JSM 2005 Online Program

Abstract #303332

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	454
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Survey Research Methods
Abstract - #303332
Title:	Simple Approaches to Estimating the Variance of the Propensity Score Weighted Estimator Applied on Volunteer Panel Web Survey Data: A Comparative Study
Author(s):	Annica Isaksson*+ and Sunghee Lee
Companies:	Statistics Sweden and University of California, Los Angeles
Address:	Methodology, Orebro, SE-70189, Sweden
Keywords:	Web survey ; propensity score weighting ; variance estimation
Abstract:	Survey data collection from volunteer Web panels is growing in popularity. While this practice has obvious advantages in terms of cost, flexibility, and speed, estimates from such data for characteristics of the general population may be seriously biased due to low web penetration in the general population, nonprobability samples, and high nonresponse rates. The propensity score adjustment was introduced as a possible solution to the selection bias in observational studies. Its modification, propensity score weighting using a reference survey, has been suggested as a remedy of problems in volunteer panel web surveys. We can derive a propensity score weighted estimator that is less biased, if not unbiased, of the parameter of interest under certain model assumptions. However, there is no evident solution for its variance estimation. In the present study, simple variance estimators are compared: (1) the estimator applicable under poststratification, (2) a variation of (1) available in SUDAAN, (3) a model-based estimator, and (4) the jackknife estimator. The study focuses on the performance of the estimators in a simulation designed from a real web survey.

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