Psychological capital, work satisfaction and health self-perception as predictors of psychological wellbeing in military personnel.
Previous studies in US military personnel found that between 16 and 29% of the individuals developed symptoms of post-traumatic stress, depression or anxiety 3-4 months after returning from operation theatres in Irak or Afghanistan (Hoge et al., 2004). Furthermore, MacGregor, Han, Dougherty, & Galarneau (2012) have addressed the negative impact of repetitive re-deployments on the psychological wellbeing and overall mental health of soldiers, who experience short re-adaptation periods to their homeland in-between missions. Some requirements of military life are thus unavoidably compromising the individual's mental and physical health and wellbeing, potentially hindering his professional performance and development (Hoge et al., 2004). However, individual variability results in a wide range of responses, with some people struggling to handle hardship, while others adapt quickly, keeping a high level of performance. Therefore, it is critical for an organisation like the Army to identify which factors can predict who will endure through hardship and how to ensure performance, with several countries considering the troop's mental health as a priority when designing defense policies in recent times (Silgo, 2013).
Contrary to the traditional focus, centered on the identifying the individual's weaknesses, positive psychology aims at revealing and enhancing the individual's strengths (Seligman & Csikszentmihalyi, 2000). Despite developed within the Clinical Psychology field, and be critized for its approach (Perez-Alvarez, 2012), positive psychology approaches have been successfully applied to other psychological fields, such as Work Psychology and Occupational Health (Avey, Luthans, Smith, & Palmer, 2010; Bakker, Rodriguez-Munoz, & Derks, 2012). These fields have benefited from the application of a range of positive psychology-related concepts, such as psychological capital, work satisfaction, general health perception and psychological wellbeing, enabling a better understanding of the individual's capabilities and performance at work. Among these, the latter represents a key element in the evaluation of an individual's general performance (Wright & Rousseau, 2004).
The Psychological Wellbeing (PsW) matches the individual's self-perception and expectations, according to his own values and aspirations (Ryff, Friedman, Morozink, & Tsenkova, 2012) and has been defined as "optimal psychological functioning and experience" (Ryan & Deci, 2001). This general measure is a good indication of a positive predisposition of the worker towards the employing organization and the workplace (Wright & Rousseau, 2004). Moreover, evidence in the literature indicates that an optimal level of PsW has an overall positive impact in the individual's recovery from physical or mental affection (Vazquez, Hervas, Rahona, & Gomez, 2009). These aspects are particularly important when taking into account the particularities of military life, because military personnel is more critically exposed to traumatic experiences than the civilian population, and a higher degree of mental and physical fitness is required. There are six key aspects of PsW that make it a valuable indicator of the individual's performance, as described by Ryff (1989) and categorized as factors in the corresponding questionnaire: self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life and personal growth.
Several other factors have been previously related to PsW. Among these, the Psychological Capital (PsC), which studies how the worker's positive attitude affects his daily performance, has been extensively analyzed by Positive Organizational Behaviour studies due to its potential long-term influence in organizational aspects, subsequently benefitting both the worker and the employer (Luthans, Avey, Avolio, & Peterson, 2010). This is exceptionally relevant for the Military Psychology field, since its more inclusive outlook makes PsC a better predictor of the critical PsW than other traditional measurements like resilience or hardiness. Indeed, not only resilience, but three more factors (namely self-efficacy, optimism and hope) are contributors to the PsC (Sinclair, Waitsman, Oliver, & Deese, 2013). Additionally, its state-like opposed to treat-like nature opens room for PsC development (Luthans, Youssef, & Avolio, 2007). Thus, PsC enhancement by the application of specific training programs is of paramount interest when applied to Military personnel, in order to improve troop readiness and performance. Similar studies have been performed taking other professionals similarly exposed to stressful situations, as is the case of Police Forces (Arnetz, Nevedal, & Lumley, 2009; Avey, Luthans, & Jensen, 2009).
