JSM 2005 - Toronto

Abstract #303931

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Legend: = Applied Session, = Theme Session, = Presenter
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 - #303931
Title: Comparison of Methods for Handling Missing Data in a Collegiate Survey of Tobacco Use
Author(s): Liza M. Nirelli*+ and Michael D. Larsen and Ivana T. Croghan and Darrell R. Schroeder and Kenneth P. Offord and Richard D. Hurt
Companies: Iowa State University and Iowa State University and Mayo Clinic and Mayo Clinic and Mayo Clinic and Mayo Clinic
Address: 4 schilletter village apt A, ames, IA, 50010, United States
Keywords: EM Algorithm ; Single Imputation ; Multiple Imputation ; Survey Scales ; Self Esteem Scale ; Cigarette Smoking
Abstract:

The need to impute data in survey analyses is an all-too-common problem. Investigators at the Mayo Clinic conducted the Survey of Young Adults in 2003 at three undergraduate schools in southeastern Minnesota. Convenience samples of 2,005 college students were collected to measure tobacco consumption behavior, body image, perceived stress, and self-esteem. Series of questions were used to form scales to measure complex concepts. Some students skipped individual questions, whereas others skipped groups of questions. Some students did not answer questions concerning smoking status and demographic variables. A common approach uses available cases for survey scale calculation and comparison of groups. This paper compares available case results with results using alternate methods for handling missing values. Single imputation methods include hot-deck, mean, mode, and prediction imputation. Methods not using explicit imputations include weighting and loglinear modeling. Multiple imputations are based on hot-deck methods and loglinear models. Results using the methods for various analyses are compared.

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