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. 2021 Dec 17;36(3):301–310. doi: 10.1080/08995605.2021.1996103

Associations of humor, morale, and unit cohesion on posttraumatic stress disorder symptoms

Rachel N Ward 1,✉, Katie J Carlson 1, Alexander J Erickson 1, Matthew M Yalch 1, Lisa M Brown 1
PMCID: PMC11046040  NIHMSID: NIHMS1751716  PMID: 38661464

ABSTRACT

Military personnel experience many stressors during deployments that can lead to symptoms of posttraumatic stress disorder (PTSD). However, not all military personnel who are exposed to deployment stressors develop PTSD symptoms. Recent research has explored factors that contribute to military personnel resilience, a multifaceted and multidetermined construct, as a means to mitigate and prevent PTSD symptoms. Much of this research has focused on the effects of individual-level factors (e.g., use of coping strategies like humor, the morale of individual unit members), with some research focusing on unit-level factors (e.g., the cohesiveness of a unit). However, there is little research exploring how these factors relate to each other in mitigating or reducing PTSD symptoms. In this study, we examined the association between deployment stressors, perceived unit cohesion, morale, humor, and PTSD symptoms in a sample of 20,901 active-duty military personnel using structural equation modeling. Results indicated that perceived unit cohesion, humor, and morale were positively associated with each other and negatively associated with PTSD symptoms over and above the effect of deployment stressors. These findings highlight the influence of resilience factors on PTSD symptoms beyond their substantial overlap and have implications for future research as well as the potential development of interventions for military personnel.

KEYWORDS: Unit cohesion, morale, posttraumatic stress disorder, resilience factors, military

What is the public significance of this article?—This study found that higher perceived unit cohesion (i.e., the ability of a group to work together), morale (i.e., the mental state of group members), and use of humor contributed to lower PTSD symptoms over and above deployment stress in active-duty Army personnel. The findings from this study have the potential to inform the design of resilience-building programs for military personnel.

Posttraumatic stress disorder (PTSD) can develop after a person experiences or witnesses a dangerous event and is common in military service members and Veterans (Meadows et al., 2018). While the etiology of PTSD is multifactorial and develops due to both military-specific and general risk factors, the effect of deployment-related stressors (e.g., combat exposure, sexual harassment, no-combat warzone experiences), is often key for the development of PTSD (Richardson et al., 2010; Vasterling et al., 2010; Vogt et al., 2008). However, not all military service members who experience stressors during their deployments develop PTSD, which raises the question of what makes some more resilient than others.

A person’s resilience is the cumulative influence of psychological characteristics (e.g., personality factors, coping styles) that influences their response to stressors (Fletcher & Sarkar, 2013). This resilience, in turn, affects their risk for PTSD. Recent research has suggested that there are multiple levels of resilience factors that affect PTSD in military service members: individual-level, family-level, unit-level, and community-level factors (Meredith et al., 2011). In US military populations, there has been a strong focus on individual-level factors (e.g., positive thinking, coping, realism), unit-level factors (e.g., teamwork, positive command climate), and family-level factors (e.g., communication, support), particularly within resilience-promotion programs (Meredith et al., 2011). While these and other factors such as leadership style (Bass et al., 2003; Jones et al., 2012) or psychological hardiness (Escolas et al., 2013; Maddi, 2006) have been well researched concerning PTSD, other factors have received less attention.

Research has found that the unit-level factor of unit cohesion and the individual-level factors of morale and use of humor to be of particular importance in military populations (Britt & Oliver, 2013; Rice & Liu, 2016), in part due to their protective and buffering effects against negative mental health outcomes like PTSD (Armistead‐Jehle et al., 2011; Britt et al., 2017; Britt & Oliver, 2013). Consistent with this, resilience-building programs in the military often include training aspects aimed at fostering unit cohesion and positive coping/thinking while neglecting morale-building exercises (Meredith et al., 2011). However, morale, like unit cohesion and use of humor, are integral aspects of military service either through the nature of existing structures or, in the case of humor, through service members’ innate characteristics. Focusing on these factors presents an opportunity to capitalize on the existing structure and personal characteristics to build resilience.

Resilience in the military

Resilience is a developable, dynamic process that contributes to a person’s capacity to overcome adversity and psychological stressors. It is also the result of the collective influence of multiple factors, referred to as resilience factors (Boe, 2015; Fletcher & Sarkar, 2013). Psychological stressors lead to the activation of a sequela of physiological and psychological stress responses. After experiencing psychological stressors, a person’s resilience then influences the degree to which they develop poorer physical and/or psychological health (Ward et al., 2021). Given the frequency of stressors present in military service, resilience factors play vital roles in preventing the development of PTSD among military service members (Meredith et al., 2011). Our understanding of how specific resilience factors affect overall resilience can be expanded through exploration of the unique contributions of certain military-specific resilience factors such as unit cohesion, morale, and use of humor (Britt & Oliver, 2013; Rice & Liu, 2016).

