Psychometric Evidence of the Brief Resilience Scale (BRS) and Modeling Distinctiveness of Resilience from Depression and Stress

The purpose of this study was to evaluate the factor structure and measurement invariance across gender and age of the Brief Resilience Scale (BRS) in 2272 Greek adults of the general population. The sample was split into three parts (20%, 40%, 40%). EFA was carried out in the first subsample (20%) evaluating 3 models. CFA was next carried out in the second subsample (40%) evaluating seven models. All models were examined further in a different CFA with a subsample of equal power (40%). The single factor of BRS was deemed unstable across the two CFA subsamples. A two-factor model was the optimal model emerged in the Greek context. Measurement invariance across gender and age was successfully established. Internal consistency reliability (α and ω) and AVE based convergent validity were adequate for the entire BRS. A consistent pattern of relationships emerged from correlation analysis with 12 different measures, suggesting convergent and discriminant validity. The distinctiveness of BRS from depression and stress was evidenced using CFA and EFA with different compound models of BRS and scales of depression, anxiety, and stress. These findings further confirmed that the Greek version of BRS has construct validity.

BRS was adapted for the Malaysian context (Amat et al., 2014) in a sample of 120 international university students, 63% males. The single factor structure of the original version was verified using PCA. The factor that emerged explained 74% of the variance. Internal consistency reliability, as measured by Cronbach's alpha was reported .93.
The validation of BRS in the Brazilian cultural context (De Holanda Coelho et al., 2016) was carried out in two samples of university students. Initially, PCA was performed and one single factor emerged, accounting for 43% of the total variance, after the removal of item 5. Internal consistency reliability was adequate. Next, this factor structure was successfully replicated in a second sample with CFA. Measurement equivalence with the student sample of the original study (Smith et al., 2008) was evaluated and partial, strong measurement invariance was established. They reported significant but weak correlations between BRS and positivity (Caprara et al., 2012), or flourishing (Diener et al., 2010), extraversion, openness, and agreeableness but negative correlations with neuroticism (John, Donahue, & Kentle, 1991).
The Spanish version of BRS (Rodríguez-Rey, Alonso-Tapia, & Hernansaiz-Garrido, 2016) was validated in both adults of special populations and adults of the general population. Confirmatory Factor Analysis was carried out, confirming the single factor structure of BRS. It should be noted that although this structure was scored as a single-factor, it was actually a two-factor first-order structure of a second-order BRS Resilience factor. One factor comprised the positively worded items and the second the negatively worded items to account for the wording effect (Alonso-Tapia & Villasana, 2014;Marsh, 1996;Wu, 2008cited in Rodríguez-Rey et al., 2016. Wording effect was attributed to positively and negatively worded items of BRS to avoid response bias (Cronbach, 1950see Rodríguez-Rey et al., 2016. Item reversing at this extent (50%) forced the items to separate in two factors despite that they measure the same dimension. To evaluate convergent, discriminant and predictive validity the following constructs were used: resilience (Campbell-Sills & Stein, 2007;Connor & Davidson, 2003), trauma (Davidson et al., 1997), stress (Cohen, Kamarck & Mermelstein, 1983), emotionality (Fredrickson, Tugade, Waugh, & Larkin, 2003), hospital anxiety and depression (Zigmond & Snaith, 1983), posttraumatic growth (Tedeschi & Calhoun, 1996), situational resilience (Hernansaiz-Garrido et al., 2014b), situational coping (Hernansaiz-Garrido et al., 2014a), and resilience personality factors. Measurement invariance between the two samples was also examined. Correlation analysis showed positive and statistical significant relationships between the BRS and the CD-RISC (Campbell-Sills & Stein, 2007;Connor & Davidson, 2003), positive emotions (Fredrickson, Tugade, Waugh, & Larkin, 2003), problem-centered coping, sense of mastery, sense of relatedness and emo-Psychology tional reactivity (Rodríguez-Rey et al., 2016). Authors reported negative correlations with stress, negative emotions, and emotion-centered coping.
Lastly, the German BRS version (Chmitorz et al., 2018), was validated in two large samples of the general population using CFA. One-factor, two-factor and a method model were evaluated. The method model was specified to account for the wording effect and consisted of a general resilience factor with all 6 items and a specific method factor with only the negatively worded items and showed optimal fit. Internal consistency reliability was reported α = .85 and ω = .85. Convergent validity was supported by a positive and significant relationship of BRS with well-being, social support, optimism, and the active coping strategies. Negative relation was reported with somatic symptoms, anxiety and insomnia, social dysfunction, depression, and the coping strategies of religion, denial, venting, substance use, and self-blame (Chmitorz et al., 2018).
Summing up BRS factor structure of the versions adapted for different cultures, BRS in a Malaysian sample (Amat et al., 2014) and in a Brazilian sample (de Holanda Coelho et al., 2016) was reported to be unidimensional. The Spanish (Rodríguez-Rey et al., 2016) and German (Chmitorz et al., 2018) version reported having a two-factor structure to account for method effects. There is also a Dutch BRS version (Consten, 2016) validated in a special population of a rehabilitation facility. Thus BRS has been validated in collectivistic and individualistic cultural contexts (Hofstede, 2001;Triandis, 1995), and special populations like cardiac rehabilitation patients and women with fibromyalgia (chronic pains; Smith et al., 2008), HIV-positive diagnosed (Rodríguez-Rey et al., 2016), cancer outpatients (Rodríguez-Rey et al., 2016), parents with children either with intellectual disabilities, development disorders or parents of oncological outpatient children (Rodríguez-Rey et al., 2016), or members of a rehabilitation facility (Consten, 2016).
Sociodemographic differences in BRS scores emerged regarding gender and age. Smith et al. (2008) found that male cardiac patients scored higher than females. However, the samples of students presented no gender differences. Rodriguez-Rey et al. (2016) also reported higher BRS scores for males. No age-related differences were reported by Smith et al. (2008) in contrast to Smith et al., (2010). Rodriguez-Rey et al. (2016) also found lower BRS scores for respondents aged from 20 to 30 years than those older than 31 years. Generally, these findings are supported by inconsistent findings of the relationship of resilience and gender or age (Lundman et al., 2007;Mehta et al., 2008). Lower Income and education were also related to lower resilience levels (Wagnild, 2003;Campbell-Sills et al., 2009) as described by Singh et al. (2016).
The purpose of this study is 1) To validate the BRS factor structure and measurement invariance across gender and age using the 3-faced validation method (Kyriazos, Stalikas, Prassa, & Yotsidi, 2018a, 2018b. 2) To model the distinctiveness of BRS with EFA and CFA from depression and stress evidencing construct validity further. 3) To examine internal consistency reliability and 4) To evaluate Convergent and Discriminant validity.

