Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-02-11T04:49:10.308Z Has data issue: false hasContentIssue false
  • English
  • Français

Geriatric Anxiety Scale: item response theory analysis, differential item functioning, and creation of a ten-item short form (GAS-10)

Published online by Cambridge University Press:  27 February 2014

Anne E. Mueller ,
Daniel L. Segal ,
Brandon Gavett ,
Meghan A. Marty ,
Brian Yochim ,
Andrea June  and
Frederick L. Coolidge
Anne E. Mueller
Affiliation:
Veterans Affairs Puget Sound Health Care System, American Lake Division, Washington, USA
Daniel L. Segal*
Affiliation:
Department of Psychology, University of Colorado at Colorado Springs, Colorado, USA
Brandon Gavett
Affiliation:
Department of Psychology, University of Colorado at Colorado Springs, Colorado, USA
Meghan A. Marty
Affiliation:
Transitions Professional Center, Portland, Oregon, USA
Brian Yochim
Affiliation:
Department of Psychology, University of Colorado at Colorado Springs, Colorado, USA VA Palo Alto Health Care System, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
Andrea June
Affiliation:
Department of Psychological Science, Central Connecticut State University, New Britain, Connecticut, USA
Frederick L. Coolidge
Affiliation:
Department of Psychology, University of Colorado at Colorado Springs, Colorado, USA
*
Correspondence should be addressed to: Daniel L. Segal, PhD, Department of Psychology, University of Colorado at Colorado Springs, Colorado Springs, CO 80918, USA. Phone: +719-255-4176; Fax: +719-255-4166. Email: dsegal@uccs.edu.
Get access

Abstract

Background:

The Geriatric Anxiety Scale (GAS; Segal et al. (Segal, D. L., June, A., Payne, M., Coolidge, F. L. and Yochim, B. (2010). Journal of Anxiety Disorders, 24, 709–714. doi:10.1016/j.janxdis.2010.05.002) is a self-report measure of anxiety that was designed to address unique issues associated with anxiety assessment in older adults. This study is the first to use item response theory (IRT) to examine the psychometric properties of a measure of anxiety in older adults.

Method:

A large sample of older adults (n = 581; mean age = 72.32 years, SD = 7.64 years, range = 60 to 96 years; 64% women; 88% European American) completed the GAS. IRT properties were examined. The presence of differential item functioning (DIF) or measurement bias by age and sex was assessed, and a ten-item short form of the GAS (called the GAS-10) was created.

Results:

All GAS items had discrimination parameters of 1.07 or greater. Items from the somatic subscale tended to have lower discrimination parameters than items on the cognitive or affective subscales. Two items were flagged for DIF, but the impact of the DIF was negligible. Women scored significantly higher than men on the GAS and its subscales. Participants in the young-old group (60 to 79 years old) scored significantly higher on the cognitive subscale than participants in the old-old group (80 years old and older).

Conclusions:

Results from the IRT analyses indicated that the GAS and GAS-10 have strong psychometric properties among older adults. We conclude by discussing implications and future research directions.