Unit cohesion

Unit cohesion involves the sustained commitment to other service members of the same unit, which helps them perform the unit’s mission. Unit cohesion includes group bonding, shared values, and a feeling of interpersonal belonging (Meredith et al., 2011; Siebold, 2006) and is associated with higher unit-level performance and positive impacts on service member well-being (MacCoun et al., 2010), in part through increasing access to emotional and cognitive resources via the effects of group bonding and task commitment (Beal et al., 2003; Kirschner et al., 2018). This in turn may increase a unit member’s ability to handle stress (Siebold, 2006), partly through increasing access to social resources (i.e., social support, Britt & Oliver, 2013). For example, greater unit cohesion is associated with lower PTSD symptoms (Armistead‐Jehle et al., 2011; Jones et al., 2012).

Morale

Morale is the mental, emotional, and spiritual state of individuals engaging in group activities (Britt & Oliver, 2013; Manning, 1994). Like unit cohesion, morale improves service member collaboration, goal completion, and overall job performance (Britt & Oliver, 2013; Weakliem & Frenkel, 2006). Higher levels of morale are also associated with lower PTSD symptoms by mitigating the effect of combat exposure on the development of PTSD symptoms when accounting for unit support (Britt et al., 2013, 2017; Jones et al., 2012).

Use of humor

Humor, specifically coping humor, is the ability to create and appreciate humorous stimuli in response to stressors (Meredith et al., 2011; Sliter et al., 2014). Many studies in nonmilitary organizations have found use of positive humor to improve work performance, mental and physical health, engagement, and satisfaction (Mesmer-Magnus et al., 2012). Humor may also be protective in the context of trauma, serving to lift spirits and allow people to focus on the task at hand. Consistent with this, research suggests that self-enhancing or affiliative humor is associated with lower levels of PTSD and other mental health problems in the aftermath of traumatic events in service members as well as other trauma survivors (Besser et al., 2015; Britt et al., 2017; Sliter et al., 2014).

Relationship between resilience factors

In addition to being negatively associated with PTSD symptoms, research suggests that unit cohesion, morale, and use of humor, while distinct, are strongly associated (Britt & Oliver, 2013; Garrick, 2006). For example, morale and unit cohesion may mutually reinforce each other (e.g., units with adaptive use of humor may have higher morale and thus be more cohesive) but act separately on PTSD symptoms (Britt & Oliver, 2013). Less clear is how these factors might influence PTSD symptoms vis-à-vis deployment stress. Knowledge of how these factors are associated with each other and with PTSD symptoms has implications for initiatives focused on reducing risk for PTSD among service members, namely on pre- and post-deployment resilience-building programs. Given the lack of demonstrated association between deployment stress and any of these resilience factors, knowing if unit cohesion, morale, and humor incrementally decrease the effects of deployment stress on PTSD symptoms would enhance our ability to further the efficacy of our resilience-building programs.

Present study

In this study, we examined the relative effects of unit cohesion, morale, and humor on PTSD symptoms over and above deployment stressors in a sample of military personnel. We hypothesized that:

H1: Perceived unit cohesion, morale, and use of humor will be positively associated with each other.

H2: After controlling for the effect of deployment stressors, perceived unit cohesion, morale, and use of humor will individually negatively predict PTSD symptoms.

Post hoc research questions:

Q1: Do the resilience factors of perceived unit cohesion, morale, and use of humor have a mediating and/or moderating effect on the relationship between deployment stressors and PTSD symptoms?

Method

Participants and procedure

Participants were 21,449 active-duty service members from the US Army who were enrolled in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS; Ursano et al., 2018) and completed the All Army Survey (AAS; for study details, see R. C. Kessler et al., 2013). After excluding cases with missing responses across all variables (548) cases, the final sample included 20,901 active duty Army personnel (97%). Nearly half, (9,616) of the participants had complete responses for all variables in the analysis (45%). The sample was majority cisgender male and White with a mean age of 28.66 (SD = 7.41, range = 18–61; see Table 1). Over a third (35%) of the participants met full criteria for probable PTSD at any point in their lifetimes, 20% of whom (7% of the total sample) also met full criteria for probable PTSD in the past 30 days. Data available for secondary data analysis did not include military career information. Procedures involving human subjects’ protections were approved by the Institutional Review Boards of the collaborating organizations (R. C. Kessler et al., 2013). The original research team obtained written informed consent prior to survey administration and asked participants to consent to link their Army/Department of Defense administrative records to their survey responses (R. C. Kessler et al., 2013). Approval for the current study was obtained from the investigators’ university IRB.

Table 1.

