Posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and depression are common among the military Veteran population and are often comorbid processes (Elliot et al., 2015). For Veterans returning from Iraq and Afghanistan, resilient personality and qualities associated with resilience, such as social support, co-occur with lower levels of depression and PTSD (Elliot et al., 2015). Vyas et al. (2016) examined a retrospective cohort of mental health care–seeking service members. Veterans with low resilience were at significantly greater odds for developing physical, behavioral, and mental health conditions, particularly sleep disorders, perceived stress, and depression, compared to Veterans with moderate/high resilience (Vyas et al., 2016).
Resilience has been found to be an important factor in Veterans' response to trauma. Resilience is the ability to bounce back from adversity (Schwab et al., 2006) and return to a healthy and stable state physically, cognitively, emotionally, and behaviorally (Rice & Liu, 2016). Resilience is reliably quantifiable (Schwab et al., 2006). According to Kalisch et al. (2017), resilience consists of multifactorial factors and is complex in nature. Resilience is viewed as comprising static or unmodifiable factors (e.g., age, sex) and dynamic or modifiable factors (e.g., social support) (Holland & Schmidt, 2015). Research on resilience is of importance because high-risk populations with factors known to influence resilience may be identified and some aspects of resilience may be modified through interventions (Neils-Strunjas et al., 2017; Rice & Liu, 2016).
Veterans are students of special concern to college campuses as a result of their experiences, demographics, and needs (Durdella & Kim, 2012; Rudd, Goulding, & Bryan, 2011; Whiteman, Barry, Mroczek, & MacDermid Wadsworth, 2013). Student Veterans may have financial resources through the post-9/11 GI Bill to cover all or most college expenses (Today's Military, 2017), and more than 1 million Veterans have taken advantage of those benefits since the bill's enactment in 2008 (Cate, 2014). However, to receive college benefits, students must successfully matriculate through a college curriculum. College retention may be measured by following students as they move from year to year in a college program. However, in some cases, extended follow up is not possible given the resources needed to track students. Other ways of predicting academic success include grade point average (GPA) (Durdella & Kim, 2012) and academic persistence decisions (i.e., the likelihood of remaining enrolled in school) (Whiteman et al., 2013). Veteran outcomes have been associated with resilience, depression, PTSD, and TBI; however, investigation of these variables has been limited in student Veteran populations (Campbell & Riggs, 2015).
The interrelationship of resilience, depression, PTSD, and TBI and their impact on academic outcomes have not been explored in the student military Veteran population; however, information about how these variables interact to impact student Veteran college success is important for rehabilitation professionals, student advisors, and counselors (Zinger & Cohen, 2010). One study of 117 student Veterans found that depression and PTSD were significantly (p < 0.01) correlated with self-report on the Student Adaptation to College Questionnaire (Campbell & Riggs, 2015). Students who indicated they were depressed or had symptoms of PTSD had lower levels of academic adjustment. PTSD symptoms are common among Veteran college students, with prevalence rates ranging from 25.6% (Campbell & Riggs, 2015) to 46% (Rudd et al., 2011). One aspect of PTSD, hyperarousal, in particular, negatively impacted academic adjustment (Campbell & Riggs, 2015).
An important factor that may influence resilience is longer length of time in service. Although deployment has been associated with PTSD and major depression (Kline et al., 2010), Rice and Liu (2016) found that for Veterans (not active-duty), the longer the time in service, the higher their resilience scores. The results from Rice and Liu (2016) indicate that length of service should be included as a variable in the development of multifactorial models of resilience.
Many returning Veterans struggle with reintegration into daily life (Vyas et al., 2016), including the demands of college (Campbell & Riggs, 2015). The current study was performed to examine factors that influence student Veterans' academic success in college. The primary focus of the study was to determine whether individual resilience had an independent impact on academic outcomes. Investigation into the impact of variables such as resilience, depression, PTSD, and TBI has been limited in student Veteran populations (Campbell & Riggs, 2015), and the current study contributes to knowledge in this important area. In addition, based on the literature review, the authors sought to determine whether the amount of student Veterans' military experience moderated the effect of PTSD, TBI, depression, and resilience on academic performance.
The following research questions were explored:
- Does resilience affect academic outcomes for student military Veterans while accounting for the impact of PTSD, TBI, and depression?
- Does the amount of student Veterans' military experience moderate the effect of PTSD, TBI, depression, and resilience on academic performance?
The current descriptive cohort pilot study was conducted at a mid-southern university. Institutional Review Board (IRB) approval was obtained. Statistical analysis of survey data was used to investigate the multifactorial relationship among resilience, depression, PTSD, TBI, and academic outcomes in a college student Veteran population.
