Mean Convergence, Combinatorics, and Grade-Point Averages

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IZA Discussion Papers ; No. 15414

Klassifikation und Themen

• 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

Beteiligte, Orts- und Zeitangaben

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

Weitere Informationen

• Handle: http://hdl.handle.net/10419/263630
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