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Activity Number: 357
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #303613
Title: Collinearity Diagnostics for Complex Survey Data
Author(s): Dan Liao*+
Companies: University of Maryland
Address: 1218 Lefrak Hall, College Park, MD, 20770,
Keywords: Collinearity ; Variance Inflation Factors ; Model based inference ; Design based inference ; Informative Sampling
Abstract:

The Variance Inflation Factor (VIF) is widely employed to assess the degree of variance inflation of the parameter estimate for the i_th independent variable by its collinearity with the other independent variables in a regression model. However, little research has been done to extend it for the analysis of complex survey data by incorporating complex survey design features, which may relate to the independent variables and affect the collinearity patterns in the model. In this paper, two adjustment coefficients, zeta_i and varrho_i are defined to adjust conventional VIF when computing collinearity diagnostics in the analysis of complex survey data. We present both the model-based and design-based theory to justify the methods. Extended statistics will be applied to the data from 1998 Survey of Mental Health Organizations (SMHO) in a case study.

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