Another factor typically linked with PsW is Work Satisfaction (WS) (Faragher, Cass, & Cooper, 2005), or the emotional evaluation of the individual's positive work experience. Low levels of WS lead to worker's poor performance and eventually desertion from the organisation (Wright, 2003). This aspect is also relevant for the Army, especially given that the military men are exposed to critical stress and are therefore more likely to experience anxiety, depression, burnout and, ultimately lack of engagement (Chamberlain, Hoben, Squires, & Estabrooks, 2016; Mark & Smith, 2012), which can lead into poor psychological contracts (Gata, Gala, Gomez-Sanabria, & Lupianez, 2011).
Lastly, one more factor has been related to PsW in military personnel, as shown empirically by Hotopf et al. (2003) and Cotton & Hart (2003): the General Health Perception (GHP) investigates the individual's self-perception of both mental and physical health. This has deep implications for the military due to the obvious need for duty-ready staff deployable in different potential scenarios, who have to be fully able mentally and physically. Surprisingly, it has been found that the proportion of military population who suffers from mental illness is lower than that of the general population (18% vs. 26%) (Riddle et al., 2007), likely due to the regularity of mental health screenings performed by the Army. Unfortunately however, it is hard to predict if a given individual will develop mental illness when exposed to operative deployment or after repatriation (Hoge, Auchterlonie, & Milliken, 2006; Rona et al., 2009).
In this study, we aim at improving worker's performance by clarifying the contribution of the factors PsC, WS and GHP to the PsW in a cohort of Spanish military. Although the impact of PsW in the performance of military personnel has been studied before (Bliese, 2006; Tucker, Sinclair, & Thomas, 2005), there is little light shed on the relationship between PsC, WS and GHP with PsW. Specifically, our investigations aim to clarify the role of these three factors, and mainly the PsC, as predictors of PsW in a Spanish-specific military population, in which to the best of our knowledge no prior research exists. We also want to study the differences of PsW, PsC, WS and GHP paying attention to the rank, the type of contract, the years in the military, if they have been deployed and the age. We believe it is important to consider these categories in the present study for a better understanding of the studied variables in the Spanish military population. Cultural traits and language barriers may hinder the generalisation of previous studies (Sue, 1999), thus underlining the value of our research.
492 Spanish Military staff were included in the study, 98,4% were male and 1.6% were female, 94.9% of all enrolled for longer than a year (M = 8.921; SD = 6.253), and with 17,7% of the individuals having an indefinite working contract with the Spanish Army. Age range was 20 to 55 (M = 30.384; SD = 5.717), and the cohort was distributed according to rank. Grade Commissioned Officers and Mid-grade Non-commissioned Officers (Captain/Lieutenant/ Staff Sergeant/ Sergeant) represent 13.6% of the sample, while the remaining individuals belong to the Junior Enlisted category (Corporal/Private First Class/Private). 53.5% of the subjects have been actively deployed in operation theatres at least once. All subjects signed the informed consent form and fulfilled the criteria for non-exclusion (1. Being in active service; 2. Being susceptible of being sent to operation theatre; 3. Age between 18 and 58; 4 Rank from Private to Captain; 5 Successfully passing the latest psycho-physical test and not being on medical leave at the moment of the study).
This correlational descriptive study was performance after psycho-physical assessment. Participants were assembled in groups in a meeting room within military premises non-simultaneously Participants were then informed about the general goal of the study, emphasizing the anonymous and voluntary character of the test. After signing the informed consent, the test was passed onto the participants, who had unlimited time for completing the form (normal completing time under 30 min). Data collection spanned a total of six months.
PsW was evaluated using the Spanish version of the Psychological Wellbeing Scale (PWBS) by Diaz et al., (2006). This PWBS is a self-reported questionnaire containing 29 items addressing the six constructs that conform PsW (self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life and personal growth). Each item is presented as a Likert scale ranging from 1 (totally disagree) to 6 (totally agree). High scorings in PWBS are indicative of a high degree of PsW. The obtained reliability [alpha]-value was .898.
