Adaptive inference for multi-stage survey dataFaculty of Informatics - Papers (Archive)
AbstractMulti-level models can be used to account for clustering in data from multi-stage surveys. In some cases, the intraclass correlation may be close to zero, so that it may seem reasonable to ignore clustering and fit a single-level model. This article proposes several adaptive strategies for allowing for clustering in regression analysis of multi-stage survey data. The approach is based on testing whether the PSU-level variance component is zero. If this hypothesis is retained, then variance estimates are calculated ignoring clustering; otherwise, clustering is reflected in variance estimation. A simple simulation study is used to evaluate the various procedures.
Citation InformationLoai Mahmoud Alzoubi, Robert Graham Clark and David G Steel. "Adaptive inference for multi-stage survey data" (2010)
Available at: http://works.bepress.com/robert_clark/8/