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Research Library

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.

Looking for older reports? Consult the Research Archive

Current Research:

LSAC’s Knowledge Report: The Composition of the First-Year Law School Class and Enrollment 2021-2025 Trends

This report focuses on first-year law school enrollment from 2021 through 2025, highlighting who is enrolling in law school, where they enrolled, and how rates of enrollment of racially and ethnically minoritized students varied across law schools.

The 2024 1L Class: Persistence in the First Year of Law School

This report provides important insights that law schools and stakeholders can use to support student retention and development in law school and beyond.

Funding the First Year: How 2024 1Ls Paid for Law School

What funding sources did the 2024 1L class use to pay for law school? How much debt do they expect to have when they graduate?

LSAC’s Knowledge Report: The 2024 1L Profile

This report focuses on the 2024 1L class, examining who is enrolling in law school, where they enrolled, and how they made their enrollment decision.

LSAC’s Knowledge Report: The Composition of the First-Year Law School Class and Enrollment Trends

This report focuses on first-year law school enrollment from 2021 through 2024, highlighting who is enrolling in law school, where they enrolled, and how rates of enrollment of racially and ethnically minoritized students varied across law schools.

Do I Belong Here? Examining Perceived Experiences of Bias, Stereotype Concerns, and Sense of Belonging in U.S. Law Schools

In this piece published in the Association of American Law Schools’ Journal of Legal Education, LSAC DEI Policy & Research Analyst and Senior Program Manager Elizabeth Bodamer explores the experiences of minoritized students in U.S. law schools.

How Serious Is IRT Misfit for Practical Decision-Making? (RR 15-04)

Item response theory (IRT) is a mathematical model used to support the development, analysis, and scoring of tests and questionnaires. For example, IRT allows for the description of item (i.e., question) characteristics, such as difficulty, as well as the proficiency level of test takers. Various IRT models are available, and choosing the most appropriate model for a particular test is essential. Since the fit of the test data to the chosen model is never perfect, measuring the fit of the model to the data is imperative.

The Effect of Item and Person Misfit on Selection Decisions: An Empirical Study (RR 15-05)

Item response theory (IRT) is a mathematical model that is often applied in the development and analysis of educational and psychological assessments. Various IRT models exist, and practitioners must choose the model that is most appropriate for their particular assessment. Even when the most appropriate model is applied, the fit of the assessment data to the model is rarely perfect in practice. How serious, then, is model misfit for practical decision-making?