Sample descriptive statistics.

  n (%)
Gender  
 Male 18,790 (88%)
 Female 2504 (12%)
Race  
 White 14,212 (66%)
 Black/African American 3253 (15%)
 American Indian/Alaskan Native 385 (2%)
 Asian 868 (4%)
 Pacific Islander 375 (2%)
 Other 2356 (11%)
Education  
 GED or equivalent 1315 (6%)
 High School Diploma 6716 (31%)
 Some Post-high School No Degree 5904 (28%)
 Technical School Degree 1336 (6%)
 2-year Associate Degree 2174 (10%)
 4-year College Degree 2766 (13%)
 Graduate/Professional Study 1050 (5%)
 Missing 188 (1%)
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Measures

Constructs of interest were measured using a battery of assessment routinely used with military samples (see Table 2 and Supplementary Materials Appendix A for psychometric information; R. C. Kessler et al., 2013).

Table 2.

Scale-level correlations and psychometric information.

  1 2 3 4 5
1. PTSD Symptoms .        
2. Unit Cohesion −.23 .      
3. Deployment Stressors .25 −.03 .    
4. Use of Humor −.21 .26 .44 .  
5. Morale
 −.22
 .42
 −.06
 .23
 .
Mean (SD) 1.34 (.67) 3.72 (.97) 1.03 (.73) 3.99 (1.06) 3.23 (1.11)
Skew 2.81 −.67 .12 −.82 −.22
Kurtosis 8.74 .05 −1.09 −.15 −.55
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Correlations greater than |.03| are significant at p < .05. Kolmogorov-Smirnov tests indicated that all variables violated the assumption of normal distribution (p < .05).

PTSD symptoms

Participants rated the severity of PTSD symptoms they experienced during the past 30 days using nine items from the PTSD Checklist (PCL) for the DSM-IV (Bliese et al., 2008; Weathers et al., 1993). Participants rated how often they experienced each symptom on a five-point scale ranging from never to six or more times a week. STARRS researchers validated the nine-item measure against clinical diagnoses of PTSD based on the Structured Clinical Interview for DSM-IV (R. Kessler et al., 2014).

Deployment stressors

Participants indicated the number of deployment stressors they had experienced during all of their deployments using nine self-report items from the Deployment Risk and Resilience Inventory (DRRI; King et al., 2006). The DRRI assessed risk factors for military personnel deployed to war zones or other hazardous environments, such as deployment social support, sexual harassment, and combat experiences. The items included in the AAS assessed aspects of deployment stress such as combat experiences, physical harm, exposure to death/destruction, experiences of physical and sexual assault, and bullying. Participants rated their responses on a five-point scale indicating the frequency with which a stressor was experienced (zero, one, two-four, five-nine, or ten or more times). A mean score of the nine items was used to represent the average number of deployment stressors experienced across all categories for use in the current analysis.

Perceived unit cohesion

We measured perceived unit cohesion using five items from a questionnaire developed by the Joint Mental Health Advisory Team 7 (J-MHAT 7) in 2007–2009: “I can rely on other members of my unit for help if I need it,” “I can open up and talk to my first line leaders if I need help,” “I respect the Non-Commissioned Officers in my unit,” “I respect the Officers in my unit,” and “My leaders take a personal interest in the well-being of all the Servicemembers in my unit.” Participants rated how much they agreed with each item on a five-point scale ranging from strongly disagree to strongly agree.

Morale

We assessed morale using a single, self-report, face-valid item from the J-MHAT 7 questionnaire (“How would you rate your morale?”). Participants rated their self-perceived morale on a five-point scale ranging from very low to very high.

Use of humor in stressful situations

Participants rated their ability to use humor during tense situations with an item from the Hurricane Katrina Community Advisory Group (“How would you rate your ability to handle stress in each of the following ways? Keep your sense of humor in tense situations.”) Participants indicated their use of humor to cope with stressful situations with a five-point scale ranging from poor to excellent.

Data analysis

We ran preliminary descriptive statistics using SPSS (IBM Corp, 2017) and primary analyses using Mplus (Muthén & Muthén, 2017). We identified predictors of missingness for all variables using a series of logistic regressions and found that missingness was primarily predicted by other predictor variables in the analysis. We used Robust Maximum Likelihood (MLR; Yuan & Bentler, 2000) to account for bias from missing data and from non-normality during analysis.

We used confirmatory factor analysis (CFA; Thompson, 1997) to evaluate if items hypothesized to operationalize perceived unit cohesion and PTSD symptoms represented unitary constructs that could be used as a latent variable in the following analyses. We conceptualized perceived unit cohesion and PTSD symptoms as latent variables in the final analysis due to the lack of robust measurement available for these constructs. We then used SEM (Mair & Hatzinger, 2007; Muthén, 1983) to examine how perceived unit cohesion, morale, and humor were associated with each other and with PTSD symptoms while accounting for the influence of deployment stressors. We used SEM to help account for bias from missing data using full-information maximum likelihood techniques (FIML; Enders, 2001) to better measure and analyze ambiguous concepts via latent constructs (Gefen et al., 2000) and to account for the correlation between predictor variables. We additionally used SEM due to its ability to handle single-item indicators (Petrescu, 2013). First, we regressed PTSD symptoms on perceived unit cohesion, morale, humor, and deployment stressors. We also correlated all predictors to examine if they uniquely predict PTSD symptoms after accounting for their overlap.

