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Strategies for Analyzing Missing Item Response Data with an Application to Lung Cancer
Author(s) -
Sheng Xiaoming,
Carrière K. C.
Publication year - 2005
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200410143
Subject(s) - missing data , computer science , estimator , data quality , data mining , sample (material) , statistics , machine learning , mathematics , engineering , metric (unit) , operations management , chemistry , chromatography
Missing data problems persist in many scientific investigations. Although various strategies for analyzing missing data have been proposed, they are mainly limited to data on continuous measurements. In this paper, we focus on implementing some of the available strategies to analyze item response data. In particular, we investigate the effects of popular missing data methods on various missing data mechanisms. We examine large sample behaviors of estimators in a simulation study that evaluates and compares their performance. We use data from a quality of life study with lung cancer patients to illustrate the utility of these methods. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)