JSM 2005 - Toronto

Abstract #304595

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.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.



The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page
Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 146
Type: Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #304595
Title: Evaluation of PCA/CHAID for the Creation of Imputation Classes
Author(s): Marcus Berzofsky*+ and June Cong and Roy Whitmore and Shiying Wu
Companies: RTI International and RTI International and RTI International and RTI International
Address: 3040 Cornwallis Rd, RTP, NC, 27709, United States
Keywords: imputation classes ; imputation ; PCA ; CHAID ; nonresponse
Abstract:

This article addresses the problem of constructing classes for imputation of nonresponse among a set of correlated, continuous survey responses. The strategy tested uses Principal Component Analysis (PCA) and Chi-Squared Automatic Interaction Detection (CHAID) to define the imputation classes. Using data from the past three years of the Integrated Postsecondary Education Data System's (IPEDS) Finance survey, we test to see if the PCA/CHAID method creates consistent imputation classes across multiple years of the survey. Moreover, using the nearest neighbor imputation method, we determine if imputations based on the PCA/CHAID method result in significantly greater reduction of bias than imputation classes based on institutional characteristics alone by subsampling from current respondents and imputing their data. Because nonresponse is not necessarily a random event, we use two approaches to test for bias reduction: subsampling from all current-year respondents and subsampling from those who were nonrespondents in either of the past two years.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005