We used goodness of fit measures CFI (Bentler, 1990), TLI (Tucker & Lewis, 1973), and Satorra-Bentler Corrected Chi-Square (Satorra & Bentler, 2010), and absolute fit indices RMSEA (Steiger & Lind, 1980) and SRMR (Hu & Bentler, 1995) to measure model fit across the CFA and SEM. Given that chi-square tends to be overly sensitive with large sample sizes (Barrett, 2007), we gave more weight to the other fit indices. We used guidelines Kenny (2015) and Schreiber et al. (2006) for model fit interpretation. We reported standardized regression coefficients to interpret effect size due to the large sample (Kaplan et al., 2014; Kelley & Preacher, 2012; Lin et al., 2013).

Results

The CFA for perceived unit cohesion (CFI = .96, TFI = .93, SRMR = .03, RMSEA = .10) and PTSD symptoms (CFI = .93, TFI = .91, SRMR = .04, RMSEA = .07) converged with an adequate model fit, with large loadings of each item on the latent variable (see Supplementary Materials Appendix B and C). Model fit indices of the initial model (CFI = .94, TLI = .92, SRMR = .03, RMSEA = .05) and the mediation model (CFI = .94, TLI = .93, SRMR = .03, RMSEA = .05) demonstrated good fit. In this model, deployment stressors had the largest effect on PTSD symptoms, although perceived unit cohesion, morale, and use of humor had effects over and above that, although these effects were small (see Supplementary Materials Appendix D).

Post hoc analyses

In addition to our hypothesized model in which the relative direct effects of deployment stressors, morale, perceived unit cohesion, and humor on PTSD symptoms were examined, a series of post hoc analyses in which we examined the mediating and moderating effects of morale, perceived unit cohesion, and humor on the association between deployment stressors on PTSD symptoms. We examined indirect effects using the model indirect command in Mplus (Muthén & Muthén, 2017) and examined moderation effects using simple slopes analysis (Robinson et al., 2013). For the moderation model, we compared the AIC and BIC of the moderation model to the initial model (Kuha, 2004) to test model fit. In the mediation model, deployment stressors were negatively associated with perceived unit cohesion, morale, and use of humor such that they mediated its effect on PTSD symptoms, although in each case the size of these effect was miniscule (see Figure 1). The moderation model (AIC = 306568.94, BIC = 306949.01) demonstrated better model fit than the initial model (AIC = 626409.23, BIC = 626870.19). In the moderation model, deployment stressors and all resilience factors had small direct effects of PTSD symptoms (β = |.07-.20|; see Figure 2). The interactions between the resilience factors and deployments stressors were all statistically significant (see Figure 3). Simple slopes analysis then revealed that although in each case the effect of the moderations was small, when taken together they reduced the effect of deployments stressors on PTSD symptoms to a minimal size (see Supplementary Materials Appendix E).

Figure 1.

Figure 1.

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Structural Model of the Indirect Effects of Deployment Stressors on PTSD symptoms. Standardized factor loadings are shown, with 95% confidence intervals in parentheses.

Figure 2.

Figure 2.

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Structural Model of the Interaction between Resilience Factors and Deployment Stressors on PTSD symptoms. Standardized factor loadings are shown, with 95% confidence intervals in parentheses.

Figure 3.

Figure 3.

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Interactions of Resilience Factors and Deployment Stressors on PTSD symptoms. Graphs were made using standardized regression coefficients.

Discussion

In this study, we explored the associations between perceived unit cohesion, morale, and use of humor on PTSD symptoms. We found that perceived unit cohesion, morale, and use of humor uniquely contributed to lower PTSD symptoms after accounting for deployment stressors. We additionally found that these three factors partially buffered the association between deployment stressors and PTSD symptoms, though the strength of the effects were weak. The resilience factors also mitigated the impact of deployment stressors on PTSD symptoms. These results from our study align with previous research and add to the existing scientific literature by highlighting the association between these resilience factors and PTSD symptoms vis-à-vis via deployment stress.

Research and clinical implications

The results indicate that perceived unit cohesion, morale, and use of humor were both associated with each other and contributed uniquely to lower PTSD symptoms. Additionally, though it does not appear that resilience factors explain the effect of deployment stressors on PTSD, they do appear to influence it. Our findings raise the question of whether bolstering resilience factors like morale and unit cohesion might mitigate the detrimental effects of deployment stressors. Additionally, our findings suggest that while resilience factors, specifically morale and unit cohesion, are related, stronger buffering effects can be found with a combination of factors. Future research should also examine whether increasing resilience factors prior to combat exposure has a stronger buffering effect on deployment stressors than attempting to increase resilience after deployment stressors have already been experienced. Lastly, while our findings are consistent with studies demonstrating negative associations between these resilience factors and PTSD symptoms, the benefits of these factors may be limited in more acutely stressful situations or in situations of prolonged stress. It is also possible that benefits seen from these factors may decrease at higher levels of endorsement. For example, use of humor may have serve as a resilience factor until service members begin to use humor to avoid adaptively dealing with stressful situations, which may lead to higher rather than lower PTSD symptoms. Future research should explore these possibilities and examine if there is an optimal endorsement of resilience factors in different situations.