1) Brief Resilience Scale (BRS)
The BRS (Smith, Dalen, Wiggins, Tooley, Christopher, & Bernard, 2008) is a 6-item measure of resilience, focusing on the ability to recover from stress and adversity. Responses are rated on a 5-point Likert scale from Strongly Disagree (1) to Strongly Agree (5). The higher the mean BRS score the more resilient the respondent is. BRS is a single factor scale. Half of the items are reversed scored to avoid social desi liability response bias (Cronbach, 1950). Smith et al. (2008) reported Cronbach's alpha from .80 -.91 over four samples. BRS was translated in Greek by Stalikas & Kyriazos (2017) with the translation/back-translation method (Brislin, 1970). Items 2, 4, 6 were reversed in all analyses, as proposed by Smith et al. (2008) to avoid desirability response bias (Cronbach, 1950)...
2) The scale of Positive and Negative Experience  This is a 12-item scale of emotionality by Diener et al., (2009Diener et al., ( , 2010 with two factors with 6 one-word items each:.positive experiences (SPANE-P) and negative ones (SPANE-N). Items are scored on a Likert scale from 1 (very rarely or never) to 5 (very often or always). Experiences are evaluated over a 4-week time frame. Possible scores per dimension range from 6 to 30. Their difference (Affect Balance or SPANE-B) can range from −24 to 24. In this study, Cronbach's alpha for SPANE-P, SPANE-N was .90, .85 respectively.
3) The scale of Positive and Negative Experience 8  This is a briefer version of SPANE with 8 items (4 in SPANE-P and 4 in SPANE-N). It is a post-hoc empirical version (Kyriazos, Stalikas, Prassa, Yotsidi, 2018b;Diener et al., 2010) with one general feeling per factor instead of three in the original SPANE (Diener et al., 2010: p. 145). The 4 positive experiences are Pleasant, Happy, Joyful, Contented and the 4 negative Bad, Sad, Afraid, Angry.

