# A Monte Carlo Investigation of the Contrasting Groups Standard Setting Method.

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The contrasting groups method is one of many possible methods for setting passing scores. The most commonly used method is probably that developed by W. H. Angoff (1971), but it has been suggested that the Angoff method may not be appropriate for many standard setting applications in education. The contrasting groups method is explored as an alternative for educational research. To implement the contrasting groups method, experts are asked to make a dichotomous judgment about examinees, usually in the form of master/nonmaster, competent/not competent, certify/deny, and so on. All judged examines then take a test covering the content area of the domain of interest. This process results in two distributions of test scores, one for the group judged masters and one for the group judged nonmasters. These two distributions can be examined and used to derive a cutting score for the examination which is then applied to examinees who take the test for whom expert judgments of mastery are not available. In this study the contrasting groups graphing procedure was used in conjunction with various combinations of population and standard setting characteristics to examine the conditions under which it most reliably captures a known standard. A Monte Carlo approach was used to simulate and analyze populations with differing distributional forms, different percentages of master and nonmasters, various sample sizes, differing sampling strategies, and varying judge error rates. Overall, findings produced suggestions that the contrasting groups graphing procedure can be applied confidently to estimate a "true" cutting score in a variety of applications. Best sampling practices are discussed, and limitations of the expert criterion judgments are reviewed. (Contains 1 table, 8 figures, and 23 references.) (SLD)

Descriptors: Cutting Scores, Educational Research, Educational Testing, Judges, Monte Carlo Methods, Pass Fail Grading, Sample Size, Sampling, Simulation, Standard Setting (Scoring), Standards, True Scores

Autor: Cizek, Gregory J.; Husband, Timothy H.

Fuente: https://eric.ed.gov/?q=a&ft=on&ff1=dtySince_1992&pg=8788&id=ED408342