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A Bifactor and item response analysis of the geriatric anxiety inventory

Published online by Cambridge University Press:  20 June 2017

H. Molde*
Affiliation:
Department of Clinical Psychology, University of Bergen, Bergen, Norway
K. M. Hynninen
Affiliation:
Department of Clinical Psychology, University of Bergen, Bergen, Norway NKS Olaviken Gerontopsychiatric Hospital, Askøy, Norway
T. Torsheim
Affiliation:
Department of Psychosocial Science, University of Bergen, Bergen, Norway
A.B. Bendixen
Affiliation:
Norwegian National Advisory Unit for Aging and Health, Vestfold Hospital and Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
K. Engedal
Affiliation:
Norwegian National Advisory Unit for Aging and Health, Vestfold Hospital and Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
N.A. Pachana
Affiliation:
School of Psychology, University of Queensland, Brisbane, Australia
I. H. Nordhus
Affiliation:
Department of Clinical Psychology, University of Bergen, Bergen, Norway Department of Behavioural Sciences in Medicine, University of Oslo, Oslo, Norway
*
Correspondence should be addressed to: Helge Molde, Department of Clinical Psychology, Christiesgate 12, 5020 BERGEN, Norway. Phone: +0047 55588682. Email: helge.molde@uib.no.

Abstract

Background:

Due to previously reported mixed findings, there is a need for further empirical research on the factorial structure of the commonly used Geriatric Anxiety Inventory (GAI). Therefore, the psychometric properties of the GAI and its short form version (GAI-SF) were evaluated in a psychogeriatric mixed in-and-out patient sample (n = 543).

Methods:

Unidimensionality was tested using a bifactor analysis. Rasch modeling was used to assess scale properties. Sex, cognitive functioning and depressive symptoms were tested for differential item functioning (DIF).

Results:

The bifactor analysis identified an essential unidimensional (general) factor structure but also specific local factors. The general factor comprises all the 20 items as one factor, and the results showed that the variance in the general and specific factors (subscale) scores is best explained by the single general factor. These findings were demonstrated for both versions of the GAI. Furthermore, the Rasch models identified extensive item overlap, indicating redundant items in the full version of the GAI. The GAI-SF also seems to extract much of the same information as the full form. Test scores and items have the same meaning for older adults across different demographic status.

Conclusion:

The findings support the use of a total sum score for both GAI and GAI-SF. Notably, when using the GAI-SF, no information is lost, in comparison with the full scale, thus, supporting the option of choosing the short form (version) when considered most appropriate in demanding clinical contexts.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2017 

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