Research Reports

Combinatorial Search Algorithm for Detection of Test Collusion (RR 13-01)

This report presents a new algorithm for detecting groups of test takers (aberrant groups) who had access to subsets of test questions (aberrant subsets) prior to an exam. This method is in line with the development of statistical methods for detecting test collusion, a new research direction in test security. Test collusion may be described as the large-scale sharing of test materials, including answers to test questions. The algorithm employs several new statistics to perform a sequence of statistical tests to identify aberrant groups. The algorithm is flexible and can be easily modified to detect other types of test collusion. It can also be applied within all major modes of testing: paper-and-pencil testing, computer-based testing, multiple-stage testing, and computerized adaptive testing. A simulation study demonstrates the advantages of using the algorithm in computerized adaptive testing.

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Evidence to Support Validity Claims for Using LSAT Scores...

Law School Admission Test (LSAT) scores provide a standard measure of an applicant’s proficiency in a well-defined set of important skills associated with success in law school coursework. LSAT scores are also a strong predictor of first-year grades (FYG) and cumulative grade point average (CGPA) in law school. The most recent correlational study of LSAT results (2019) shows that LSAT scores are far superior to undergraduate grade point average (UGPA) in predicting FYG...

Understanding and Interpreting Law School Enrollment Data...

The Law School Admission Council (LSAC) has a long-standing commitment to diversity, equity, and inclusion in legal education and in the legal profession. In line with its mission to promote quality, access, and equity in legal education, LSAC is providing this report, Understanding and Interpreting Law School Enrollment Data: A Focus on Race and Ethnicity, to help law schools, admission professionals, and other legal education stakeholders understand how we are measuring who is the pipeline.