TEST BIAS: VALIDITY OF THE SCHOLASTIC APTITUDE TEST FOR NEGRO AND WHITE STUDENTS IN INTEGRATED COLLEGES
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For this research, a test was said to be biased for members of a subgroup of the population if, in the prediction of a criterion for which the test was designed, consistent nonzero errors of prediction are made for members of the subgroup. Samples of Negro and white students from three integrated colleges were studied. In the two eastern colleges, no significant differences in the regression lines were found. In the one college in the southwest, significant differences were found, but it was the Negro scores which were overpredicted. Thus, in one of the three schools, the Scholastic Aptitude Test was found to be slightly biased, but biased in favor of the Negro student.
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The general notions from early fairness literature transfer to (binary) classification settings and define essential concepts such as protected/demographic variables, notions of group vs individual fairness, impossibility conditions between fairness conditions, and metric-based fairness quantification.
“However, the general notions transfer to (binary) classiication settings and thus deine essential concepts such as protected/demographic variables (e.g.[72,73]), notions of group vs individual fairness (e.g.[257,278]), impossibility conditions between fairness conditions ( [81]), and metric-based fairness quantiication (e.g., true positive rates, [74]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [72]
Two initial foci of fairness research were unfairness of standardized tests in higher education contexts and discrimination in employment contexts.
“Two initial foci of fairness research were unfairness of standardized tests in higher education contexts [72,73] and discrimination in employment contexts [133].”
From Fairness in machine learning: A survey (2024) · cites this paper as [72]
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Cites
THE USE OF SPECIAL MATRIX OPERATORS IN STATISTICAL CALCULUS
Beaton, A. E. (1964) · ETS Research Bulletin Series · doi:10.1002/j.2333-8504.1964.tb00689.x · Cited by 102 · see citing works
The Negro student at integrated colleges
Clark, K. B., & Plotkin, L. (1963) · Cited by 46 · see citing works
PREDICTION OF COLLEGE GRADES FOR ALL PUBLIC COLLEGES OF A STATE
Hills, J. R. (1964) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1964.tb00175.x · Cited by 23 · see citing works
SOME IMPLICATIONS OF THE INTELLECTUAL CHARACTERISTICS OF FRESHMEN ENTERING A LIBERAL ARTS COLLEGE1
McKelpin, J. P. (1965) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1965.tb00411.x · Cited by 20 · see citing works
TESTING OF CULTURALLY DIFFERENT GROUPS
Campbell, J. T. (1964) · ETS Research Bulletin Series · doi:10.1002/j.2333-8504.1964.tb00506.x · Cited by 10 · see citing works
Biaggio, A. M. B. (1966) · Cited by 9 · see citing works
AN INVESTIGATION OF ITEM BIAS1
Cleary, T. A., & Hilton, T. L. (1966) · ETS Research Bulletin Series · doi:10.1002/j.2333-8504.1966.tb00355.x · Cited by 7 · see citing works
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Research works printed but not linked: 1
- Princeton, N.J.: Educational Testing Service, 1957. 23338504, 1966, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/j.2333-8504.1966.tb00529.x, Wiley Online Library on [31/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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Algorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687 · see citing works
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
ROTH, P. L., BEVIER, C. A., BOBKO, P., SWITZER, F. S., & TYLER, P. (2001) · Personnel Psychology · doi:10.1111/j.1744-6570.2001.tb00094.x · Cited by 325 · see citing works
50 Years of Test (Un)fairness: Lessons for Machine Learning
Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287600 · Cited by 297 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
- Authors
- Cleary, T. A.
- Affiliations
- Developmental Research Division, ETS
- Corresponding author
- Cleary, T. Anne
- Venue
- ETS Research Bulletin Series
- Publisher
- Wiley
- Published
- 1966-12
- Volume
- 1966
- Issue
- 2
- ISSN
- 0424-6144 (ISSN-L)
- 2333-8504
- OpenAlex
- W2002771657
- Cited by
- 37
- References
- 7
- Citations by year
- 2025: 1 · 2024: 15 · 2023: 4 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 2 · 2013: 2
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ETS Research Bulletin Series (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Sociology and Political Science › Critical Race Theory in Education
- Social Sciences › Education › Higher Education Research Studies
- Keywords
- test bias, Scholastic Aptitude Test, white students, criterion validity, prediction error, college admissions testing
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