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Temporally Dynamic, Cohort-Varying Value-Added Models.

Garritt L Page1, Ernesto San Martín2,3,4, David Torres Irribarra5

  • 1Brigham Young University.

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Summary
This summary is machine-generated.

This study introduces dynamic school value-added estimation to assess school effectiveness persistence. Incorporating temporal dependence improves estimation accuracy, even with weak effects, unlike ignoring it which reduces efficiency.

Keywords:
School value persistenceTemporal dependenceValue-added models

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Area of Science:

  • Educational Measurement
  • Statistics
  • Econometrics

Background:

  • School performance often exhibits year-to-year temporal dependence.
  • Accurate estimation of school value-added is crucial for accountability and improvement.
  • Existing value-added models may not fully capture dynamic school effectiveness.

Purpose of the Study:

  • To dynamically estimate school value-added over time.
  • To establish and quantify school effectiveness persistence.
  • To account for temporal dependence in school performance data.

Main Methods:

  • Modeling random school effects using an auto-regressive process.
  • Incorporating cohort-to-cohort performance dependence in value-added estimators.
  • Conducting identification analysis to clarify value-added indicator meanings.

Main Results:

  • Two distinct methods for incorporating temporal dependence in value-added models are proposed.
  • Simulations demonstrate that ignoring temporal dependence reduces estimation efficiency.
  • Incorporating temporal dependence improves value-added estimation, even when weak.

Conclusions:

  • The proposed models offer valuable tools for monitoring specific aspects of school persistence.
  • Dynamic value-added estimation provides a more nuanced understanding of school effectiveness over time.
  • Methodology illustrated using Chilean national mathematics test data.