Arbeitspapier
Mean Convergence, Combinatorics, and Grade-Point Averages
While comparing students across large differences in GPA follows one's intuition that higher GPAs correlate positively with higher-performing students, this need not be the case locally. Grade-point averaging is fundamentally a combinatorics problem, and thereby challenges inference based on local comparisons—this is especially true when students have experienced only small numbers of classes. While the effect of combinatorics diminishes in larger numbers of classes, mean convergence then has us jeopardize local comparability as GPA better delineates students of different ability. Given these two characteristics in decoding GPA, we discuss the advantages of machine-learning approaches to identifying treatment in educational settings.
- Language
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Englisch
- Bibliographic citation
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Series: IZA Discussion Papers ; No. 15414
- Classification
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Wirtschaft
Analysis of Education
Returns to Education
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Subject
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GPA
grades
program evaluation
random forest
regression discontinuity
- Event
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Geistige Schöpfung
- (who)
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Waddell, Glen R.
McDonough, Robert
- Event
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Veröffentlichung
- (who)
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Institute of Labor Economics (IZA)
- (where)
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Bonn
- (when)
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2022
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Waddell, Glen R.
- McDonough, Robert
- Institute of Labor Economics (IZA)
Time of origin
- 2022