PsC was measured with the Spanish version of Psychological Capital Questionnaire, conformed by 24 items (PCQ-24) (Azanza, Dominguez, Moriano, & Molero, 2014) that give rise to four factors. Each item is presented as a Likert scale ranging from 1 (totally disagree) to 6 (totally agree). High scorings in PCQ-24 are indicative of a high degree of PsC. The obtained reliability [alpha]-value was .884
WS was analysed by a single item, following Nagy's indications (2002). The question "In general, which is your degree of work satisfaction?" was presented to the individuals, admitting responses as a Likert scale ranging from 1 to 6. This item encompasses all aspects related to WS, avoiding specificities (such as working schedule, salary) that may bias responses in military personnel (Wanous, Reichers, & Hudy, 1997).
In order to assess the GHP, the Spanish version of Goldberg's General Health Questionnaire (GHQ-28) with a total of 28 items was used (Lobo, Perez-Echeverria, & Artal, 1986). Answers for this test are codified, presenting four alternatives per item, admitting a single choice. This test gives rise to four scales which analyse aspects such as affection (depression), physical health (somatic symtoms) and psychological distress (anxiety and insomnia and social dysfunction). High scorings in GHQ-28 are indicative of psychological disorder. In order to facilitate data handling, scoring was reversed (lower scores are indicative of high degree of GHP). The obtained reliability [alpha]-value was .795.
Averages and standard deviations were obtained for every variable studied (PsW, PsC, WS, GHP). The analysed population was stratified according to: (1) military rank (Grade Commissioned Officers and Mid-grade Non-commissioned Officers, termed "Officers"; Junior Enlisted troops, termed "Enlisted"), (2) type of contract (indefinite working contract, termed "Permanent", temporay contract, termed "Temporay"), (3) missions (if the staff have been deployed at least once, termed "> 1", if never have been deployed, termed "None"), (4) age (the stablished age groups were: "< 25", "25-30", and "> 30") and finally, (5) years in active service (the established age groups were: "< 6", "6-12", and ">12"). T-test was used to determine statistically significant differences among pairs of variables according to rank, type of contract and have been on mission at least one, and ANOVA was run to explore differences related with the age and years of active service. If the criterion of homogeneity of variances was violated, the non-parametric Mann-Withney U-test or Kruskal-Wallis H-test were applied. Pearson correlation coefficients were calculated for the given pairs of variables, and a correlation array was constructed with all pair-wise combinations. Step-wise, multiple linear regression analysis was then applied, using a sequence of F-tests where the PsW was the dependent variable and PsC, WS, GHP, rank, type of contract, mission, age and years of service were the predictors, generating a 8-factorial model. The software platforms used for data extraction and analysis were R (version 3.5.1) and SPSS (version 25).
The determination of the contribution of each variable to explain the variance of PsW in the abovementioned regression model was based on the "relaimpo" package built within the R software platform (Gromping, 2006). This algorithm examines the relative importance of each contributor to the model by averaging over orderings of regressors. Apart from delivering six different metrics, relaimpo also provides bootstrap confidence intervals (Gromping, 2006). In this study, the Img metric was taken, since the aim is mainly to decompose [R.sup.2].
The descriptive statistics of the variables PsW, PsC, WS, GHP according to military rank, type of contract, missions, age and years of service and their mean comparisons test results are presented in Table 1 and Table 2.
After presenting the values for the different variables and the existence of significant differences between rank, type of contract, missions, age and years of service, a Pearson Correlation analyses was performed for all pairs of variables. The results of all estimated correlations are displayed in Table 3, where there is a clear, positive correlation between PsW, PsC, WS and GHR The highest correlation coefficient was found for PsW and PsC, with r(492) = .721, p = .01. The a reliability values are also shown, suggesting a high degree of confidence in the second order variables.
Since the goal of this study is to reveal the potential predictive power of PsC, WS and GHP over the PsW, a step-wise Pearsons' linear regression analysis was performed. Results revealed that a model including four predictors (PsC, WS, GHP and Rank) was able to explain most of the variance of the dependent variable PsW. Results are shown in table 4. Notably, results indicate that 53.8% of the variance of PsW may be explained by the regression model.
