RealVAMS: An R package for fitting a multivariate value-added model (VAM)

Jennifer Broatch, Jennifer Green, Andrew Karl

Research output: Contribution to journalArticle

Abstract

We present RealVAMS, an R package for fitting a generalized linear mixed model to multimembership data with partially crossed and partially nested random effects. RealVAMS utilizes a multivariate generalized linear mixed model with pseudo-likelihood approximation for fitting normally distributed continuous response(s) jointly with a binary outcome. In an educational context, the model is referred to as a multidimensional value-added model, which extends previous theory to estimate the relationships between potential teacher contributions toward different student outcomes and to allow the consideration of a binary, real-world outcome such as graduation. The simultaneous joint modeling of continuous and binary outcomes was not available prior to RealVAMS due to computational difficulties. In this paper, we discuss the multidimensional model, describe RealVAMS, and demonstrate the use of this package and its modeling options with an educational data set.

Original languageEnglish (US)
Pages (from-to)22-30
Number of pages9
JournalR Journal
Volume10
Issue number1
StatePublished - Jul 1 2018

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Binary Outcomes
Generalized Linear Mixed Model
Joint Modeling
Pseudo-likelihood
Multidimensional Model
Random Effects
Binary
Approximation
Modeling
Model
Estimate
Demonstrate
Education
Value added
Students
Generalized linear mixed model
Context
Relationships

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Cite this

RealVAMS : An R package for fitting a multivariate value-added model (VAM). / Broatch, Jennifer; Green, Jennifer; Karl, Andrew.

In: R Journal, Vol. 10, No. 1, 01.07.2018, p. 22-30.

Research output: Contribution to journalArticle

Broatch, Jennifer ; Green, Jennifer ; Karl, Andrew. / RealVAMS : An R package for fitting a multivariate value-added model (VAM). In: R Journal. 2018 ; Vol. 10, No. 1. pp. 22-30.
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