Mixed Methods in a Post-Deployment Study of U.S. Army National Guard Soldiers

Abstract

Mixed methods research combines quantitative and qualitative methodologies to achieve a product that is more than the sum of its parts. Here, the authors combine two different data reduction techniques—factor and content analyses of quantitative and qualitative data, respectively—to tap the dimensions of deployment-related variables among U.S. Army National Guard (ARNG) soldiers: stressors and supports, resilience, post-traumatic stress disorder symptoms, and post-traumatic growth. Using these combined techniques, the authors explore the validity and substance of these variables and their measures so that employers and practitioners can better understand what deployment means for their employees and clients who are in the ARNG.

KEYWORDS:

• deployment stressors
• deployment supports
• mixed methods
• national guard soldiers
• post-traumatic growth
• post-traumatic stress
• resilience

Acknowledgments

This article reflects the opinions of the authors and does not necessarily represent the position of the U.S. Air Force Academy, the U.S. Air Force, or the Department of Defense.

Notes

1-F = Deploy Stress—Worry; 2-F = Deploy Stress—Danger; 3-F = Deploy Stress—Unit.

^a Indicate whether this was a source of stress for you during your last deployment. Response format: 1 = not a source of stress, 5 = a very big source of stress.

^b Principal components extraction, number of factors equals number of eigenvalues >1, oblimin rotation (allows factors to be correlated). Loading is listed as “—” if less than +/–.35.

1-F = Barriers to Support.

^a Indicate if these have played a role in barring you from obtaining support. Response format: 0 = have not played a role, 2 = have played a very big role.

^b Principal components extraction, number of factors equals number of eigenvalues >1.

1-F = Positive Benefits—Pride; 2-F = Positive Benefits—Money.

^a Indicate how much” benefit” you experienced. Response format: 1 = not at all, 4 = quite a bit.

^b Principal components extraction, number of factors equals number of eigenvalues >1, oblimin rotation. Loading is listed as “—” if less than +/–.35.

^c Indicate how much support this has been for you. Response format: 1 = no support at all, 4 = a lot of support.

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