Finally, we investigated the predictive power over PsW of each of the analysed factors of the regression model, individually. For achieving this, we took advantage of the "relaimpo" statistical model, decomposing [R.sup.2] using the Img metric (Gromping, 2006). Interestingly, we report that out of the 53.8% of PsW variance explained by the 8-factorial regression model, 80% is actually described by the PsC, while WS and GHP encodes 8% of this variance each, and the remaining factors (rank, type of contract, missions, years of service and age) are only responsible for 4%. When this analysis is repeated taking in consideration a 14-factorial regression model where the components of PsC and
GHP were included, the PsW variance explained mainly by PsC components, followed by WS and some components of GHP (anxiety-insomnia and somatic symptoms). Figure 1 and Figure 2, resume the data.
These are relevant result, since they imply that PsC and its components are main contributors to the variance found for PsW, and since actuations on the PsC are possible, we may be able to influence the overall PsW in military population, ultimately resulting in employer performance improvements.
Our investigation suggests that the PsC is a key predictor of PsW in Spanish military population. Since specific training programs can be developed in order to enhance the PsC, it is possible to improve PsW through such programs, reverting in benefits both for the employee, who will have more tools to handle stressful or traumatic situations, and for the organisation, who will have better performing staff in critical situations.
Several studies in the work frame of Positive Psychology have analysed the role of PsW in the performance of civilian workers and the associated mutual benefits for employee and employer (better predisposition at work and overall life satisfaction for the first, improved productivity and loyalty for the latter) (Faragher et al., 2005; Siu, Cheung, & Lui, 2015). However, military population is subject to work-related particularities that require specific assessment, since the exposure to operation theatre-related events may prove difficult to handle physically and emotionally. Although a handful of reports linking PsC and PsW-related aspects (i.e. health perception, stress perception) in military population are available in the literature, there is a lack of information in this regard (Krasikova, Lester, & Harms, 2015; Schaubroeck et al., 2011). More precisely, there is no report available taking into consideration the Spanish cultural and language particularities. Thus, we believe our research fills a gap, adding information on the relation between PsC and PsW in military staff while providing a Spanish perspective.
We find that, similarly to what has been observed in civilian-oriented studies, positive employee behaviours in military personnel result in high PsC scores. Thus, this study is in line with the Positive Organisational Behaviour Theory (Luthans et al., 2007). This theory implies that personal psychological skills and strengths can be developed by targeted programs, and subsequently can positively affect individual's overall wellbeing (Luthans et al., 2007). Not only the PsC correlates with PsW, but also other factors such as WS and GHP have been found to positively correlate with PsW in the light of this theory in civilian studies, as in our own.
In here, we aim not only at finding positive correlations between PsC, WS, GHP and PsW, but we go one step further proposing a step-wise linear regression model that is able to explain up to 53.8% of the PsW variance using PsC, WS, GHP and rank as predictors. Moreover, we ranked these factors according to their prediction power, notably finding that 80% of the total explained variance can be uniquely predicted by PsC. This is of paramount importance for an organisation like the Army, since Positive Organisational Behaviour Theory proposes that strategies can be developed for improving PsC, and therefore PsW (Luthans et al., 2007). Moreover, PsC individual scoring could be used for stratifying the military personnel, addressing the defined categories with specific training programs aimed at preparing staff for future military activities in a more efficient manner.
Our investigation revealed statistically significant differences for the different predictor variables when comparing rank, type of contract and mission. This is of relevance for the Army, given that different enhancement strategies and programs should be developed to improve the PsC and, in turn, the PsW and general performance of the employee. These differences may arise from different military training between "Officers" vs. "Enlisted" personnel, with the "Officers" trained on decision-making and problem-solving skills, while the "Enlisted" personnel mainly physically trained. Linked to the aforementioned, another important difference between ranks is access to information, mostly reserved to the "Officers" and restricted to the "Enlisted" segment, which results in a higher degree of uncertainty (Mantzios, Wilson, & Linnell, 2015), which could be increased by the personnel's type of contract. Uncertainty has been identified as a main contributor to anxiety, resulting in learnt helplessness (Seligman, 1975). Thus, it is important to focus efforts into coaching the "Enlisted" and "Non-permanent" staff to provide them with needed emotional handling skills, shielding them from critical situations when they are actively deployed. On the other hand, to have participated in missions means to gain a real experience, giving meaning to the military's work when they have to implement the trained skills and develop new ones due to the novelty of the environment in the mission.
