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Abstract Details

Activity Number: 582
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #306416
Title: A Hierarchical Clustering Algorithm for Multivariate Stratification in Stratified Sampling
Author(s): Stephanie Zimmer*+ and Jaekwang Kim and Sarah Nusser
Companies: and Iowa State University and Iowa State University
Address: 1308 Walton Dr, Ames, IA, 50014, United States
Keywords: stratification ; superpopulation ; clustering

Stratification is used in sampling to create homogeneous groups. A number of methods have been proposed for stratification of populations using covariates of the variable of interest. These include Dalenius and Hodges' (1959) cumulative root frequency method, the Lavallee and Hidiroglou (1988) algorithm, and the Gunning and Horgan (2004) geometric stratification method. All of these methods assume you have one variable of interest and one correlated auxiliary variable known for the population. Many surveys have more than one important variables of interest as well as many auxiliary variables. The method we propose uses a superpopulation model to create a distance metric between elements in the population that depends on multiple auxiliary variables and considers multiple variables of interest. Using the proposed metric, a hierarchical clustering algorithm can be used to implement the opt

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