Study findings have implications for resilience-building programs in the military. For example, future interventions could focus on developing, implementing, and evaluating a curriculum to strengthen service members’ existing resilience factors (e.g., use of humor, morale) and reinforce unit-level factors (e.g., unit cohesion). For example, unit activities that involve perceived incremental and achievable objectives (e.g., increasing morale), require unit members to work together (e.g., increasing cohesion), and require unit leaders and members to reinforce each other when healthy coping mechanisms are used (e.g., increasing use of humor and other coping strategies) may augment the existing programs (Beal et al., 2003; Britt & Dickinson, 2006; Kirschner et al., 2018). Most resilience-building programs use individual-level factors (e.g., realism, behavioral control) that have received weak to moderate levels of research support (Meredith et al., 2011). These programs may derive additional benefits by focusing on cohesion, morale, and use of humor, such as increased performance and a variety of other factors, may help resilience-building programs overcome common barriers like leadership buy-in (Meredith et al., 2011). Findings from one of the more thoroughly evaluated programs, Resiliency Training, support these findings. Resiliency Training includes interventions building resilience factors such as unit cohesion and positive coping/thinking and has been found to be efficacious at reducing PTSD symptoms (Meredith et al., 2011). However, while morale and unit cohesion are associated, they are separate constructs. Incorporating morale, as described above, into existing resilience-building programs may further increase their efficacy.

Future directions and study limitations

This study has several limitations, which suggest other directions for future research. First, possibly due to the practical restraints of implementing a large-scale study (Fisher, Matthews, & Gibbons, 2015), the original research team used relatively impoverished measures of their constructs of interest (e.g., only 9 of the 17 items of the PCL, single-item measurements of humor and morale), leading to constrained variance in these measures and potentially reduced effect sizes. Additionally, the use of solely self-report measures is potentially problematic, as self -report measures are prone to bias (Donaldson & Grant‐Vallone, 2002), in part due to potential mental health stigma common in military service members. These limitations may have prevented a more detailed exploration of the interaction between severity of deployment stressors and resilience factors on PTSD symptoms, such as the curvilinear relationship between unit cohesion and PTSD symptoms at high levels of stress that some research has found (Fontana & Rosenheck, 1994). Future research may benefit from using a structured interview to assess PTSD symptoms (e.g., the Clinician-Administered PTSD Scale for DSM-5 [CAPS-5]; Weathers et al., 2018) as well as more robust self-report measures of use of humor (e.g., Coping Humor Scale; Martin & Lefcourt, 1983) and/or morale (e.g., Military Morale Scale; Britt & Dickinson, 2006). Another limitation is the cross-sectional study design, which prevents causal interpretation of findings (Davies & Pickles, 1985). The lack of military career information, such as rank and military occupational specialty, also prevents controlling for the effects of this information on outcomes, both of which are important to consider in exploring PTSD symptoms in military personnel. Future research should address these limitations by using more robust and multi-method measurement of a broader range of study constructs (e.g., military occupational specialty) using a longitudinal design and by exploring non-linear effects. In this study we examined the association between unit cohesion, morale, and use of humor on PTSD symptoms and found that these factors were negatively associated with PTSD symptoms even when controlling for the effect of deployment stressors. Future research could expand upon these findings and contribute to the ongoing process of improving programs for military personnel that may lessen some of the detrimental impacts of the invisible wounds of war.

Supplementary Material

Supplemental Material
HMLP_A_1996103_SM4932.docx (459.9KB, docx)

Acknowledgments

This publication is based on public use data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). The data are available from the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan (http://doi.org/10.3889/ICPSR35197.v2). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the views of the Army STARRS investigators, funders, Department of the Army, or Department of Defense.

Funding Statement

This work was supported by the National Institute of Mental Health [U01MH087981].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website

