Publication: Field procedures in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
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Date
2013
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Wiley-Blackwell
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Citation
Heeringa, Steven G., Nancy Gebler, Lisa J. Colpe, Carol S. Fullerton, Irving Hwang, Ronald C. Kessler, James A. Naifeh, et al. 2013. “Field Procedures in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).” International Journal of Methods in Psychiatric Research 22 (4) (September 3): 276–287. doi:10.1002/mpr.1400. http://dx.doi.org/10.1002/mpr.1400.
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Abstract
The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi-component epidemiological and neurobiological study of unprecedented size and complexity designed to generate actionable evidence-based recommendations to reduce U.S. Army suicides and increase basic knowledge about determinants of suicidality by carrying out coordinated component studies. A number of major logistical challenges were faced in implementing these studies. The current report presents an overview of the approaches taken to meet these challenges, with a special focus on the field procedures used to implement the component studies. As detailed in the paper, these challenges were addressed at the onset of the initiative by establishing an Executive Committee, a Data Coordination Center (the Survey Research Center [SRC] at the University of Michigan), and study-specific design and analysis teams that worked with staff on instrumentation and field procedures. SRC staff, in turn, worked with the Office of the Deputy Under Secretary of the Army (ODUSA) and local Army Points of Contact (POCs) to address logistical issues and facilitate data collection. These structures, coupled with careful fieldworker training, supervision, and piloting contributed to the major Army STARRS data collection efforts having higher response rates than previous large-scale studies of comparable military samples.
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Keywords
Suicide, mental disorders, U.S. Army, epidemiologic research design, design effects, sample bias, sample weights, survey design efficiency, survey sampling
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