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.

Sprache
Englisch

Erschienen in
Series: IZA Discussion Papers ; No. 15414

Klassifikation
Wirtschaft
Analysis of Education
Returns to Education
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Thema
GPA
grades
program evaluation
random forest
regression discontinuity

Ereignis
Geistige Schöpfung
(wer)
Waddell, Glen R.
McDonough, Robert
Ereignis
Veröffentlichung
(wer)
Institute of Labor Economics (IZA)
(wo)
Bonn
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Waddell, Glen R.
  • McDonough, Robert
  • Institute of Labor Economics (IZA)

Entstanden

  • 2022

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