References

  1. Armistead‐Jehle, P., Johnston, S. L., Wade, N. G., & Ecklund, C. J. (2011). Posttraumatic stress in U.S. marines: The role of unit cohesion and combat exposure. Journal of Counseling & Development, 89(1), 81–88. 10.1002/j.1556-6678.2011.tb00063.x [DOI] [Google Scholar]
  2. Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815–824. 10.1016/j.paid.2006.09.018 [DOI] [Google Scholar]
  3. Bass, B. M., Avolio, B. J., Jung, D. I., & Berson, Y. (2003). Predicting unit performance by assessing transformational and transactional leadership. Journal of Applied Psychology, 88(2), 207–218. 10.1037/0021-9010.88.2.207 [DOI] [PubMed] [Google Scholar]
  4. Beal, D. J., Cohen, R. R., Burke, M. J., & McLendon, C. L. (2003). Cohesion and performance in groups: A meta-analytic clarification of construct relations. Journal of Applied Psychology, 88(6), 989–1004. 10.1037/0021-9010.88.6.989 [DOI] [PubMed] [Google Scholar]
  5. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. 10.1037/0033-2909.107.2.238 [DOI] [PubMed] [Google Scholar]
  6. Besser, A., Weinberg, M., Zeigler-Hill, V., Ataria, Y., & Neria, Y. (2015). Humor and trauma-related psychopathology among survivors of terror attacks and their spouses. Psychiatry, 78(4), 341–353. 10.1080/00332747.2015.1061312 [DOI] [PubMed] [Google Scholar]
  7. Bliese, P. D., Wright, K. M., Adler, A. B., Cabrera, O., Castro, C. A., & Hoge, C. W. (2008). Validating the primary care posttraumatic stress disorder screen and the posttraumatic stress disorder checklist with soldiers returning from combat. Journal of Consulting and Clinical Psychology, 76(2), 272–281. 10.1037/0022-006X.76.2.272 [DOI] [PubMed] [Google Scholar]
  8. Boe, O. (2015). Building resilience: The role of character strengths in the selection and education of military leaders. International Journal of Emergency Mental Health and Human Resilience, 17(4). 714–716. 10.4172/1522-4821.1000301 [DOI] [Google Scholar]
  9. Britt, T. W., Adler, A. B., Bliese, P. D., & Moore, D. (2013). Morale as a moderator of the combat exposure-PTSD symptom relationship. Journal of Traumatic Stress, 26(1), 94–101. 10.1002/jts.21775 [DOI] [PubMed] [Google Scholar]
  10. Britt, T. W., Adler, A. B., Sawhney, G., & Bliese, P. D. (2017). Coping strategies as moderators of the association between combat exposure and posttraumatic stress disorder symptoms. Journal of Traumatic Stress, 30(5), 491–501. 10.1002/jts.22221 [DOI] [PubMed] [Google Scholar]
  11. Britt, T. W., & Dickinson, J. M. (2006). Morale during military operations: A positive psychology approach. In Britt T. W., Castro C. A., & Adler A. B. (Eds.), Military performance. Military life: The psychology of serving in peace and combat: Military performance (pp. 157–184). Praeger Security International. [Google Scholar]
  12. Britt, T. W., & Oliver, K. K. (2013). Morale and cohesion as contributors to resilience. In Sinclair R. R. & Britt T. W. (Eds.), Building psychological resilience in military personnel: Theory and practice (pp. 47–65). American Psychological Association. 10.1037/14190-003 [DOI] [Google Scholar]
  13. Davies, R. B., & Pickles, A. R. (1985). Longitudinal versus cross-sectional methods for behavioural research: A first-round knockout. Environment and Planning A: Economy and Space, 17(10), 1315–1329. 10.1068/a171315 [DOI] [Google Scholar]
  14. Donaldson, S. I., & Grant‐Vallone, E. J. (2002). Understanding self-report bias in organizational behavior research. Journal of Business and Psychology, 17(2), 245–260. 10.1023/A:1019637632584 [DOI] [Google Scholar]
  15. Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling: A Multidisciplinary Journal, 8(1), 128–141. 10.1207/S15328007SEM0801_7 [DOI] [Google Scholar]
  16. Escolas, S. M., Pitts, B. L., Safer, M. A., & Bartone, P. T. (2013). The protective value of hardiness on military posttraumatic stress symptoms. Military Psychology, 25(2), 116–123. 10.1037/h0094953 [DOI] [Google Scholar]
  17. Fisher, G., Matthews, R., & Gibbons, A. (2015). Developing and investigating the use of single-item measures in organizational research. Journal of Occupational Health Psychology, 21(1), 3–123. 10.1037/a0039139 [DOI] [PubMed] [Google Scholar]
  18. Fletcher, D., & Sarkar, M. (2013). Psychological resilience. European Psychologist, 18(1), 12–23. 10.1027/1016-9040/a000124 [DOI] [Google Scholar]
  19. Fontana, A., & Rosenheck, R. (1994). Posttraumatic stress disorder among Vietnam theater veterans. A causal model of etiology in a community sample. The Journal of Nervous and Mental Disease, 182(12), 677–684. 10.1097/00005053-199412000-00001 [DOI] [PubMed] [Google Scholar]
  20. Garrick, J. (2006). The humor of trauma survivors. Journal of Aggression, Maltreatment & Trauma, 12(1–2), 169–182. 10.1300/J146v12n01_09 [DOI] [Google Scholar]
