A0996
Title: Models for cluster randomised designs using ranked set sampling
Authors: Olena Kravchuk - University of Adelaide (Australia)
Richard Jarrett - University of Adelaide (Australia)
Omer Ozturk - The Ohio State University (United States) [presenting]
Abstract: Cluster randomized designs (CRD) provide a rigorous development for randomization principles for studies where treatments are allocated to cluster units rather than the individual subjects within clusters. It is known that CRDs are less efficient than completely randomized designs since the randomization of treatment allocation is applied to the cluster units. To mitigate this problem, ranked set sampling design from survey sampling studies is embedded into CRD for the selection of cluster and subsampling units. It is shown that ranking groups in ranked set sampling act like a covariate, reducing the expected to mean squared cluster error and increasing the precision of the sampling design. An optimality result is provided to determine the sample sizes at the cluster and sub-sample levels. The proposed sampling design is applied to a longitudinal study from an education intervention program.
