Analytical Package for Education Trials
eefAnalytics provides both frequentist and Bayesian multilevel models for analyzing education
trials. Multilevel model or random effect model is an "omnibus" model that can be used to
analyse multi-site trials, cluster randomised controlled trials and simple randomised trials
across schools. The model reduces to ordinary linear regression when intra-cluster correlation is zero and
number of pupils is the same in each school . The frequentist method relies on lme4 package and
the implementation of Hedges'effect size based on unequal cluster size for 'within' and 'total' variance (Hedges, 2007).
The Bayesian method relies on the packages geoR for scaled-inverse-Chi-square and mvtnorm for
multivariate Normal distribution. The estimation are based on Gibb's sampling from the
full conditional posterior distributions as discussed by Wang et al. (1993). To guarantee
convergence of the mcmc chains, a minimum of 10,000 iterations is recommended. All parameters are based on vague
priors and in most cases the results should be similar to the frequentist method.
Adetayo Kasim email@example.com
ZhiMin Xiao firstname.lastname@example.org
Steve Higgins email@example.com
G. Verbeke and G. Molenberghs, Linear Mixed Models for Longitudinal Data, Springer, New York, NY, USA, 2000.
Hedges, L. V. (2007). Effect Sizes in Cluster-Randomized Designs. Journal of Educational and Behavioral Statistics, 32 (4), 341-370.
Torgerson, D. J., & Torgerson, C. J. (2008). Designing Randomised Trials in Health, Education and the Social Sciences: An Introduction. London: Palgrave Macmillan.
Wang, C., Rutledge, J., & Gianola, D. (1993). Marginal inferences about variance components in a mixed linear model using Gibbs sampling. Genetics Selection Evolution, 25 , 41-62.
Weinberger, M., Oddone, E., Henderson, W., Smith, D., Huey, J., Giobbie-Hurder, A., & Feussner, J. (2001). Multisite Randomized Controlled Trials in Health Services Research: Scientific Challenges and Operational Issues. Medical Care, 39 (6), 627-634.