Participants included current students and recent alumni of a mid-Southern university system. To qualify as a student Veteran, participants must have reported past or current service in any branch of the U.S. military, including Active Duty, ROTC, Reserves, or National Guard. Student Veterans were recruited through collaboration with the university's Military Student Services office, Veterans Upward Bound program, and Student Veterans Alliance. Data were collected from 79 student Veterans; however, missing data in the dependent variable resulted in an operational sample of 77 respondents.
Mean age for all participants was 36.15 (SD = 12.45 years). Individuals with less military experience were younger (mean age = 31.72, SD = 11.27 years) than individuals with more military experience (mean age = 46.13, SD = 8.73 years). Participants were predominantly male (82.1%) and reported their marital status as single (32.1%), married (41%), or separated/divorced/widowed (26.9%). Individuals with less military experience were more likely to be single (44.4%) than individuals with more experience (4.2%). Individuals with more military experience were more likely to be married (58.3%) than individuals with less experience (33.3%).
The survey was created in Qualtrics® Survey Software and comprised sections addressing resilience, depression, PTSD, TBI, academic outcomes, and demographics. Questions measuring resilience, depression, PTSD, and TBI were drawn from existing standardized instruments. Academic outcomes were measured by student-reported GPA and number of disruptions to degree progression.
Resilience. The Connor-Davidson Resilience Scale 25 (CD-RISC-25) was used to measure resilience (Connor & Davidson, 2003). Internal consistency for the CD-RISC is high, with a Cronbach's alpha of 0.89 and test–retest reliability of 0.87 (Connor & Davidson, 2003). The resilience scale comprises 25 questions with answers ranging from 0 (not true at all) to 4 (nearly true all the time). Possible resilience scores ranged from 0 to 100, with scores ≥76 indicating high resilience.
Posttraumatic Stress Disorder. The PTSD Checklist–Military Version (PCL-M) was used to obtain PTSD scores (Kimerling, 2009; Weathers, Huska, & Keane, 1991). The PTSD checklist included 17 questions with answers ranging from 1 (not at all) to 5 (extremely). A score ≥44 indicates a positive screen for PTSD. Scores ranged from 17 to 85, with higher scores reflecting a greater probability of being diagnosed with PTSD. The PCL has high internal consistency, with an overall Cronbach's alpha of 0.97.
Traumatic Brain Injury. The 3 Question Defense and Veterans Brain Injury Center (DVBIC) Screening Tool was used to evaluate symptoms of TBI (Schwab et al., 2006). The TBI screening tool was designed for use with a military population. The instrument comprises three components: injury, alteration of consciousness, and continuing symptoms. If the respondent reported at least one injury and at least one alteration of consciousness that occurred immediately after the injury or injuries, the participant was coded as a positive screen for TBI (score = 1). All others were considered negative screens (score = 0).
Psychometric data for the DVBIC Screening Tool, also known as the Brief Traumatic Brain Injury Screen (BTBIS), are limited. The authors of the screening tool cross-validated the DVBIC Screening Tool against two other instruments (Schwab et al., 2006). They found a moderate association between the DVBIC Screening Tool and the Quarterly Survey (ϕ = 0.546, p = 0.001), and a low association between the DVBIC Screening Tool and the Computerized TBI Questionnaire (ϕ = 0.479, p = 0.001).
Depression. The Patient Health Questionnaire-2 (PHQ-2) was used to measure symptoms of depression (Kroenke, Spitzer, & Williams, 2003). This version comprises two questions with answers ranging from 0 (not at all) to 3 (nearly every day). One additional question was added from the long version of the PHQ-9, which stated, “If you checked off any of the problems above, how difficult have those problems made it for you to do your work?” Answers ranged from 0 (not difficult at all) to 3 (extremely difficult). The range of possible scores was 0 to 6. For the current study, a positive screen for depression was defined as a score ≥3, consistent with the recommendation of Kroenke et al. (2003).
The questionnaire developers report that for scores ≥3, the PHQ-2 has a sensitivity of 83% and a specificity of 92% for major depression (Kroenke et al., 2003).
Academic Outcomes. Academic outcomes included two self-reported measures: GPA and number of disruptions to degree progression. GPA was self-reported by student Veterans. Number of disruptions comprised the total number of courses from which participants reported they had dropped, withdrawn, or received a grade of incomplete.
Demographic Data. Demographic factors included in the analysis were age, gender, marital status, number of years in the military, and number of tours of duty (Table 1).
