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Selection Bias in College Admissions Test Scores

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  • Rothstein, J
Abstract
Data from the two leading college admissions tests—the SAT and the ACT—can provide a valuable measures of student achievement, but bias due to the non-representativeness of test takers is an important concern. We take advantage of a policy reform in Illinois that made the ACT a graduation requirement to identify the within- and across-school selectivity of ACT takers. In contrast to cross-sectional or time-differenced estimates, estimates based on the Illinois policy change indicate substantial positive selection into test participation both across and within schools. Despite this, school-level averages of observed scores are extremely highly correlated with average latent scores, as noise introduced by across-school variation in sample selectivity is small relative to the underlying signal. As a result, in most contexts the use of observed school mean test scores in place of latent means understates the degree of between school variation in average achievement but is otherwise unlikely to lead to misleading conclusions.
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Suggested Citation

  • Rothstein, J, 2023. "Selection Bias in College Admissions Test Scores," Department of Economics, Working Paper Series qt8w74088z, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    • Handle: RePEc:cdl:econwp:qt8w74088z
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    References listed on IDEAS

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    1. Jacob L. Vigdor & Charles T. Clotfelter, 2003. "Retaking the SAT," Journal of Human Resources, University of Wisconsin Press, vol. 38(1).
    2. Card, David & Payne, A. Abigail, 2002. "School finance reform, the distribution of school spending, and the distribution of student test scores," Journal of Public Economics, Elsevier, vol. 83(1), pages 49-82, January.
    3. David Card & Alan B. Krueger, 2005. "Would the Elimination of Affirmative Action Affect Highly Qualified Minority Applicants? Evidence from California and Texas," ILR Review, Cornell University, ILR School, vol. 58(3), pages 416-434, April.
    4. Jesse M. Rothstein, 2006. "Good Principals or Good Peers? Parental Valuation of School Characteristics, Tiebout Equilibrium, and the Incentive Effects of Competition among Jurisdictions," American Economic Review, American Economic Association, vol. 96(4), pages 1333-1350, September.
    5. Katharine G. Abraham & Melissa A. Clark, 2006. "Financial Aid and Students’ College Decisions: Evidence from the District of Columbia Tuition Assistance Grant Program," Journal of Human Resources, University of Wisconsin Press, vol. 41(3).
    6. Krueger, Alan B & Whitmore, Diane M, 2001. "The Effect of Attending a Small Class in the Early Grades on College-Test Taking and Middle School Test Results: Evidence from Project STAR," Economic Journal, Royal Economic Society, vol. 111(468), pages 1-28, January.
    7. Brian A. Jacob & Steven D. Levitt, 2003. "Rotten Apples: An Investigation of the Prevalence and Predictors of Teacher Cheating," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 843-877.
    8. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
    9. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    10. Dynarski, Mark & Gleason, Philip, 1993. "Using scholastic aptitude test scores as indicators of state educational performance," Economics of Education Review, Elsevier, vol. 12(3), pages 203-211, September.
    11. Dynarski, Mark, 1987. "The Scholastic Aptitude Test: Participation and performance," Economics of Education Review, Elsevier, vol. 6(3), pages 263-273, June.
    12. Gronau, Reuben, 1974. "Wage Comparisons-A Selectivity Bias," Journal of Political Economy, University of Chicago Press, vol. 82(6), pages 1119-1143, Nov.-Dec..
    13. Daniel M. Koretz, 2002. "Limitations in the Use of Achievement Tests as Measures of Educators' Productivity," Journal of Human Resources, University of Wisconsin Press, vol. 37(4), pages 752-777.
    14. Eric A. Hanushek & Lori L. Taylor, 1990. "Alternative Assessments of the Performance of Schools: Measurement of State Variations in Achievement," Journal of Human Resources, University of Wisconsin Press, vol. 25(2), pages 179-201.
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    Cited by:

    1. Goodman, Joshua & Hurwitz, Michael & Smith, Jonathan & Fox, Julia, 2015. "The relationship between siblings’ college choices: Evidence from one million SAT-taking families," Economics of Education Review, Elsevier, vol. 48(C), pages 75-85.
    2. Card, David & Rothstein, Jesse, 2007. "Racial segregation and the black-white test score gap," Journal of Public Economics, Elsevier, vol. 91(11-12), pages 2158-2184, December.
    3. Sezgin Polat & Jean-Jacques Paul, 2016. "How to predict university performance: a case study from a prestigious Turkish university?," Investigaciones de Economía de la Educación volume 11, in: José Manuel Cordero Ferrera & Rosa Simancas Rodríguez (ed.), Investigaciones de Economía de la Educación 11, edition 1, volume 11, chapter 22, pages 423-434, Asociación de Economía de la Educación.
    4. Jesse M. Rothstein, 2006. "Good Principals or Good Peers? Parental Valuation of School Characteristics, Tiebout Equilibrium, and the Incentive Effects of Competition among Jurisdictions," American Economic Review, American Economic Association, vol. 96(4), pages 1333-1350, September.
    5. Guyonne Kalb & Sholeh Maani, 2011. "How important are omitted variables, censored scores and self-selection in analysing high-school academic achievement?," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 14(3), pages 307-332.
    6. Kaitlin Anderson & Gema Zamarro & Jennifer Steele & Trey Miller, 2021. "Comparing Performance of Methods to Deal With Differential Attrition in Randomized Experimental Evaluations," Evaluation Review, , vol. 45(1-2), pages 70-104, February.
    7. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    8. Goodman, Joshua & Hurwitz, Michael & Smith, Jonathan & Fox, Julia, 2016. "Reprint of “The relationship between siblings’ college choices: Evidence from one million SAT-taking families”," Economics of Education Review, Elsevier, vol. 51(C), pages 125-135.
    9. Sarena Goodman, 2013. "Learning from the test: raising selective college enrollment by providing information," Finance and Economics Discussion Series 2013-69, Board of Governors of the Federal Reserve System (U.S.).
    10. Raj Aggarwal & Joanne Goodell & John Goodell, 2014. "Culture, Gender, and GMAT Scores: Implications for Corporate Ethics," Journal of Business Ethics, Springer, vol. 123(1), pages 125-143, August.

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    More about this item

    Keywords

    Test Scores; Applied Economics; Economics; Applied economics;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • I20 - Health, Education, and Welfare - - Education - - - General

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