4) Depression Anxiety Stress Scale (DASS)
The DASS (Lovibond & Lovibond, 1995) measures depression, anxiety, and stress in three 7-item factors. Items are rated on a four-point Likert scale from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time) T. A. Kyriazos et al. Psychology over the past week. The higher the score the more intense the emotional distress.

8) Warwick-Edinburgh Mental Well-being Scale (WEMWBS)
The WEMWBS (Universities of Warwick and Edinburgh; Tennant et al., 2007) is a unidimensional scale of mental well-being and psychological functioning.
The 14 items of the scale are rated on a 5-point Likert scale from None of the time to All of the time. Internal consistency reliability was adequate (.91 in an adult sample and .89 in an undergraduate student sample; Tennant et al., 2007). Internal consistency reliability in this study was α = .91.

9) Mental Health Continuum-Short Form (MHC-SF)
Mental Health Continuum-Short Form (Keyes et al., 2008;Keyes, 2002) is a 14-item measure of well-being containing 3 factors: emotional (EWB), social (SWB) and psychological (PWB). Responses are rated on a 6-point Likert scale (never, once or twice a month, about once a week, two or three times a week, almost every day, every day) over the last month. Internal consistency reliability for the total MHC-SF was reported to be greater than .80 (Keyes, 2005). Internal

T. A. Kyriazos et al. Psychology
reliability for the total scale in this study was α = .90.

10) Satisfaction with Life Scale (SWLS)
The Satisfaction with Life Scale (Diener, Emmons, Larsen, & Grifin, 1985) is a brief measure of life satisfaction. The 5 Items of the scale are rated on a 7-point Likert scale (1 = Strongly Disagree) to 7 = Strongly Agree). Internal consistency reliability (Cronbach's alpha) was reported from .79 to .89 (Pavot & Diener, 1993). In this study, Cronbach' s alpha was α = .88. 11) Meaning in Life Questionnaire (MLQ) The MLQ (Steger et al., 2006) measures the presence of and search for meaning in life, with 10 items tapping two factors (Presence of meaning and search for meaning). Items are rated on a 7-point Likert scale (from "Absolutely True" to "Absolutely Untrue"). Steger et al. (2006) reported Cronbach's alphas of .86 for Presence factor and .87 for Search. Internal consistency reliability in this study was α = .78

12) Trait Hope Scale (HS)
Trait Hope Scale (Snyder et al., 1991) is a 12-item measure of dispositional hope having two factors: Agency and Pathways. Items are rated on a Likert scale ranging from 1 (Definitely False) to 8 (Definitely True). Snyder et al. (1991) reported Cronbach's alphas for the total scale from .74 to .84. Internal reliability in this study was α = .89.
13) The Gratitude Questionnaire  The GQ-6 (McCullough, Emmons, & Tsang, 2002) is a 6-item scale of gratitude experience. Items are rated on a 7-point Likert scale from 1 = strongly disagree to 7 = strongly agree). GQ-6 has a unidimensional factor structure. Items 3 and 6 are reversed scored. The internal consistency reliability of GQ-6 in the original study was .82 (McCullough et al., 2002) and in this study, it was α = .68.

Procedure
One hundred and fifty undergraduate psychology students assisted the online data collection procedure by forwarding a link to an electronic test battery (in Google Forms© format) to 15 -20 adults from their social environment. Students participating in the study received extra-credit. All the fields of the digital test-battery were set as required. The following process took place for the data collection. Initially, students attended a training course on the administration of digital psychology questionnaires. Then, pilot-testing of the digital test-battery followed to track ambiguities in the questionnaire used or potential flaws in the digital procedure. The completion time was approximately 15 minutes. After successful pilot testing, students received a link to the official study.

Research Design
Research scope was twofold: a) on three subsamples (EFA, CFA 1, and CFA 2) to establish construct validity with EFA and confirm it with two CFA in a sample of equal power; b) over the entire sample (Total sample) to evaluate strict measurement invariance across gender and age. This is a construct validation pro-

T. A. Kyriazos et al. Psychology
cedure we termed "3-faced construct validation method" (see Kyriazos et al., 2018aKyriazos et al., , 2018b. Table 1 presents an overview of the method as implemented in the present study. Regarding the factor analysis methods applied, in the first subsample (EFA subsample), Exploratory Factor Analysis (EFA) and Bifactor EFA were implemented, testing three alternative models. In the second subsample (CFA 1 subsample) Independent Cluster Model Confirmatory Factor Analysis (ICM-CFA),

