TEST BIAS: PREDICTION OF GRADES OF NEGRO AND WHITE STUDENTS IN INTEGRATED COLLEGES

By T. Cleary. 1968

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Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “For example, since the 1960s, psychometricians have studied the fairness of educational tests based on their ability to predict performance (at school or work) [25, 26, 165, 36, 45, 141, 80, 107, 81].”
50 Years of Test (Un)fairness: Lessons for Machine Learning (2018, background): “first time, cast in terms of a formal model for predicting educational outcomes from test scores [10, 11]:”
An Expected Utility Model for “Optimal” Selection (1976, background): “The paradigm selected is precisely that studied earlier by Cleary (1968), Cole (1973), Darlington (1971), Einhorn and Bass (1971), and Thorndike (1971). Petersen and Novick (1976) have discussed certain technical difficulties in previously proposed solutions within”
The Implicit Fairness Criterion of Unconstrained Learning (2018, methodology): “It was formalized by the Cleary criterion [Cleary, 1968], which compares the slope of regression lines between the test score and the outcome in different groups.”
RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*: RISK, RACE, AND RECIDIVISM (2016, background): “This is commonly tested by examining whether groups systematically deviate from a common regression line that relates test scores to the criterion (Cleary, 1968; see also Sackett and Bobko, 2010).”
A Meta-Analysis of Gender Group Differences for Measures of Job Performance in Field Studies (2012, methodology): “Accurate assessment of the magnitude of any gender differences in the measurement of job performance is also important for the application of human resource models of selection test fairness (Cleary, 1968, see also Bartlett, Bobko, Mosier, & Hannan, 1978).”
Testing for Factorial Invariance in the Context of Construct Validation (2010, methodology): “This perspective on invariance is based on a definition of test bias proposed by Cleary (1968) that states that “a test is biased for members of a subgroup of the population if the criterion score predicted from the common regression line is consistently too high or too low for members of the…”
A Generalized Solution for Approximating the Power to Detect Effects of Categorical Moderator Variables Using Multiple Regression (2001, background): “ity, nonminority) and test scores (e.g., general cognitive ability) on a measure of performance (e.g., supervisory ratings) ( Cleary, 1968 ).”