MEASUREMENT RESEARCH ASSOCIATES
TEST INSIGHTS
November 2008
Greetings! 
 
An oral certification examination is very complex and requires the interaction of examiners, candidates, cases, and tasks in the scoring process.  Examiners are primarily responsible for scoring, so the consistency with which they score is an important aspect of the validity and reliability of the examination.

Lidia Martinez
Manager, Computer Based Testing and Analysis

The Measurement of Examiner Consistency
The multi-facet analysis provides measures of the consistency with which examiners score candidates.  The outfit mean-square statistic, the one we find most useful, is based on the ratio of observed variance to expected variance.  This fit statistic has an expectation of 1.00, that is, examiners rated as expected, given their severity, the ability of the candidate, and the difficulty of the task.
 
Various criteria for acceptable values have been used.  For most medical oral examinations, we use the criteria of less than 0.5 or greater than 1.5.  A fit statistic of less than 0.5 indicates a non-discriminating or overly consistent examiner who tends to give the same rating to all candidates.  A fit statistic greater than 1.5 indicates an inconsistent examiner who gave some ratings that are higher than expected to some less able candidates, and/or some lower than expected ratings to some able candidates.
 
One of the reasons examiners can show up as misfitting is due to deviations from their pattern of ratings.  For example, if an examiner is relatively lenient, but gives several unsatisfactory ratings to relatively able candidates, the examiner is likely to show up with a large outfit statistic that reflects that examiner's inconsistency in rating.
 
On the other hand, if an examiner rarely deviates from a particular rating, he/she is likely to have a low fit statistic.  For example, if an examiner tends to give ratings of 3 (satisfactory) to 90% of the candidates scored, regardless of the candidates' abilities, this examiner is likely to show up with a outfit statistic less than 0.5.  Since it is unlikely that this examiner had only satisfactory or better candidates, it is highly probable that the examiner is not using the rating scale to distinguish between the less able and more able candidates.
 
Fortunately, relatively few examiners show up as misfits in the oral certification examinations we typically analyze.  The table shows the number of examiners for five different oral certification exams and the number and percent of examiners who misfit.  This suggests that examiners are familiar with the scoring process and able to distinguish among the medical skill and expertise of the candidates they score. Since examiners' ratings are critical to oral examination scoring, it is re-assuring to know that they do their jobs effectively.

 

Exam

N of Examiners

N  fit < 0.5

(Non Discriminating)

N  fit > 1.5 (Inconsistent)

Total % Misfit Examiners

Exam 1

109

4

11

14%

Exam 2

159

10

5

9%

Exam 3

24

0

0

0%

Exam 4

33

2

4

18%

Exam 5

49

0

2

4%

Measurement Research Associates, Inc.
505 North Lake Shore Dr., Suite 1304
Chicago, IL  60611
Phone: (312) 822-9648     Fax: (312) 822-9650
 

Please help with Standard Dataset 4: Andrich Rating Scale Model



Rasch Publications
Rasch Measurement Transactions (free, online) Rasch Measurement research papers (free, online) Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Applying the Rasch Model 3rd. Ed., Bond & Fox Best Test Design, Wright & Stone
Rating Scale Analysis, Wright & Masters Introduction to Rasch Measurement, E. Smith & R. Smith Introduction to Many-Facet Rasch Measurement, Thomas Eckes Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr. Statistical Analyses for Language Testers, Rita Green
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Journal of Applied Measurement Rasch models for measurement, David Andrich Constructing Measures, Mark Wilson Rasch Analysis in the Human Sciences, Boone, Stave, Yale
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