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The Ebel method is a standard setting method normally used to determine a cut score for multiple-choice question types of tests. The Ebel method has been used for setting standards for examinations within the fields of higher education and medical and health professions and for applicant selection decision-making.

The Ebel method involves a panel of experts who classify each item by two criteria: (1) level of difficulty (e.g., easy, medium, hard) and (2) relevance or importance (e.g., essential, important, desirable, unsure). Then, the panel reaches a consensus regarding the expected percentage of items that should be answered correctly for each group of items, classified by both difficulty and relevance/importance. To determine the cut score for the test, the total number of items in each group is multiplied by the required percentage of correct answers; then the sum of all the groups is divided by the number of items multiplied by the number of panelists. The hypothetical example shown in Table 1 demonstrates how that works.

Table 1 An example of calculating cut score using the Ebel method

A

B

C

D

E

F

G

H

I

J

K

1

Relevance/importance category

Difficulty Category

Number of items classifed per group by each judge

Agreed percentage correct required

Product

Cut score

2

Expert 1

Expert 2

Expert 3

Expert 4

Expert 5

Total

3

Essential

Easy

17

14

13

17

15

76

100

7600

4

Medium

11

9

8

10

14

52

85

4420

5

Hard

9

7

5

4

6

31

75

2325

6

Important

Easy

14

11

11

14

17

67

70

4690

7

Medium

14

14

15

9

8

60

65

3900

8

Hard

8

14

13

6

11

52

60

3120

9

Desirable

Easy

7

9

12

10

8

46

60

2760

10

Medium

12

11

9

7

10

49

55

2695

11

Hard

16

13

11

22

14

76

50

3800

12

Not sure

Easy

6

8

9

8

9

40

40

1600

13

Medium

5

8

8

9

8

38

30

1140

14

Hard

6

7

11

9

5

38

10

380

15

Total

125

125

125

125

125

625

38430

61.49

In Table 1, a panel of five experts applies the Ebel method to an examination consisting of 125 items.

  • Expert 1 classifies each item to one of the 12 groups; the number of items classified to each group for Expert 1 is shown in column C;
  • Expert 2 does the same for column D and so forth with all other experts. The sum of items in each group across all experts is placed in column H.
  • The agreed percentage correct required per group is placed in column I. Column J is the product of multiplication of H × I.
  • The total number of items classified is the sum of column H (H15).
  • The total sum of product J is J15.
  • The calculated cut score is J15/H15, that is, 38,430/625 = 61.49.

Modifications

A few modifications have been suggested to the Ebel method. For example, instead of reaching a consensus on the percentage correct required, it is possible to ask each expert about his or her preferred percentage correct required and then calculate the average across all experts. Alternatively, the percentage correct required could be determined by policy rather than by experts’ advice. Another modification is to classify the degree of difficulty according to the probability of items responded to correctly, for example, <0.5 hard, 0.5–0.7 medium, and >0.7 easy. Ernest Skakun and Samuel Kling suggested adding taxonomy criteria to the Ebel method.

Strengths and Weaknesses

Strengths

The Ebel method requires experts to estimate the difficulty and the relevance of items. This replicates the process that the item/examiner writers would normally go through—trying to identify items that are relevant and at the right pitch. As such, the method adds an additional perspective to the same process of examination design. For the experts, there is no need to imagine the hypothetical borderline examinee, which may vary significantly across experts. They all relate to the same group of examinees and the same curriculum, hence the reference for the judgment is similar to all.

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