All reports in LSAC’s Research Library are available upon request. Executive summaries are available below for the latest LSAT Technical Reports and other research published within the last 10 years.
Current Research:
The Law School Admission Council (LSAC) 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, LSAC also conducts differential validity and differential prediction studies on the LSAT to ensure that the test is fair across gender subgroups.
The Law School Admission Council (LSAC) 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, LSAC also conducts differential validity and differential prediction studies on the LSAT to ensure that the test is fair across racial/ethnic subgroups.
Among the assumptions that should be met when applying an item response theory (IRT) model to the analysis of test data is measurement invariance. Measurement invariance requires that, after controlling for a test taker’s proficiency, group membership have no effect on the probability that that test taker will answer a test question correctly. Groups may be defined on the basis of many factors, including gender, race/ethnicity, and citizenship.
The statistical theory of estimating and testing item response theory (IRT) models for items (questions) with discrete (correct or incorrect) responses has been thoroughly developed (recall that IRT is a mathematical model that is typically used to analyze test data). In contrast, the theory for IRT models for items with continuous responses has hardly received any attention. This omission is mainly due to the fact that, so far, the continuous response format has hardly been used by the testing industry.
In this report we present a measure to identify unlikely patterns of correct/incorrect answers to test questions (commonly referred to as items). Some examples of why such patterns may occur include the misinterpretation of questions, item preknowledge, answer copying, or guessing behavior. The proposed measure is the probability of exceedance (PE). PE provides information about the probability of a correct/incorrect answer pattern, conditional on the test taker’s total score. Although this concept is not new, it is hardly if ever applied in practice.
This study was conducted to evaluate the predictive validity of each of the current Law School Admission Test (LSAT) item types as well as the interrelationships among them. The current LSAT consists of three item types: Analytical Reasoning (AR), Logical Reasoning (LR), and Reading Comprehension (RC). Even though the correlation of overall LSAT scaled score with first-year average (FYA) in law school is examined on a regular basis at the Law School Admission Council (LSAC), the separate correlations for each of these three item types have only rarely been studied.
In the analysis of data for the Law School Admission Test (LSAT) and other similar standardized tests, a mathematical model called item response theory (IRT) is commonly used to estimate both the characteristics of the test questions (items) and the ability level of the test takers. Such analyses are based on the test takers’ correct and incorrect responses to the test items.