Detecting bias in admissions to graduate and
professional schools presents important problems to
the data analyst. In this paper some traditionally used
methods, such as multiple regression analysis, are
compared with the newer methods of logistic regression
and structural equations models. The problems
faced in modeling decision rules in this situation are
(1) a dichotomous dependent variable, (2) nonlinear
relationships between independent variables and the
probability of being admitted, (3) omitted variables,
and (4) errors in variables. Each method used involves
an attempt to solve one or more of these problems,
but each has its own drawbacks. Using multiple methods,
and finding several areas of agreement in the results
among the methods, makes the conclusions
stronger than had only one method been used.
Rindskopf, David & Everson, Howard. (1984). A comparison of models for detecting discrimination: An example from medical school admissions. Applied Psychological Measurement, 8, 89-106. doi:10.1177/014662168400800110
Rindskopf, David; Everson, Howard.
A comparison of models for detecting discrimination: An example from medical school admissions.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.