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A Foundation of Validity

Beginning with the very first notion of a standardized test for admission to law school, validity was a primary focus. In his May 17, 1945 letter to the College Entrance Examination Board (CEEB) suggesting the development of such a test, Frank H. Bowles, Director of Admissions at Columbia University, stated the seven criteria listed below.

Since the inception of the Law School Admission Test (LSAT), the Law School Admission Council (¾«¶«Ó°Òµ) has sought to evaluate and ensure its validity for use in the law school admission process. As predictive validity is an important component in the overall evaluation of test validity, ¾«¶«Ó°Òµ has carried out predictive validity studies, also called LSAT Correlation Studies, since the test was first administered.

Contextual Information for Holistic Evaluation in Law School Admission

The figure below shows the progression of undergraduate yearly grade-point average (GPA) for law school applicants across 4 years of undergraduate study divided into quintiles based on individual Law School Admission Test (LSAT) scores.

The goal of the Law School Admission Council (¾«¶«Ó°Òµ) Skills Analysis Study is to identify the skills that law school faculty consider important for success in required law school courses. If certain tasks are required of all or most law school required courses, the skills involved in those tasks can be inferred to be essential to success in law school.

Test collusion (TC) is the sharing of test materials or answers to test questions (items) before or during a test. Because of the potentially large advantages for the test takers involved, TC poses a serious threat to the validity of score interpretations. The proposed approach applies graph theory methodology to response similarity analyses to identify groups involved in TC while minimizing the false-positive detection rate. The new approach is illustrated and compared with a recently published method using real and simulated data.

This study employed a data simulation to evaluate the impact of a strategy to reduce test length by including only high-quality test questions, where quality was defined by a statistical indicator of the degree to which a question distinguishes between more and less able test takers. The impact of this strategy on the rank ordering of simulated test takers according to their total test score was evaluated, as was the predictive validity and classification accuracy of scores based on the shorter tests.

This report addresses a general type of cluster aberrancy in which a subgroup of test takers has an unfair advantage on some subset of administered items. Examples of cluster aberrancy include item preknowledge and test collusion. In general, cluster aberrancy is hard to detect due to the multiple unknowns involved: Unknown subgroups of test takers have an unfair advantage on unknown subsets of items. The issue of multiple unknowns makes the detection of cluster aberrancy a challenging problem from the standpoint of applied mathematics.

Most high-stakes testing programs apply methods to identify unlikely patterns of correct/incorrect responses to test questions. Some examples of why such patterns may occur include misinterpretation of questions, question preknowledge, answer copying, or guessing behavior. This report provides an overview of existing approaches to identifying atypical response patterns that fall into a class of analyses known as nonparametric statistics. Results of a simulation study comparing the different approaches, along with guidelines for applying these indices in practice, are also presented.

Since the inception of the Law School Admission Test (LSAT), the Law School Admission Council (¾«¶«Ó°Òµ) has sought to evaluate and ensure its validity for use in the law school admission process. As predictive validity is an important component in the overall evaluation of test validity, ¾«¶«Ó°Òµ has carried out annual predictive validity studies, also called LSAT Correlation Studies, since the test was first administered.