LSAC National Longitudinal Bar Passage Study. LSAC Research Report Series.

By Linda F. Wightman. 1998

Subfields: Statistics and Probability, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

In Social Sciences & Humanities

Citations in the corpus

The Capacity for Moral Self-Correction in Large Language Models (2023, methodology): “The dataset is based on a survey administered by the Law School Admission Council to 27K students across 163 law schools in the United States between the years 1991-1997 [63].”
Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “Law school : Law School Admissions Councils National Longitudinal Bar Passage Study (Wightman, 1998) has 20,649 examples.”
An Empirical Study of Rich Subgroup Fairness for Machine Learning (2018, methodology): “We ran experiments on 3 datasets from the UCI Machine Learning Repository Dheeru and Karra Taniskidou [2017]: Communities and Crime [Redmond and Baveja, 2002], Adult, and Student [Cortez and Silva, 2008], and the Law School dataset from the Law School Admission Council’s National Longitudinal Bar Pa”
Getting a CLUE: A Method for Explaining Uncertainty Estimates (2020, methodology): “We validate CLUE on LSAT academic performance regression (Wightman et al., 1998), UCI Wine quality regression, UCI Credit classiﬁcation (Dua & Graff, 2017), a 7 feature variant of COMPAS recidivism classiﬁcation (Angwin et al.)”
A Reductions Approach to Fair Classification (2018, background): “• Law School Admissions Council’s National Longitudinal Bar Passage Study (Wightman, 1998) (20,649 examples).”
Affirmative Action and the Quality-Fit Tradeoff (2016, unknown): “For example, Wightman (1998, p. 70) shows that African American and white students had similar beliefs regarding their law-school class rank.10 But, as we will see in section 4, African American students have significantly lower grades in law school than their white counterparts.”
Pairwise Fairness for Ranking and Regression (2019, background): “Law School: This dataset [Wightman, 1998] contains details of 27,234 law school students, and we predict the undergraduate GPA for a student from the student’s LSAT score, family income, full-time status, race, gender and the law school cluster the student belongs to, with gender as the protected…”
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding (2019, methodology): “Our data comes from law school students in the US (Wightman, 1998).”
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness (2017, background): “4.1 Fairly predicting law grades We begin by investigating a dataset of survey results across 163 U.S. law schools conducted by the Law School Admission Council [19] .”
Avoiding Resentment Via Monotonic Fairness (2019, unknown): “Law school admissions data [40]: This dataset contains data from 9800 male and 7600 female law school students(4) from 1991, with an outcome variable of normalized first year average (ZFYA) grades in law school and non-protected attributes of undergraduate grade”
A survey on datasets for fairness‐aware machine learning (2021, unknown): “The Law school dataset (Wightman, 1998) Protected attributes: In the literature, race (Bechavod & Ligett, 2017; Lahoti et al., 2020; Russell, Kusner, Loftus, & Silva, 2017; Kusner, Loftus, Russell, & Silva, 2017; Chzhen, Denis, Hebiri, Oneto, & Pontil, 2020; Kearns et al., 2019; Ruoss, Balunovic,…”