Despite the new findings suggesting the relationship between PsC and PsW in Spanish military, there are certain limitations to this study. Foremost, this study is restricted to correlation analyses among pairs of variables, and thus it is not possible to establish causal relationships between them. More precisely, we report a strong correlation between PsC and PsW, however, it is not possible to conclude that improvements in PsC will uniquely cause improvements in PsW. As a solution for this, we propose the performance of longitudinal studies of these variables over time in follow-up studies. Secondly, data collection was performed in a safe and stable environment in the Homeland, so caution has to be taken when extrapolating the conclusions to active deployment scenarios, given that other factors such as tiredness, perceived life risk may affect individual responses (Innes & Barling, 2006). Therefore, we would like to perform data collection during active deployment in future work. Lastly, we have only stratified the sample according to military rank, type of contract, missions, years of service and age, nevertheless other socio-demographic characteristics of the sample could be used to study potential differential responses. Among these, we believe that income, family, type of residence, place of the family residence, relationship with the employer, and daily task could be reported in following research.
In here we conclude that PsC has a relevant predictive power of PsW according to our correlation-based model. Despite other variables (WS, GHP) being equally assessed, most of the variance of PsW could be explained by PsC. This poses a relevant outlook since PsC, according to Positive Psychology frame-works, is subject to improvement by the application of specific programs aimed at improving individual's physical and psychological wellbeing. This is critical for an organisation like the Army, where exceptional mental and physical fitness is required.
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Eduardo Hernandez-Varas (1), Francisco Javier Labrador Encinas (1), and Mariano Mendez Suarez (2)
(1) Universidad Complutense de Madrid and (2) ESIC Business & Marketing School
Received: January 28, 2019 (*) Accepted: April 9, 2019
Corresponding author: Eduardo Hernandez-Varas
Facultad de Psicologia
Universidad Complutense de Madrid
28003 Madrid (Spain)
Table 1 Mean comparison Rank Type of contract M/SD M/SD M.C. M/SD M/SD Officers Enlisted Permanent Temporary PsW 4.92/.S9 4.81/.S2 t=1.48 4.90/.S3 4.81/.S3 PsC 4.67/.60 4.44/.S7 t=3.02 (**) 4.67/.S6 4.43/.S7 WS 4.38A86 3.91/1.22 U=11110.50 (**) 4.44/.88 3.87/1.22 GHP 3.67/. 13 3.60/. 17 U=10539 (**) 3.64/. 19 3.60/.16 (N=67) (N=42S) (N=87) (N=40S) Missions M/SD M.C. M/SD M/SD Officers None [greater than or equal to]1 PsW 4.92/.S9 t=1.39 4.81/.S1 4.84/.S5 PsC 4.67/.60 t=3.50 (**) 4.41/.S2 4.S3/.62 WS 4.38A86 t=4.133 (**) 3.80/1.17 4.12/1.18 GHP 3.67/. 13 t=1.52 3.60/.17 3.62/.16 (N=67) (N=229) (N=263) M/SD M.C. Officers PsW 4.92/.S9 t=-.22 PsC 4.67/.60 U=266S0 (*) WS 4.38A86 t=-3.05 (**) GHP 3.67/. 