Student Veterans were provided with a hyperlink to the survey. Upon accessing the electronic survey, a description of the study and consent form were viewed. Student Veterans gave consent by choosing to enter the survey. Participants then responded to items included in the screening measures, academic outcomes questions, and demographic questions. Upon completing the survey, participants were offered an opportunity to confidentially provide an e-mail address allowing them to receive a $10 electronic gift card. Each participant also received a custom military challenge coin. In addition, all participants were provided a Mental Health Services resource list in the event that survey questions caused them to become concerned regarding any existing symptoms or conditions.
All participant responses were recorded with a PIN number to protect participant identity. E-mail addresses that some participants chose to provide for the purpose of receiving the incentive were collected in a separate database from the study response data to prevent identification of participants.
Data were exported from Qualtrics to Microsoft® Excel, and then imported into SPSS 24. A two-step cluster analysis was performed, clustering on years of experience and number of tours of duty. Preliminary analysis of the data included descriptive statistical analysis and single sample t tests. A just-identified path model was developed, including all aforementioned covariates. Insignificant paths were trimmed from the full, just-identified model using AMOS 24 to identify the trimmed, unconstrained path model (Figure). Multigroup moderation was tested using the chi-square difference method to determine if the various paths differed across clusters. Multigroup path analysis was then performed to evaluate the manner in which the aforementioned covariates influenced academic performance across the separate clusters. In the path analysis, continuous scores were used for the resilience, PTSD, and depression variables, and dichotomous scores for the TBI variable. To identify the incidence of positive screenings, dichotomous scores were used for PTSD, TBI, and depression screening results.
Trimmed path model of predictors of academic performance among student military Veterans. Separate analyses were performed on trimmed model for more experienced and less experienced clusters (see Table 4).
Note. PTSD = posttraumatic stress disorder; TBI = traumatic brain injury.
The prevalence of PTSD symptoms within the current sample was significantly higher at 32% (t = 5.369) than the estimated U.S. population prevalence of 3.5% (Kessler, Chiu, Demler, & Walters, 2005), although TBI and depression levels were approximately equivalent to population estimates (Frost, Farrer, Primosch, & Hedges, 2013; Substance Abuse and Mental Health Services Administration, 2016). Of participants, 35% had at least one positive screening (PTSD, depression, or TBI), and approximately 20% screened positive for multiple conditions (Table 2).
Incidence of Positive Screenings
Two-step cluster analysis identified two separate groups within the sample, which differentiated based on military experience and tours of duty. The group with less experience was found to have an average of 4.16 years of military service and had been deployed for an average of 1.19 tours. Conversely, participants with more experience reported 18.13 years of service and 3.08 tours of duty. A multigroup moderation analysis was performed (Table 3), enabling the identification of differences across the experience clusters. Three paths were identified that were moderated by military experience. The three paths measured how: (a) tours of duty impact TBI, (b) presence of PTSD symptoms influences depression, and (c) presence of TBI symptoms is associated with academic disruptions.
Multigroup Moderation Analysis (N = 77)
The final path model was found to be a good fit, with a root mean square error of approximation of 0.000, comparative fit index of 1.000, and Tucker-Lewis Fit Index of 1.040 (Klem, 1995) with power analysis of 0.9936 (Preacher & Coffman, 2006). Analysis began with measuring academic success using GPA and the number of academic disruptions experienced, with disruptions predicting GPA. GPA was ultimately eliminated from the analysis in the process of trimming the model of irrelevant pathways. In the analysis, individual resilience scores had no impact on GPA or total academic disruptions. It is worth noting that total academic disruptions were predicted through different pathways based on level of experience (Table 4).
Path Analysis of Academic Performance Among Student Veterans (N = 77)
For participants with more military experience, a positive TBI screen increased the number of academic disruptions by approximately three events (b = 2.821, p = 0.001), whereas a 2-point increase in the depression score (b = 0.530, p = 0.002) resulted in approximately one additional disruption for Veterans with less experience. This result indicates that Veterans with more military experience who have a positive TBI screen are predicted to experience more academic disruptions. Veterans with less military experience who have higher depression scores are predicted to experience more academic disruptions.
Among participants with less military experience, each additional tour of duty led to a 5-point increase in PTSD scores (b = 5.255, p = 0.002) and an increase in likelihood of a positive TBI screen (b = 0.161, p < 0.001). However, neither of these relationships was observed among the more experienced cluster. Similarly, whereas every 30 points on the resilience scale reduces the depression score by 1 point among more experienced Veterans (b = −0.035, p = 0.001), this relationship is absent in less experienced Veterans. Recognizing that higher PTSD scores resulted in greater depression scores for both experience clusters, the effect for those with more military experience (b = 0.038, p < 0.001) is approximately one half of what is observed among less experienced Veterans (b = 0.065, p < 0.001). Put into context, for every 15 points on the PCL-M (PTSD) scale among Veterans with less experience, there is a 1-point increase in the PHQ-2 (depression) scale. However, this same 15-point increase among more experienced Veterans will result in an increase of only 0.57 points on the PHQ-2. Thus, among more experienced Veterans, PTSD had less of an effect on depression scores.
