Models vs. Experts #1: College GPA predictions

Models vs. Experts #1: College GPA predictions

May 2, 2013 Research Insights, Behavioral Finance
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(Last Updated On: January 18, 2017)

Predictive efficiency of two multivariate statistical techniques in comparison with clinical predictions

  • Alexakos, C.E. Journal of Educational Psychology, 57, 207-306
  • A version of the paper can be found here.
  • Want a summary of academic papers with alpha? Check out our free Academic Alpha Database!

Abstract:

The college grade-point average of academically superior high school students was predicted by statistical methods (regression and discriminant analyses) on the basis of 19 independent variables, and by clinical counselors on the basis of all information collected through counseling interviews and testing during the 4 high school years. The obtained results indicated that the statistical were slightly superior to the clinical predictions; however, both types of prediction were not as efficient as one would wish them to be. Weighed combinations of the 3 methods increased the efficiency by 37% in the least favorable case and by 106% in the most favorable. It was concluded that combinations of clinical-statistical predictions could be more efficient and more useful in counseling college-oriented students than any single method.

Prediction:

Prediction of 2nd year college GPA for high school students. The prediction variables and model estimates are as follows:

var
[Click to enlarge] The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Alpha Highlight:

There are many interesting tests in this paper. The table below highlights the key results: machines were better at identifying high school students that will perform poorly in college, but humans were better at predicting the students that will excel in college. The authors also find evidence that machines are better at predicting results for female students (across all GPA bands), but do worse for male students.

tabl1
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Strategy Summary:

This paper also shows that combining human and machine estimates can improve performance.

These results conflict with prior research by Meehl: “The clinicians systematically over-predicted grade average.”

Thoughts on the paper?


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Definitions of common statistics used in our analysis are available here (towards the bottom)




About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.


  • Tian

    It is an interesting research case for Behavioral Science! We can also apply the clinical-statistical predictions to the Stock markets. It could happen that different investors (Male/Female, Rational/Irrational ) should be predicted by different method to improve the prediction efficiency.