A Model-Based Approach for Complex Sampling Designs in Education Surveys

• In Event: Educational Research From Different Lenses

Abstract

This study examines the performance of an intuitive to implement approach named the sample distribution approach (SDA) that utilizes Markov Chain Monte Carlo (MCMC) methods and Bayesian inference to deal with complex sampling designs in large-scale education data. Our simulation design focused on clustering effects, represented by the Intraclass Correlation (ICC) and on the sample size of the cluster. We analyzed a large-scale educational assessment dataset (Early Childhood Longitudinal Study - Kindergarten 2011) to compute estimates for the simulation. Findings reveal that the SDA overall generated reliable posterior distributions of parameters and had small error variances. Although design informativeness is important, the ICC and cluster sample size factors had little impact on the performance of this model based approach.

Authors

• Ting Shen, Missouri University of Science & Technology

• Spyros Konstantopoulos, Michigan State University