13 t=-1.19 (N=67) Notes: M/SD = Mean/Standard deviation; M.C. = Mean comparison test; t = t test; U = Mann-Withney test; (*) p < .05 (bilateral); (**) p< .01 (bilateral) Table 2 Mean comparison Age M/SD M/SD M/SD F M/SD <25 25-30 >30 <6 PsW 4.82/49 4.84A56 4.82/.54 .04 4.83/.50 PsC 4.41/.S3 4.47A56 4.50/.61 .65 4.41/.52 WS 3.72/1.17 3.90/1.25 4.15/1.10 .4.72 (**) 3.81/1.19 GHP 3.59/. 18 3.61/.17 3.62/. 16 1.05 3.59/. 17 (N=92) (N=193) (N=207) (N=171) Years of service M/SD M/SD F S-12 >12 PsW 4.81/.56 4.85/.54 17 PsC 4.46/.59 4.56/.62 2.52 WS 3.92/1.20 4.23/1.18 5.21 (**) GHP 3.62/.16 3.61/.17 1.21 (N=177) (N=144) Notes: M/SD = Mean/Standard deviation; (*) p< .05 (bilateral); (**) p< .01 (bilateral) Table 3 Means, standard deviations, correlations and reliabilities Variables Mean SD 1 2 3 1. PsW 4.83 .53 (.89) 2. PsC 4.47 .58 .72 (**) (.88) 3. WS 3.97 1.18 .22 (**) .39 (**) .1 4. GHP 3.61 .17 .30 (**) .28 (**) .30 (**) 5. Self-acceptance 4.97 .73 .83 (**) .66 (**) .21 (**) 6. Positive relations 5.13 .74 .75 (**) .48 (**) .20 (**) 7. Autonomy 4.82 .70 .72 (**) .48 (**) .11 (*) 8. Enviro. Mastery 4.28 .52 .63 (**) .47 (**) .18 (**) 9. Personal growth 5.00 .73 .78 (**) .53 (**) .12 (**) 10. Purpose in life 4.85 .76 .86 (**) .68 (**) .21 (**) 11. Efficacy 4.75 .80 .61 (**) .83 (**) .28 (**) 12. Hope 4.49 .78 .62 (**) .84 (**) .32 (**) 13. Resilience 4.68 .63 .63 (**) .77 (**) .09 (*) 14. Optimism 3.96 .73 .39 (**) .68 (**) .50 (**) 15. Somatic symptoms 3.61 .30 .24 (**) .23 (**) .25 (**) 16. Anxiety-insomnia 3.73 .34 .27 (**) .23 (**) .27 (**) 17. Social dysfunction 3.14 .26 .09(*) .14 (**) .11 (*) 18. Depression 3.96 .17 .07 .01 .04 19. Age 30.38 5.71 -.00 .04 .10 (*) 20. Rank 1.97 1.39 .02 .12 (**) .17 (**) 21. Type of contract 1.82 .38 -.06 .15 (**) .18 (**) 22. Years of service 8.92 6.25 -.00 .08 .14 (**) 23. Number of missions 1.64 2.16 .03 .13 (**) .14 (**) Variables 4 5 6 7 1. PsW 2. PsC 3. WS 4. GHP (.79) 5. Self-acceptance .22 (**) (.79) 6. Positive relations .33 (**) .51 (**) (.76) 7. Autonomy .23 (**) .44 (**) .50 (**) (.65) 8. Enviro. Mastery .12 (**) .50 (**) .32 (**) .30 (**) 9. Personal growth .17 (**) .61 (**) .51 (**) .46 (**) 10. Purpose in life .24 (**) .81 (**) .51 (**) .45 (**) 11. Efficacy .19 (**) .55 (**) .43 (**) .45 (**) 12. Hope .21 (**) .58 (**) .39 (**) .37 (**) 13. Resilience .15 (**) .58 (**) .37 (**) .46 (**) 14. Optimism .32 (**) .37 (**) .31 (**) .22 (**) 15. Somatic symptoms .76 (**) .16 (**) .27 (**) .21 (**) 16. Anxiety-insomnia .78 (**) .18 (**) .28 (**) .26 (**) 17. Social dysfunction .43 (**) .13 (**) .10 (*) .06 18. Depression .38 (**) .02 .09 (*) .13 (**) 19. Age .00 -.01 .04 .00 20. Rank .16 (**) -.01 .00 .04 21. Type of contract -.06 -.06 .02 .01 22. Years of service .04 -.01 .05 .00 23. Number of missions .04 .01 .04 .07 Variables 8 9 10 11 1. PsW 2. PsC 3. WS 4. GHP 5. Self-acceptance 6. Positive relations 7. Autonomy 8. Enviro. Mastery (.16) 9. Personal growth .42 (**) (.65) 10. Purpose in life .52 (**) .66 (**) (.83) 