the minimally competent examinee is; and the proportion of such hypothetical examinees that would correctly answer each item. The means of the proportions of this hypothetical minimally competent examinees who would correctly answer each item then determine the cut-score [ 6 , 46 ]. Furthermore, the empirical association between proportions of examinees and an examination cut-score has not been discussed in the literature, thus can at best be an arbitrary mechanism [ 1 , 47 ].
These proportions derived are not used to calculate the cut-score. As discussed above, having concrete points of reference or principles may enhance the accuracy of the determined cut-scores [ 48 , 49 ]. So which cut-score is more trustworthy? The ImpExp model [ 52 ], for example, provides a detailed explanation of that process of responses to questions which overall indicates that variance among Angoff judges is unavoidable.
First the method asks the judges to make judgements about what is clear clear pass and clear fail rather than what is vague probability of correctly answering an item by a minimally competent examinee. What is a clear pass and what is a clear fail can be more easily agreed among assessors as these are based on principles that do not frequently change [ 49 ]. Based on data generated from the same judges for the same set of items, the new method cut-score was That variability may be related to a range of biases derived from judges characteristics, opinions, expertise as well as other factors which should be considered rather than minimised [ 27 , 30 , 53 , 54 ].
So what is the preferred cut-score?
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We believe that the new method cut-score is more trustworthy, firstly, as it is derived from mathematical principles, whereas the directly suggested cut-score is based on an overall impression of the examination difficulty and provides a less defensible cut score. Since asking examiners why they made each decision was not within the scope of the study this is a topic for future studies.
Overall the traditional Angoff and its variants use more data but can be expensive in time and money, whereas the new method uses less data but is quick and inexpensive. Third, the new method considers two independent variances Var L and Var H. The new method provides an inherent moderating mechanism for extreme judges as the cut-score is determined not only by the L and the H but also by the related variances. The use of two different variances determines a cut-score which is not necessarily at the mid-point between the H and the L We suggest that this is a preferable outcome since the mid-point between the H and the L does not consider differences in agreement among judges.
Last but not least, the new method optimises the balance between the false positive and false negative and estimates the confidence that the cut-score is correct. Nonetheless, this is the most balanced cut-score reachable given this particular examination and judges the same z-score for clear pass and clear fail. Similar to many other Angoff methods [ 30 , 56 ] the level of confidence in the cut-score may increase as the number of judges increase.
Nevertheless, the level of confidence in the cut-score should not be of concern since although the closer the H and the L are to each other the smaller the confidence is; a close gap between the L and the H is a desirable and defensible outcome as it is an indication that the judges believe the examination has a high discrimination value. This feasibility study demonstrates how a revised Angoff method could generate a defensible cut-score.