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Adaptive inference for multi-stage survey data
Faculty of Informatics - Papers (Archive)
  • Loai Mahmoud Alzoubi, University of Wollongong
  • Robert Graham Clark, University of Wollongong
  • David G Steel, University of Wollongong
Publication Date
Publication Details

Al-zou'bi, L. Mahmoud., Clark, R. Graham. & Steel, D. G. (2010). Adaptive inference for multi-stage survey data. Communications in Statistics: Simulation and Computation, 39 (7), 1334-1350.

Multi-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 Information
Loai Mahmoud Alzoubi, Robert Graham Clark and David G Steel. "Adaptive inference for multi-stage survey data" (2010)
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