11. Efficacy .41 (**) .46 (**) .53 (**) (.90) 12. Hope .38 (**) .51 (**) .63 (**) .59 (**) 13. Resilience .49 (**) .45 (**) .56 (**) .54 (**) 14. Optimism .21 (**) .24 (**) .42 (**) .35 (**) 15. Somatic symptoms .11 (**) .12 (**) .18 (**) .15 (**) 16. Anxiety-insomnia .13 (**) .14 (**) .18 (**) .18 (**) 17. Social dysfunction .01 .10 (*) .15 (**) .07 18. Depression .01 .01 .04 .02 19. Age .06 -.02 .01 .11 (**) 20. Rank .01 .00 .05 .18 (**) 21. Type of contract -.03 -.06 .12 (**) .17 (**) 22. Years of service .05 -.03 .01 .17 (**) 23. Number of missions .08 .00 .04 .20 (**) Variables 12 13 14 15 1. PsW 2. PsC 3. WS 4. GHP 5. Self-acceptance 6. Positive relations 7. Autonomy 8. Enviro. Mastery 9. Personal growth 10. Purpose in life 11. Efficacy 12. Hope (.80) 13. Resilience .55 (**) (.65) 14. Optimism .49 (**) .28 (**) (.56) 15. Somatic symptoms .20 (**) .13 (**) .25 (**) (.71) 16. Anxiety-insomnia .14 (**) .14 (**) .25 (**) .49 (**) 17. Social dysfunction .17 (**) .02 .16 (**) .08 18. Depression .03 .02 .03 .15 (**) 19. Age .04 .02 .04 .03 20. Rank .03 .02 .14 (**) .18 (**) 21. Type of contract .11 (*) .02 .15 (**) .09 (*) 22. Years of service .01 .02 .07 .05 23. Number of missions .03 .06 .09 (*) .05 Variables 16 17 18 19 1. PsW 2. PsC 3. WS 4. GHP 5. Self-acceptance 6. Positive relations 7. Autonomy 8. Enviro. Mastery 9. Personal growth 10. Purpose in life 11. Efficacy 12. Hope 13. Resilience 14. Optimism 15. Somatic symptoms 16. Anxiety-insomnia (.80) 17. Social dysfunction .05 (.70) 18. Depression .17 (**) -.07 (.83) 19. Age .06 -.12 (**) .00 1 20. Rank .16 (**) -.04 .05 .51 (**) 21. Type of contract -.08 .03 -2 -.40 (**) 22. Years of service .10 (*) -.07 .00 .87 (**) 23. Number of missions .06 -.03 .00 .65 (**) Variables 20 21 22 23 1. PsW 2. PsC 3. WS 4. GHP 5. Self-acceptance 6. Positive relations 7. Autonomy 8. Enviro. Mastery 9. Personal growth 10. Purpose in life 11. Efficacy 12. Hope 13. Resilience 14. Optimism 15. Somatic symptoms 16. Anxiety-insomnia 17. Social dysfunction 18. Depression 19. Age 20. Rank 1 21. Type of contract .78 (**) 1 22. Years of service .59 (**) -.45 (**) 1 23. Number of missions .39 (**) -.24 (**) .74 (**) 1 Note: N = 492; Values in paraphrases on the diagonal are alpha reliabilities. (*) p < .05; (**) p < .01 Table 4 Stepwise regression model Variable [beta] S.E.([beta]) [beta] t Constant .52 .35 1.46 PsC .67 .03 .72 21.39 (**) GHP .42 .10 .13 3.99 (**) WS -.03 .01 -.08 -2.52 (*) Rank -.02 .01 -.07 -2.33 (*) Variable [R.sup.2] [R.sup.2]adj. Constant .54 .53 PsC GHP WS Rank Note: [beta] = unstandarized coefficient; S.E. ([beta]) = standard error; [beta] = standardized coefficient; t = statistical contrast; [R.sup.2] = coefficient of determination; [R.sup.2]adj. = adjusted coefficient of determination; (*) p < .05 (bilateral); (**) p < .01 (bilateral)
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|Author:||Hernandez-Varas, Eduardo; Encinas, Francisco Javier Labrador; Suarez, Mariano Mendez|
|Date:||Jul 1, 2019|
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