  21. Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–77. 10.17705/1CAIS.00407 [DOI] [Google Scholar]
  22. Hu, L.-T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Sage Publications, Inc. [Google Scholar]
  23. IBM Corp . (2017). IBM SPSS statistics for windows, version 25.0. [Google Scholar]
  24. Jones, N., Seddon, R., Fear, N. T., McAllister, P., Wessely, S., & Greenberg, N. (2012). Leadership, cohesion, morale, and the mental health of U.K. armed forces in Afghanistan. Psychiatry: Interpersonal and Biological Processes, 75(1), 49–59. 10.1521/psyc.2012.75.1.49 [DOI] [PubMed] [Google Scholar]
  25. Kaplan, R. M., Chambers, D. A., & Glasgow, R. E. (2014). Big data and large sample size: A cautionary note on the potential for bias. Clinical and Translational Science, 7(4), 342–346. 10.1111/cts.12178 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137–152. 10.1037/a0028086 [DOI] [PubMed] [Google Scholar]
  27. Kenny, D. A. (2015). Measuring model fit. http://www.davidakenny.net/cm/fit.htm
  28. Kessler, R. C., Colpe, L. J., Fullerton, C. S., Gebler, N., Naifeh, J. A., Nock, M. K., Sampson, N. A., Schoenbaum, M., Zaslavsky, A. M., Stein, M. B., Ursano, R. J., & Heeringa, S. G. (2013). Design of the army study to assess risk and resilience in servicemembers (army STARRS). International Journal of Methods in Psychiatric Research, 22(4), 267–275. 10.1002/mpr.1401 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kessler, R., Heeringa, S., Stein, M., Colpe, L., Fullerton, C., Gilman, S., Hwang, I., Naifeh, J., Nock, M., Petukhova, M., Sampson, N., Schoenbaum, M., Zaslavsky, A., & Ursano, R.; Collaborators, on . (2014). Detailed response to: Letter to the editor: Problems with conclusions in army STARRS publications. [Google Scholar]
  30. King, L. A., King, D. W., Vogt, D. S., Knight, J., & Samper, R. E. (2006). Deployment risk and resilience inventory: A collection of measures for studying deployment-related experiences of military personnel and veterans. Military Psychology, 18(2), 89–120. 10.1207/s15327876mp1802_1 [DOI] [Google Scholar]
  31. Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano, J. (2018). From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning, 13(2), 213–233. 10.1007/s11412-018-9277-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kuha, J. (2004). AIC and BIC: Comparisons of assumptions and performance. Sociological Methods & Research, 33(2), 188–229. 10.1177/0049124103262065 [DOI] [Google Scholar]
  33. Lin, M., Lucas, H. C., & Shmueli, G. (2013). Too big to fail: Large samples and the p-value problem. Information Systems Research, 24(4), 906–917. 10.1287/isre.2013.0480 [DOI] [Google Scholar]
  34. MacCoun, R. J., & Hix, W. M. (2010). Unit cohesion and military performance. In RAND Corporation (Ed.), Sexual orientation and U.S. Military personnel policy: An update of RAND’s 1993 study (pp. 137–165). Santa Monica, CA: RAND. 10.7249/MG1056 [DOI] [Google Scholar]
  35. Maddi, S. R. (2006). Hardiness: The courage to grow from stresses. The Journal of Positive Psychology, 1(3), 160–168. 10.1080/17439760600619609 [DOI] [Google Scholar]
  36. Mair, P., & Hatzinger, R. (2007). Extended rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1–20. 10.18637/jss.v020.i09 [DOI] [Google Scholar]
  37. Manning, F. J. (1994). Morale and cohesion in military psychiatry. In Jones F. D., Sparacino L. R., Wilcox V. L., and Rothberg J. M., (Eds.), Military psychiatry: Preparing in peace for war (pp. 1–18). Washington, DC: Office of The Surgeon General at TMM Publications, Borden Institute, Walter Reed Army Medical Center. 10.1037/e629552011-002 [DOI] [Google Scholar]
  38. Martin, R. A., & Lefcourt, H. M. (1983). Sense of humor as a moderator of the relation between stressors and moods. Journal of Personality and Social Psychology, 45(6), 1313–1324. 10.1037/0022-3514.45.6.1313 [DOI] [Google Scholar]
  39. Meadows, S. O., Engel, C. C., Collins, R. L., Beckman, R. L., Cefalu, M., Hawes-Dawson, J., Waymouth, M., Kress, A. M., Sontag-Padilla, L., Ramchand, R., & Williams, K. M. (2018). 2015 health related behaviors survey (HBRS): Mental and emotional health among U.S. active-duty service members. RAND Corporation. 10.7249/RB9955.3 [DOI] [Google Scholar]
  40. Meredith, L. S., Sherbourne, C. D., Gaillot, S. J., Hansell, L., Ritschard, H. V., Parker, A. M., & Wrenn, G. (2011). Promoting psychological resilience in the U.S. military. Rand Health Quarterly, 1(2), 1–158. https://www.rand.org/pubs/monographs/MG996.html [PMC free article] [PubMed] [Google Scholar]
  41. Mesmer-Magnus, J., Glew, D., & Viswesvaran, C., (Vish). (2012). A meta-analysis of positive humor in the workplace. Journal of Managerial Psychology, 27(2), 155–190. 10.1108/02683941211199554 [DOI] [Google Scholar]
