Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling

Description

An EM algorithm (Karl et al. 2013) is used to estimate the generalized, variable, and complete persistence models (Mariano et al. 2010).

Details

Author(s)

Andrew Karl, Yan Yang, and Sharon Lohr

Maintainer: Andrew Karl <akarl@asu.edu>

References

Karl, A., Yang, Y. and Lohr, S. (2013) Efficient Maximum Likelihood Estimation of Multiple Membership Linear Mixed Models, with an Application to Educational Value-Added Assessments Computational Statistics & Data Analysis 59, 13–27.

Karl, A., Yang, Y. and Lohr, S. (2014) Computation of Maximum Likelihood Estimates for Multiresponse Generalized Linear Mixed Models with Non-nested, Correlated Random Effects Computational Statistics & Data Analysis 73, 146–162.

Karl, A., Yang, Y. and Lohr, S. (2014) A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects Journal of Educational and Behavioral Statistics 38, 577–603.

Lockwood, J., McCaffrey, D., Mariano, L., Setodji, C. (2007) Bayesian Methods for Scalable Multivariate Value-Added Assesment. Journal of Educational and Behavioral Statistics 32, 125–150.

Mariano, L., McCaffrey, D. and Lockwood, J. (2010) A Model for Teacher Effects From Longitudinal Data Without Assuming Vertical Scaling. Journal of Educational and Behavioral Statistics 35, 253–279.

McCaffrey, D. and Lockwood, J. (2011) Missing Data in Value-Added Modeling of Teavher Effects, Annals of Applied Statistics 5, 773–797

Results