Notably, for both experience clusters, most demographic factors were unrelated to all other factors in the model. The only demographic factor that was found to be relevant to the model was the aforementioned impact of number of tours on TBI and PTSD.
Resilience among student Veterans did not have a direct impact on academic performance. The relationship between resilience and academic outcomes for student Veterans appears to be complex and multifaceted, and further research is required to identify the factors involved. However, interesting direct and indirect relationships were found between and among the PTSD, TBI, and depression variables and academic outcomes.
Influence of TBI and Depression on Academic Disruptions
The manner in which TBI influences academic performance is moderated by the extent of military experience. Specifically, the negative impact of TBI on academic performance is much greater among students with more military experience. In addition, although not seen to be significantly different based on the Multigroup Moderation Analysis or difference in slopes test (data not shown), it is worth noting that student Veterans with less military experience were found to have more academic disruptions as depression scores increased. This relationship was not observed in Veterans with more experience. Given the relatively small sample size and corresponding large standard error in the current study, it is possible that a larger sample would eradicate some of the error and provide greater clarity to the moderating effect.
Predictors of Academic Disruption
Although academic disruption was found to be an important and predictable outcome for the student Veteran population, it is revealing that there is a difference in what predicts the academic outcome. Among less experienced Veterans, the amount of academic disruption was predicted by level of depression. Among more experienced Veterans, the amount of academic disruption was predicted by a positive TBI screening. This moderating effect of military experience may be explained in part by age difference. Mean age of Veterans with more and less experience is 46.13 years and 31.72 years, respectively. It is possible that individuals with more military experience have developed coping mechanisms to manage depression, thus reducing its impact on daily functioning. Veterans who are younger and less experienced will have had less time to adjust to the reality and impact of depression, rendering it more disruptive. On the other hand, although coping mechanisms have been found to be ineffective in mitigating the impact of TBI (Tomberg, Toomela, Ennok, & Tikk, 2007), research suggests that the consequences of TBI are more severe if the injury occurs later in life (Hukkelhoven et al., 2003). As such, individuals with more military experience and more tours may have experienced TBI at a later age, resulting in more disruptions due to the increased severity of the consequences.
Influence of PTSD
The difference in the PTSD to depression pathway warrants discussion. Although both experience clusters showed an increase in the depression score as a result of an increase in the PTSD score, the increase among the less experienced group was approximately twice that of the more experienced group. One might postulate that the reason for the difference between groups is related to level of social support (Pietrzak et al., 2010) or stress inoculation, one type of which is defined as enhanced resistance to stress through exposure to challenge (Basoglu et al., 1997; Knight, Gatz, Heller, & Bengston, 2000). Existing studies suggest that greater amounts of social support are protective against PTSD and depression (Pietrzak et al., 2010); therefore, the possibility of stress inoculation cannot be overlooked. Prior studies indicate that repeated exposure to traumatic events reduces the subsequent manifestation of psychological comorbidities (Basoglu et al., 1997; Knight et al., 2000), which is especially relevant when one considers the manner in which PTSD influences academic disruptions.
Although the PTSD score does not have a direct impact on disruptions, there is a logical indirect relationship that can be observed for individuals with less military experience. By calculating the indirect effect (0.530 × 0.065), a 1-point increase in the PTSD score indirectly increases the number of disruptions by 0.035. Although this seems like a negligible amount, when considering the mean PTSD score of Veterans with less experience (34.78), the average student Veteran within the less experienced group will have 1.2 additional disruptions due to the indirect impact of their PTSD status.
Influence of Number of Tours
Finally, the fact that the number of tours is predictive of PTSD score and positive TBI screening for Veterans with less experience, but not for Veterans with more experience, requires discussion. It seems illogical that number of tours would be important to one experience group, but not the other. However, it is possible that the impact of number of tours has, in effect, a plateau and is predictive of TBI only on the lower end of the scale. Individuals with PTSD and TBI may inherently have fewer years of duty as a result of their condition. For Veterans with more experience, number of tours may not be predictive as these soldiers may be serving at higher rank, reducing their chances for exposure to environmental conditions that could lead to PTSD and TBI.
