JSM 2005 Online Program

Abstract #303085

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:	528
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Survey Research Methods
Abstract - #303085
Title:	Methodology to Evaluate Longitudinal Estimates After Imputation in a Graduate Student Survey
Author(s):	Adriana Perez*+
Companies:	The University of Texas Health Science Center at Houston
Address:	80 Fort Brown SPH RAHC, Brownsville, TX, 78520, United States
Keywords:	Establishment data ; logical imputation ; missing completely at random ; missing at random ; inflator/deflator factors ; national survey
Abstract:	Nonresponse in establishment surveys is an ongoing problem. Cross-sectional establishment surveys usually are analyzed in combination with previous cycles, setting up a longitudinal structure. Imputation is used to handle item nonresponse. There are few studies reporting the effects on totals estimators (including its variance) after imputation techniques have been carried out in establishment surveys. We propose a methodology to examine the effect of the imputation method implemented at the Graduate Student Survey and Postdoctorates in Science and Engineering (GSS). This examination involves longitudinal features in that imputations are based on current and previous year's data. A complete pseudo universe with yearly data from 1998--2002 was generated. Longitudinal patterns of missing data observed in the GSS have been applied to this pseudo universe, therefore creating a dataset with missing values. Within this dataset, the GSS's imputation method has been implemented obtaining total and variance estimators after imputation. Accuracy and precision measures from simulations are proposed.

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