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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.

Automated methods have been developed for assembling test forms, evaluating a pool of test questions (i.e., items) to determine the number of test form assemblies it can support, and designing an item pool that can most efficiently support the test form assembly process. Automated methods have greatly maintained and improved such activities, all of which are essential to the support of every testing program. This report reviews the major approaches that have been applied in the development of these methods.

The problems of item pool analysis and design are the subject of many recent studies. The rationale for this type of research is to increase the usability of existing item pools and to decrease the cost of designing new items. Clearly these are crucial problems for all testing agencies.

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.

Many standardized tests are now administered via computer rather than paper and pencil. The computer-based delivery mode brings with it certain advantages, such as the ability to record not only the test taker’s response to each item (i.e., question), but also the amount of time the test taker spends considering and answering each item. The analysis of response times (RTs) is still a developing area of research.

A mathematical model called item response theory is often applied to high-stakes tests to estimate test-taker ability level and to determine the characteristics of test questions (i.e., items). Often, these tests contain subsets of items (testlets) grouped around a common stimulus. This grouping often leads to items within one testlet being more strongly correlated among themselves than among items from other testlets, which can result in moderate to strong testlet effects.

Text similarity measurement provides a rich source of information and is increasingly being used in the development of new educational and psychological applications. However, due to the high-stakes nature of educational and psychological testing, it is imperative that a text similarity measure be stable (or robust) to avoid uncertainty in the data. The present research was sparked by this requirement. First, multiple sources of uncertainty that may affect the computation of semantic similarity between two texts are enumerated.

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 Law School Admission Council (¾«¶«Ó°Òµ) has carried out annual predictive validity studies, also called LSAT Correlation Studies, since the Law School Admission Test (LSAT) was first administered. These studies are geared toward evaluating and ensuring the effectiveness and validity of LSAT scores for use in the law school admission process. In conjunction with these predictive validity studies, ¾«¶«Ó°Òµ also conducts differential validity and differential prediction studies on the LSAT to ensure that the test is fair across gender subgroups.