Abstract #301615

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JSM 2003 Abstract #301615
Activity Number: 449
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract - #301615
Title: The Impact of Missing Data, and Procedures for Handling Missing Data Using an Item Repsonse Theory Framework
Author(s): Carolyn F. Furlow*+ and Rachel T. Fouladi and Tiffany A. Whittaker
Companies: Student and University of Texas M.D. Anderson Cancer Center and University of Texas
Address: 2106A Homedale, Austin, TX, 78704,
Keywords: item response theory ; missing data ; multiple imputation ; Rasch model ; Andrich's rating scale ; partial credit model
Abstract:

When measurement error in observed variables cannot be avoided, such as in the case of the assessment of the quality of life, functional ability or attitudes, latent variable parameter estimates are obtained through summarizing patterns on multiple observed variables using a number of different approaches including item repsonse theory. Because missing data are not uncommon, this paper evaluates the impact of missing observations and different methods for handling missing observations for data derived under several item response theory models. These models include the Rasch model, Andrich's Rating Scale, and the Partial Credit Model. With the degree and type of missing data varied to reflect commonly encountered data conditions, methods for handling missing data including complete-case analyses, mean substitution, as well as, multiple imputation are compared with regard to estimation bias and root mean squared error of estimation.

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