Abstract #300067

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JSM 2003 Abstract #300067
Activity Number: 272
Type: Invited
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: WNAR
Abstract - #300067
Title: Cluster-Level Factor Analysis for Survey Data with Structured Nonresponse
Author(s): Alan M. Zaslavsky*+ and Alistair James O'Malley
Companies: Harvard University Medical School and Harvard University Medical School
Address: 180 Longwood Ave., Boston, MA, 02115-5899,
Keywords: covariance functions ; survey ; hierarchical models ; factor analysis ; health care ; bayesian analysis
Abstract:

Health care quality surveys are administered to individual respondents (hospital patients, health plan members) to evaluate the performance of health care units. The measures for each unit are item means for all respondents or defined subgroups. Due to both planned item nonresponse (controlled by screening questions and skip patterns) and unplanned nonresponse, each such measure is based on a different subset of the survey respondents. For better understanding and more parsimonious reporting of dimensions of quality, we wish to analyze how these measures are related at the unit level, by applying techniques such as factor analysis to covariance structure estimated at the unit level in a hierarchical model. We fit a hierarchical quasi-likelihood model using generalized variance-covariance functions that take into account the nonresponse pattern, and both maximum quasi-likelihood and Bayesian inferential procedures. We use this model together with exploratory factor analysis to draw inferences about grouping of measures into related composites. The full Bayesian inference is used to assess the computationally simpler quasi-likelihood approach.

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