Practical methods for dealing with 'not applicable' item responses in the AMC Linear Disability Score project
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Practical methods for dealing with 'not applicable' item responses in the AMC Linear Disability Score project Rebecca Holman*1, Cees AW Glas2, Robert Lindeboom1, Aeilko H Zwinderman1 and Rob J de Haan1 Address: 1Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, Amsterdam, The Netherlands and 2Department of Educational Measurement and Data Analysis, University of Twente, Enschede, The Netherlands Email: Rebecca Holman* - [email protected]; Cees AW Glas - [email protected]; Robert Lindeboom - [email protected]; Aeilko H Zwinderman - [email protected]; Rob J de Haan - [email protected] * Corresponding author
Published: 16 June 2004 Health and Quality of Life Outcomes 2004, 2:29
Received: 23 April 2004 Accepted: 16 June 2004
This article is available from: http://www.hqlo.com/content/2/1/29 © 2004 Holman et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
Abstract Background: Whenever questionnaires are used to collect data on constructs, such as functional status or health related quality of life, it is unlikely that all respondents will respond to all items. This paper examines ways of dealing with responses in a 'not applicable' category to items included in the AMC Linear Disability Score (ALDS) project item bank. Methods: The data examined in this paper come from the responses of 392 respondents to 32 items and form part of the calibration sample for the ALDS item bank. The data are analysed using the one-parameter logistic item response theory model. The four practical strategies for dealing with this type of response are: cold deck imputation; hot deck imputation; treating the missing responses as if these items had never been offered to those individual patients; and using a model which takes account of the 'tendency to respond to items'. Results: The item and respondent population parameter estimates were very similar for the strategies involving hot deck imputation; treating the missing responses as if these items had never been offered to those individual patients; and using a model which takes account of the 'tendency to respond to items'. The estimates obtained using the cold deck imputation method were substantially different. Conclusions: The cold deck imputation method was not considered suitable for use in the ALDS item bank. The other three methods described can be usefully implemented in the ALDS item bank, depending on the purpose of the data analysis to be carried out. These three methods may be useful for other data sets examining similar constructs, when item response theory based methods are used.
Background When questionnaires consisting of a number of related items are used to measure constructs such as health related quality of life [1,2], cognitive ability [3] or functional status [4], it is likely that some patients will omi
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