Statistics: Value-Added Models

Jennifer E. Broatch, Sarah E. Manski, Jennifer L. Green

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

An ongoing concern for researchers, policy makers, and educators is the contribution to student achievement of educational inputs such as individual schools and teachers, interventions, teaching practices, and school policies. Value-added modeling estimates such effects from longitudinal student achievement data. This article provides an overview of Value-Added Models (VAMs) by describing representative examples of econometric, statistical, and alternative approaches and their essential features. It also discusses the concerns that value-added modeling estimates may be biased or lack sufficient stability and precision to support desired inferences.

Original languageEnglish (US)
Title of host publicationInternational Encyclopedia of Education
Subtitle of host publicationFourth Edition
PublisherElsevier
Pages390-396
Number of pages7
ISBN (Electronic)9780128186299
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

Keywords

  • Bias
  • Cross-classified models
  • Fixed effects models
  • Hierarchical linear models
  • Layered model
  • Longitudinal data
  • Precision
  • School effects
  • Teacher effects
  • Teacher evaluation systems
  • Variable persistence model

ASJC Scopus subject areas

  • Social Sciences(all)

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