Keywords

geriatricanxiety assessmentanxietyitem response theoryshort form
Type
Research Article
Information
International Psychogeriatrics , Volume 27 , Special Issue 7: Late Life Anxiety , July 2015 , pp. 1099 - 1111
Copyright
Copyright © International Psychogeriatric Association 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baker, F. (2001). The Basics of Item Response Theory. College Park, MD: ERIC Clearinghouse on Assessment and Evaluation, University of Maryland.Google Scholar
Brock, K., Clemson, L., Cant, R., Ke, L., Cumming, R. G., Kendig, H. and Mathews, M. (2011). Worry in older community-residing adults. International Journal of Aging and Human Development, 72, 289–301. doi:10.2190/AG.72.4.a.Google Scholar
Bryant, C., Jackson, H. and Ames, D. (2008). The prevalence of anxiety in older adults: methodological issues and a review of the literature. Journal of Affective Disorders, 109, 233–250. doi:10.1016/j.jad.2007.11.008.Google Scholar
Byrne, G. J. and Pachana, N. A. (2011). Development and validation of a short form of the Geriatric Anxiety Inventory – the GAI-SF. International Psychogeriatrics, 23, 125–131. doi:10.1017/S1041610210001237.Google Scholar
Calleo, J. et al. (2009). Generalized anxiety disorder in older medical patients: diagnostic recognition, mental health management, and service utilization. Journal of Clinical Psychology in Medical Settings, 16, 178–185. doi:10.1007/s10880-008-9144-5.Google Scholar
Cairney, J., Corna, L. M., Veldhuizen, B. A., Herrmann, N. and Streiner, D. L. (2008). Comorbid depression and anxiety in later life: patterns of association, subjective well-being, and impairment. American Journal of Geriatric Psychiatry, 16, 201–208.Google Scholar
Choi, S. W., Gibbons, L. E. and Crane, P. K. (2011). Lordif: an R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software, 39, 1–30.CrossRefGoogle Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd edn. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159.Google Scholar
Crane, P. K., Gibbons, L. E., Jolley, L. and van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIF detect and dif with par. Medical Care, 44, S115–123.CrossRefGoogle Scholar
de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. New York, NY: Guilford Press.Google Scholar
De Beurs, E., Beekman, A. T. F., van Dyck, D. J. H. D. and van Tilburg, W. (2000). Predictors of change in anxiety symptoms of older persons: results from the Longitudinal Aging Study, Amsterdam. Psychological Medicine, 30, 515–527.Google Scholar
Edelen, M. O. and Reeve, B. B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16, 5–18. doi:10.1007/s11136-007-9198-0.CrossRefGoogle ScholarPubMed
Edelstein, B. A. et al. (2008). Older adult psychological assessment: current instrument status and related considerations. Clinical Gerontologist, 31, 1–35.Google Scholar
Embretson, S.E. and Reise, S. P. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Flint, A. J. et al. (2010). Effect of age on the frequency of anxiety disorders in major depression with psychotic features. American Journal of Geriatric Psychiatry, 18, 404–412.Google Scholar
Gum, A. M., King-Kallimanis, B. and Kohn, R. (2009). Prevalence of mood, anxiety, and substance-abuse disorders for older Americans in the National Comorbidity Survey Replication. American Journal of Geriatric Psychiatry, 17, 769–781. doi:10.1097/JGP.0b013e3181ad4f5a.Google Scholar
Kabacoff, R. I., Segal, D. L., Hersen, M. and Van Hasselt, V. B. (1997). Psychometric properties and diagnostic utility of the Beck Anxiety Inventory and the State-Trait Anxiety Inventory with older adult psychiatric outpatients. Journal of Anxiety Disorders, 11, 33–47. doi:10.1016/S0887-6185(96)00033-3.Google Scholar
Katon, W., Lin, E. and Kroenke, K. (2007). The association of depression and anxiety with medical symptom burden in patients with chronic medical illness. General Hospital Psychiatry, 29, 147–155. doi:10.1016/j.genhosppsych.2006.11.005.CrossRefGoogle ScholarPubMed
Leach, L. S., Christensen, H. and Mackinnon, A. J. (2008a). Gender differences in the endorsement of symptoms for depression and anxiety: are gender-biased items responsible? Journal of Nervous and Mental Disease, 196, 128–135. doi:10.1097/NMD.0b013e318162aa63.Google Scholar
Leach, L. S., Christensen, H., Mackinnon, A. J., Windsor, T. D. and Butterworth, P. (2008b). Gender differences in depression and anxiety across the adult lifespan: the role of psychosocial mediators. Social Psychiatry and Psychiatric Epidemiology, 43, 983–998. doi:10.1007/s00127-008-0388-z.Google Scholar
Lowe, P. A. and Reynolds, C. R. (2005). Do relationships exist between age, gender, and education and self-reports of anxiety among older adults? Individual Differences Research, 3, 239–259.Google Scholar
Murphy, L. B., Sacks, J. J., Brady, T. J., Hootman, J. M. and Chapman, D. P. (2012). Anxiety is more common than depression among US adults with arthritis. Anxiety Care & Research, 64, 968–976. doi:10.1002/acr.21685.Google ScholarPubMed
Pedraza, O. and Mungas, D. (2008). Measurement in cross-cultural neuropsychology. Neuropsychology Review, 18, 184–193.CrossRefGoogle ScholarPubMed
Potvin, O. et al. (2011). Norms and associated factors of the STAI-Y state anxiety inventory in older adults: results from the PAQUID study. International Psychogeriatrics, 23, 869–879. doi:10.1017/S1041610210002358.Google Scholar
R Core Team. (2012). R: A Language and Environment for Statistical Computing (computer software). Vienna, Austria: R Foundation for Statistical Computing. Available at: http://www.R-project.org; last accessed: 7 December 2012.Google Scholar
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34, 100–114.Google Scholar
Schaub, R. T. and Linden, M. (2000). Anxiety and anxiety disorders in the old and very old: results from the Berlin Aging Study (BASE). Comprehensive Psychiatry, 41, 48–54. doi:10.1016/S0010-440X(00)80008-5.Google Scholar
Segal, D. L., June, A., Payne, M., Coolidge, F. L. and Yochim, B. (2010). Development and initial validation of a self-report assessment tool for anxiety among older adults: the Geriatric Anxiety Scale. Journal of Anxiety Disorders, 24, 709–714. doi:10.1016/j.janxdis.2010.05.002.Google Scholar
Therrien, Z. and Hunsley, J. (2011). Assessment of anxiety in older adults: a systematic review of commonly used measures. Aging & Mental Health, 15, 1–16. doi:10.1080/13067863.2011.602960.Google Scholar
Van Dam, N. T., Earleywine, M. and Forsyth, J. P. (2009). Gender bias in the sixteen-item Anxiety Sensitivity Index: an application of polytomous differential item functioning. Journal of Anxiety Disorders, 23, 256–259. doi:10.1016/j.janxdis.2008.07.008.Google Scholar
Wolitzky-Taylor, K. B., Castriotta, N., Lenze, E. J., Stanley, M. A. and Craske, M. G. (2010). Anxiety disorders in older adults: a comprehensive review. Depression and Anxiety, 27, 190–211. doi:10.1002/da.20653.Google Scholar
Yochim, B. P., Mueller, A. E., June, A. and Segal, D. L. (2011). Psychometric properties of the Geriatric Anxiety Scale: comparison to the Beck Anxiety Inventory and Geriatric Anxiety Inventory. Clinical Gerontologist, 34, 21–33. doi:10.1080/07317115.2011.524600.Google Scholar
Yochim, B. P., Mueller, A. E. and Segal, D. L. (2013). Late life anxiety is associated with decreased memory and executive functioning in community dwelling older adults. Journal of Anxiety Disorders, 27, 567–575.Google Scholar
Zumbo, B. D. (1999). A Handbook on the Theory and Methods of Differential Item Functioning (DIF): Logistic Regression Modeling as a Unitary Framework for Binary and Likert-Type (Ordinal) Item Scores. Ottawa, Canada: Directorate of Human Resources Research and Evaluation, Department of National Defense.Google Scholar