Socioeconomic status and college performance prediction among minority students

Interaction between SES and College Performance

Zwick, R. & Himelfarb, I. (2011). The effect of high school socioeconomic status on the predictive validity of SAT scores and high school grade-point average. Journal of Educational Measurement, 48(2), 101-121.

African-American (AA) and Latino students underperform other racial groups during their first year of college if SAT scores are used in the prediction formula. The reasons for this are unknown, although socioeconomic status (SES) is suspected.

The current study was undertaken to evaluate whether an SES index could improve the predictive performance of a formula incorporating high school grade-point averages (HSGPAs) and SAT scores.

Objectives

Evaluate the value of including a SES correction factor in the formula used for predicting first-year college grade-point average (FGPA) performance, for the purpose of correcting for errors introduced by the suspected racially-insensitive HSGPAs. The predictive value of the SAT score in relation to SES will also be evaluated. The overall goal is to develop a formula that more accurately predicts FGPA scores.

Procedures

The data from 34 U.S. universities and colleges, with at least five feeder high schools, were retrieved from a number of sources, including the College Board, universities and colleges, and high schools. The College Board, the organization funding this study, matched student records to the data and then de-identified the information. The number of public high school students that were eventually included was 70,712 from 5,702 schools. Regression analysis was performed to identify the key variables that could accurately predict FGPA performance.

Findings

Three regression models were tested: (1) self-reported HSGPA only, (2) model 1 plus SAT scores, and (3) models 1 and 2 plus high school SES. The contributions to FGPA in model 3 were as follows: 0.31 for HSGPA, 0.14 for SAT (writing), 0.11 for SES, and 0.6 for SAT creative writing and math. SES as a predictor tended to overestimate and underestimate FGPA performance for students from low-SES and high-SES schools, respectively, except for AA students who were overestimated in both cases. The inclusion of SAT scores in model 2 and an SES index in model 3 rendered the prediction of FGPA performance more accurate, regardless of racial identity; however, prediction error for AA students from high-SES schools actually worsened. The authors of this study also compared the predictive value of self-reported HSGPAs obtained from SAT applications vs. transcripts and discovered that the latter rendered all three prediction models more accurate; however the overall prediction pattern was unchanged.

Summary

Combining HSGPAs, SAT scores, and an SES index produces the most accurate prediction formula for FGPA performance. If HSGPAs are obtained from official transcripts, then accuracy increased for all groups. The only exceptions are AA students from high-SES schools, who tend to underperform regardless of the prediction model used.

Conclusions

The accuracy of predicting first year academic performance at the college level can be improved substantially by combining official HSGPAs with SAT scores and an SES index.

Recommendations

Model 3 should be used by college admissions officers to select the students most likely to succeed in college. In addition, these finding may help researchers and educators become more focused on developing interventions that could minimize academic underperformance associated with poverty and racial disparities.

List of References

Betts, J.R., Rueben, K.S., & Danenberg, A. (2000). Equal resources, equal outcomes? The distribution of school resources and student achievement in California. San Francisco: Public Policy Institute of California.

Bowen, W.G., & Bok, D. (1998). The shape of the river: Long-term consequences of considering race in college and university admissions. Princeton, NJ: Princeton University Press.

Bridgeman, B., Burton, N., & Pollack, J. (2008). Predicting grades in college courses: A comparison of multiple regression and percent succeeding approaches. The Journal of College Admission, 199, 19 -- 25.

Carnegie Foundation for the Advancement of Teaching (2008). Carnegie Classifications Data File. Retrieved March 18, 2009 from http://www.carnegiefoundation.org/classifications/index.asp?key=809

Cleary, T.A. (1968). Test bias: Prediction of grades of Negro and White students in integrated colleges. Journal of Educational Measurement, 5, 115 -- 124.

College Entrance Examination Board (2006). College-bound seniors. Retrieved February 2, 2010 from http://professionals.collegeboard.com/data-reports-research/sat/archived/2006.

Crouse, J., & Trusheim, D. (1988). The case against the SAT. Chicago, IL: University of Chicago Press.

Geiser, S., & Santelices, M.V. (2007). Validity of high-school grades in predicting student success beyond the freshman year. (Research & Occasional Paper Series: CSHE.6.07.) Berkeley: University of California Center for Studies in Higher Education.

Geiser, S., & Studley, R. (2004). UC and the SAT: Predictive validity and differential impact of the SAT and SAT II at the University of California. In R. Zwick (Ed.), Rethinking the SAT: The future of standardized testing in university admissions (pp. 125 -- 153). New York, NY: RoutledgeFalmer.

Linn, R.L. (1966). Grade adjustments for prediction of academic performance: A review. Journal of Educational Measurement, 3, 313 -- 329.

Linn, R.L. (1983). Predictive bias as an artifact of selection procedures. In H. Wainer & S. Messick (Eds.), Principles of modern psychological measurement: A Festschrift for Frederic M. Lord (pp. 27 -- 40). Hillsdale, NJ: Erlbaum.

Mattern, K.D., Patterson, B.F., Shaw, E.J., Kobrin, J.L., & Barbuti, S.M. (2008). Differential validity and prediction of the SAT (College Board Research Report No. 2008-4). New York, NY: The College Board.

Pike, G.R., & Saupe, J.L. (2002). Does high school matter? An analysis of three methods of predicting first-year grades. Research in Higher Education, 43, 187 -- 207.

Rothstein, J.M. (2005, April). SAT scores, high schools, and collegiate performance predictions. Presented at the meeting of the National Council on Measurement in Education, Montreal.

Snedecor, G.W., & Cochran, W.G. (1967). Statistical methods (6th ed.). Ames: The Iowa State University Press.

Wainer, H., & Brown, L.M. (2007). Three statistical paradoxes in the interpretation of group differences: Illustrated with medical school admission and licensing data. In C.R. Rao and S. Sinharay (Eds.), Handbook of statistics Vol. 26: Psychometrics (pp. 893 -- 918). Amsterdam, The Netherlands: North-Holland.

Young, J.W. (2004). Differential validity and prediction: Race and sex differences in college admissions testing. In R. Zwick (Ed.), Rethinking the SAT: The future of standardized testing in university admissions (pp. 289 -- 301). New York, NY: RoutledgeFalmer.

Young, J.W., & Johnson, P.M. (2004). The impact of an SES-based model on a college's undergraduate admissions outcomes. Research in Higher Education, 45, 777 -- 797.

Zwick, R. (2002). Fair game? The use of standardized admissions tests in higher education. New York, NY: RoutledgeFalmer.

Zwick, R., & Green, J.G. (2007). New perspectives on the correlation of SAT scores, high school grades, and socioeconomic factors. Journal of Educational Measurement, 44, 23 -- 45.

Zwick, R., & Himelfarb, I. (2009, April). The effect of high school quality on the predictive validity of SAT scores and high school grade-point average. Presented by I. Himelfarb at the Annual Meeting of the American Educational Research Association, San Diego.

Zwick, R., & Schlemer, L. (2004). SAT validity for linguistic minorities at the University of California, Santa Barbara. Educational Measurement: Issues and Practice, 25(2), 6 -- 16.