Open Access Highly Accessed Research article

Exploratory factor analysis of self-reported symptoms in a large, population-based military cohort

Molly L Kelton1, Cynthia A LeardMann1*, Besa Smith1, Edward J Boyko2, Tomoko I Hooper3, Gary D Gackstetter4, Paul D Bliese5, Charles W Hoge5, Tyler C Smith1 and the Millennium Cohort Study Team

Author Affiliations

1 Department of Defense Center for Deployment Health Research at the Naval Health Research Center, San Diego, CA, USA

2 Seattle Epidemiologic Research and Information Center, Veterans Affairs, Puget Sound Health Care System, Seattle, WA, USA

3 Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA

4 Analytic Services Inc., Arlington, VA, USA

5 Center for Psychiatry and Neuroscience, Walter Reed Army Institute of Research, Silver Spring, MD, USA

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BMC Medical Research Methodology 2010, 10:94  doi:10.1186/1471-2288-10-94

Published: 15 October 2010

Abstract

Background

US military engagements have consistently raised concern over the array of health outcomes experienced by service members postdeployment. Exploratory factor analysis has been used in studies of 1991 Gulf War-related illnesses, and may increase understanding of symptoms and health outcomes associated with current military conflicts in Iraq and Afghanistan. The objective of this study was to use exploratory factor analysis to describe the correlations among numerous physical and psychological symptoms in terms of a smaller number of unobserved variables or factors.

Methods

The Millennium Cohort Study collects extensive self-reported health data from a large, population-based military cohort, providing a unique opportunity to investigate the interrelationships of numerous physical and psychological symptoms among US military personnel. This study used data from the Millennium Cohort Study, a large, population-based military cohort. Exploratory factor analysis was used to examine the covariance structure of symptoms reported by approximately 50,000 cohort members during 2004-2006. Analyses incorporated 89 symptoms, including responses to several validated instruments embedded in the questionnaire. Techniques accommodated the categorical and sometimes incomplete nature of the survey data.

Results

A 14-factor model accounted for 60 percent of the total variance in symptoms data and included factors related to several physical, psychological, and behavioral constructs. A notable finding was that many factors appeared to load in accordance with symptom co-location within the survey instrument, highlighting the difficulty in disassociating the effects of question content, location, and response format on factor structure.

Conclusions

This study demonstrates the potential strengths and weaknesses of exploratory factor analysis to heighten understanding of the complex associations among symptoms. Further research is needed to investigate the relationship between factor analytic results and survey structure, as well as to assess the relationship between factor scores and key exposure variables.