Detecting consensus emergence in organizational multilevel data : power simulations

Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach-the consensus emergence model-builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research

Combat and Trajectories of Physical Health Functioning in US Service Members

Introduction Previous research has demonstrated that different forms of mental health trajectories can be observed in service members, and that these trajectories are related to combat. However, limited research has examined this phenomenon in relation to physical health. This study aims to determine how combat exposure relates to trajectories of physical health functioning in U.S. service members. Methods This study included 11,950 Millennium Cohort Study participants who had an index deployment between 2001 and 2005. Self-reported physical health functioning was obtained 5 times between 2001 and 2016 (analyzed in 2017), and latent growth mixture modeling was used to identify longitudinal trajectories from these assessments. Differences in the shape and prevalence of physical health functioning trajectories were investigated in relation to participants’ self-reported combat exposure over the index deployment. Results Five physical health functioning trajectories were identified (high-stable, delayed-declining, worsening, improving-worsening, and low-stable). Combat exposure did not influence the shape of trajectories (p=0.12) but did influence trajectory membership. Relative to personnel not exposed to combat, participants reporting combat exposure were more likely to be in the delayed-declining, worsening, and low-stable classes and less likely to be in the high-stable class. However, the high-stable class (i.e., the most optimal class) was the most common trajectory class among not exposed (73.0%) and combat-exposed (64.5%) personnel. Conclusions Combat exposure during military deployment is associated with poorer physical health functioning trajectories spanning more than a decade of follow-up. However, even when exposed to combat, consistently high physical health functioning is the modal response

Prospective associations of perceived unit cohesion with postdeployment mental health outcomes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149506/1/da22884_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149506/2/da22884.pd

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p