Analysis of Differential Prediction of Law School Performance by Gender Subgroups Based on the 1996–1998 Entering Law School Classes (TR-00-03)
by Jennifer R. Duffy and Louis A. Roussos

Executive Summary

In the law school admission process, it is essential that the criteria used for admission are fair to all subgroups in the applicant population. One method used to evaluate the fairness of the admission process is to compare the predicted and actual first-year grade-point averages (FYAs) at each law school for various subgroups. The purpose of the current study is to address questions of differential prediction between male and female first-year law school students based on data from the 1996, 1997, and 1998 entering classes of 167 law schools.

Statistical regression analyses were used to predict first-year average using three traditional predictors: undergraduate grade-point average (UGPA), scaled score on the Law School Admission Test (LSAT ), and the best predictive linear combination of UGPA and LSAT score. A separate analysis was conducted for each law school included in the study.

The regression model with both LSAT score and UGPA as the predictors resulted in the least amount of differential prediction between male and female students. When UGPA was used as the sole predictor variable, FYA tended to be underpredicted on average for male students and overpredicted on average for female students. When LSAT alone was used to predict FYA, some underprediction of female FYA mean and some overprediction of male FYA mean was observed. Differential prediction was reduced when LSAT and UGPA were used in combination to predict FYA. For all three of the regression models, the magnitude of the over- and underprediction that did occur was quite small. The differences between predicted and actual FYA means were usually within one point of zero (i.e., one tenth of a standard deviation); this was most likely to be true when the model using LSAT score and UGPA as predictors was used. (In this study, FYA was scaled to have a mean of 50 and a standard deviation of 10.) The results of this study do not support the concern that the use of LSAT scores or the traditional combination of LSAT scores and UGPAs result in unfair admission decisions with regard to gender subgroups.

While considering the results of this study, the reader should keep in mind that they refer only to subgroup behavior and not to individuals. For example, while results may suggest that UGPAs alone may overpredict FYAs for female law students on average, the performance of many individual female law students will be underpredicted based solely on their UGPAs.


Analysis of Differential Prediction of Law School Performance by Gender Subgroups Based on the 1996–1998 Entering Law School Classes (TR-00-03)

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