Limitations and Strengths
As is often the case with pilot studies, one limitation of the current study is the relatively small sample. Furthermore, it would have been beneficial to collect data regarding combat exposure and/or duties in the combat theater. However, due to concern that a prolonged survey would deter participants from completing the questionnaire, these data were not collected. In addition, due to the sampling method used, the data comprise only Veterans who are currently enrolled at a mid-Southern university. Such methods had the effect of excluding any Veterans who were at one point attending the university but were unable to maintain enrollment. It is possible that inclusion of these individuals would provide more clarity regarding how the predictors influence academic performance, and more importantly would provide insight into how to provide assistance and/or guidance to students who ultimately leave the university, in the interest of supporting them in completing their education.
Despite these shortcomings, the study also has its strengths. The use of path analysis provides the opportunity to understand how multiple covariates interact to impact outcomes. Furthermore, analyzing that model across multiple clusters allows for recognizing the manner in which the covariates impact the various outcomes based on the cluster. The combination of these two methods provides insight that does not currently exist within the literature.
The purpose of this pilot study was to evaluate which factors, within the student Veteran population, influence academic progression and performance. The results of the analysis suggest that viewing the student Veteran population as a uniform body and developing programs with the intent of serving the needs of all Veterans would be inefficient and ineffective. Within the current sample, student Veterans with less military experience who report symptoms of depression appear to be at higher risk for academic disruption than those with more experience. More and less experienced Veterans who report PTSD symptoms are also likely to report symptoms of depression. The increased risk of depression appears to be much higher among younger, less experienced Veterans than among more experienced Veterans. Academic disruption among more experienced Veterans was most associated with symptoms of TBI. When developing programs to decrease academic disruptions for student Veterans, it must be recognized that this singular outcome requires multiple, divergent approaches to address the differing needs of subgroups within the student Veteran population.
Understanding how to provide assistance to student Veterans to encourage and facilitate program completion is becoming increasingly important. As a result, future studies should expand on the framework provided within the current study. The inclusion of students no longer enrolled is imperative. In addition, the development of longitudinal studies will provide greater insight into how PTSD, TBI, and depression impact academic performance within student Veterans as they progress through their educational programs.
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|Variable||All Student Veterans (N = 77a)||Less Military Experience (n = 54)||More Military Experience (n = 24)|
| Male||64 (82.1)||46 (85.2)||18 (75)|
| Female||14 (17.9)||8 (14.8)||6 (25)|
| Married||32 (41)||18 (33.3)||14 (58.3)|
| Single, never married||25 (32.1)||24 (44.4)||1 (4.2)|
| Separated/divorced/widowed||21 (26.9)||12 (22.3)||9 (37.5)|
|Positive TBI screening||14 (17.9)||9 (16.7)||5 (20.8)|
|Age (years)||36.15 (12.45)||31.72 (11.27)||46.13 (8.73)|
|Resilience score (0 to 100)||74.94 (12.39)||73.76 (11.55)||77.58 (13.98)|
|PTSD score (17 to 85)||35.72 (16.98)||34.78 (15.63)||37.83 (19.88)|
|Depression score (0 to 6)||1.04 (1.35)||1.04 (1.40)||1.04 (1.27)|
| GPA (0 to 4)||3.12 (0.80)||3.05 (0.88)||3.28 (0.58)|
| Disruptions in program||0.62 (0.96)||1.11 (1.73)||1.42 (1.98)|
| Years of service||8.45 (7.11)||4.16 (2.48)||18.13 (3.78)|
| Number of tours||1.77 (2.05)||1.19 (1.1)||3.08 (2.95)|
Incidence of Positive Screenings
|Positive Screenings by Condition||Study Sample (%)||General Population (%)|
|Positive screenings (n)|
Multigroup Moderation Analysis (N = 77)
|Constrained Paths||Constrained χ2|
|Number of tours → PTSD score||15.782|
|Number of tours → TBI screen||18.218*|
|PTSD score → Depression score||17.235*|
|Resilience → Depression score||13.000|
|Depression score → Academic disruptions||14.908|
|TBI screen → Academic disruptions||18.191*|
Path Analysis of Academic Performance Among Student Veterans (N = 77)
|Paths||Less Experience||More Experience|
|Number of tours → PTSD score||5.255**||0.375||1.090||0.166|
|Number of tours → TBI screen||0.161***||0.471||0.033||0.234|
|PTSD score → Depression score||0.065***||0.720||0.038***||0.599|
|Resilience → Depression score||−0.019||−0.161||−0.035**||−0.396|
|Depression score → Academic disruptions||0.530**||0.426||−0.052||−0.033|
|TBI screen → Academic disruptions||0.024||0.005||2.821**||0.592|
|Model Fit Indices|