  42. Muthén, B. (1983). Latent variable structural equation modeling with categorical data. Journal of Econometrics, 22(1–2), 43–65. 10.1016/0304-4076(83)90093-3 [DOI] [Google Scholar]
  43. Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Muthén & Muthén. [Google Scholar]
  44. Petrescu, M. (2013). Marketing research using single-item indicators in structural equation models. Journal of Marketing Analytics, 1(2), 99–117. 10.1057/jma.2013.7 [DOI] [Google Scholar]
  45. Rice, V., & Liu, B. (2016). Personal resilience and coping part II: Identifying resilience and coping among U.S. military service members and veterans with implications for work. Work, 54(2), 335–350. 10.3233/WOR-162301 [DOI] [PubMed] [Google Scholar]
  46. Richardson, L. K., Frueh, B. C., & Acierno, R. (2010). Prevalence estimates of combat-related PTSD: A critical review. The Australian and New Zealand Journal of Psychiatry, 44(1), 4–19. 10.3109/00048670903393597 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Robinson, C. D., Tomek, S., & Schumacker, R. E. (2013). Tests of moderation effects: Difference in simple slopes versus the interaction term. Multiple Linear Regression Viewpoints, 39(1), 16–24. [Google Scholar]
  48. Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243–248. 10.1007/s11336-009-9135-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–337. 10.3200/JOER.99.6.323-338 [DOI] [Google Scholar]
  50. Siebold, G. L. (2006). Military group cohesion. In Britt T. W., Castro C. A., & Adler A. B. (Eds.), Military performance. Military life: The psychology of serving in peace and combat (Vol. 1, pp. 185–201). Praeger Security International. [Google Scholar]
  51. Sliter, M., Kale, A., & Yuan, Z. (2014). Is humor the best medicine? The buffering effect of coping humor on traumatic stressors in firefighters. Journal of Organizational Behavior, 35(2), 257–272. 10.1002/job.1868 [DOI] [Google Scholar]
  52. Steiger, J. H., & Lind, J. C. (1980). Statistically-based tests for the number of common factors [Paper presentation]. Psychometric society annual meeting, Iowa City, IA. [Google Scholar]
  53. Thompson, B. (1997). The importance of structure coefficients in structural equation modeling confirmatory factor analysis. Educational and Psychological Measurement, 57(1), 5–19. 10.1177/0013164497057001001 [DOI] [Google Scholar]
  54. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. 10.1007/BF02291170 [DOI] [Google Scholar]
  55. Ursano, R. J., Stein, M. B., Kessler, R. C., & Heeringa, S. G. (2018, December 3). Army study to assess risk and resilience in servicemembers (STARRS). ICPSR35197-v2. Inter-university Consortium for Political and Social Research [distributor]. 10.3886/ICPSR35197.v2 [DOI] [Google Scholar]
  56. Vasterling, J. J., Proctor, S. P., Friedman, M. J., Hoge, C. W., Heeren, T., King, L. A., & King, D. W. (2010). PTSD symptom increases in Iraq-deployed soldiers: Comparison with nondeployed soldiers and associations with baseline symptoms, deployment experiences, and postdeployment stress. Journal of Traumatic Stress, 23(1), 41–51. 10.1002/jts.20487 [DOI] [PubMed] [Google Scholar]
  57. Vogt, D. S., Samper, R. E., King, D. W., King, L. A., & Martin, J. A. (2008). Deployment stressors and posttraumatic stress symptomatology: Comparing active duty and national guard/reserve personnel from gulf war I. Journal of Traumatic Stress, 21(1), 66–74. 10.1002/jts.20306 [DOI] [PubMed] [Google Scholar]
  58. Ward, R. N., Brady, A. J., Jazdzewski, R., & Yalch, M. (2021). Stress, resilience, and coping. In Pradhan R., and Kumar U. (Eds.), Emotion, wellbeing and resilience: Theoretical perspectives and practical applications (pp. 3–14). Apple Academic Press - of Taylor and Francis Group; . [Google Scholar]
  59. Weakliem, D. L., & Frenkel, S. J. (2006). Morale and workplace performance. Work and Occupations, 33(3), 335–361. 10.1177/0730888406290054 [DOI] [Google Scholar]
  60. Weathers, F. W., Bovin, M. J., Lee, D. J., Sloan, D. M., Schnurr, P. P., Kaloupek, D. G., Keane, T. M., & Marx, B. P. (2018). The clinician-administered PTSD scale for DSM-5 (CAPS-5): Development and initial psychometric evaluation in military veterans. Psychological Assessment, 30(3), 383–395. 10.1037/pas0000486 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., & Keane, T. M. (1993). The PTSD checklist (PCL): Reliability, validity and diagnostic utility [Paper presented]. 9th annual conference of the ISTSS, San Antonio, TX. [Google Scholar]
  62. Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with non-normal missing data. Sociological Methodology, 30(1), 165–200. 10.1111/0081-1750.00078 [DOI] [Google Scholar]

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