CFA Bifactor and Exploratory Structural Equation Modeling Analysis (ESEM)
and ESEM Bifactor Analysis were carried-out in seven alternative models. The third subsample (CFA 2 subsample) was used to cross-validate the CFA model established in the CFA 1 subsample. Then, a multigroup CFA (MGCFA) was carried out in the entire sample (N = 2272) using the CFA 2 optimal model as a baseline model, to test for strict measurement invariance across gender and age (see Table 1 for an overview of this method). Reliability analyses using Cronbach's α (Cronbach, 1951) and McDonald's ω (McDonald, 1999;Werts, Lim, & Joreskog, 1974) coefficients were carried out in the entire sample. Convergent validity was examined first with Average Variance Extracted (AVE; Fornell & Larcker, 1981). Convergent/discriminant validity was then examined based on correlation analysis over the entire sample. The correlation of BRS with mental distress, well-being, emotionality, positivity, and quality of life was examined.

Data Screening and Sample Power
There were no missing values in all variables the data set because all the fields of the digital test-battery were set as required (see Procedure section). To examine the construct validity of BRS the total sample (N = 2272) was randomly split into three parts (20%, 40%, and 40%). EFA was carried out in the first subsample (n EFA = 452, 20%). CFA followed both in the second subsample (n CFA1 = 910, 40%) and in the third (CFA 1 and CFA 2 respectively). The third subsample was of equal sample power to the second (n CFA2 = 910, 40%). CFA 2 was carried out to cross-validate the optimal model established in CFA 1. The number of cases per BRS indicator for the total sample, first subsample (EFA) and second and third subsamples (CFA 1 and CFA 2) was 378.67, 75.33 and 151.67 respectively.

Univariate and Multivariate Normality
The data in all four samples (Total, EFA, CFA1, and CFA2) violated the univariate and multivariate normality assumption. Kolomogorov-Smirnov tests  (Massey, 1951) on all 6 BRS items were statistically significant (p < .001), indicating an absence of univariate normality.
Three EFA Models were tested. MODEL 1 is the original single factor model proposed by Smith et al. (2008) and replicated by others (Amat et al., 2014;de Holanda Coelho et al., 2016). MODEL 2 is a two-factor model having items 1, 3, and 5 in Factor 1 and items 2, 4, and 6 in Factor 2 separating non-reversed items (Factor 1) from reversed (Factor 2). This structure extracted was identical to a two-factor first-order CFA BRS structure proposed by Rodríguez-Rey et al. (2016) to account for response bias method effects (Alonso-Tapia & Villasana, 2014;  Marsh, 1996;Wu, 2008; as reported by Rodríguez-Rey et al., 2016). Note however that the model tested here is an EFA structure that emerged, while the model proposed was defined by CFA. MODEL 3 is a higher-order EFA Bifactor model (Jennrich & Bentler, 2011) with items 1, 3, 5 in Factor 1 (non-reversed items) and items 2, 4, 6 in Factor 2 (reversed items) and a General BRS resilience factor.
Concerning model fit, MODEL 1 hardy showed a fit within acceptable limits.
MODEL 2 had a good fit with all goodness of fit measures far better than acceptability limits, factor intercorrelation at 0.146 and factor loadings in Factor 1 from .512 to .729 and in factor 2 from .555 to .730. MODEL 3 failed to be identified. (See Table 3 for all EFA model fit statistics).
Based on previous literature and EFA that was carried out in the previous phase, the following seven models were tested. MODEL 1 was the single factor model originally proposed by Smith et al. (2008) and validated by Amat et al.  Note. Factor 1 = Items 1, 3, 5; Factor 2 = items 2, 4, 6; FI = Factor Intercorrelations; Estimator = MLR; EFA Factor rotation = Geomin. (Wang & Wang, 2012). Instead, we tested a higher order CFA Bifactor (Harman, 1976;Holzinger & Swineford, 1937) and ESEM Bifactor model with two factors (MODEL 5 and 6 respectively) since Bifactor models do not have this restriction (see Brown, 2015). MODEL 7 was a CFA Bifactor model with the two-factor structure proposed by Chmitorz et al. (2018). Regarding model fit, MODEL 1 showed an acceptable fit, except for the RMSEA. MODEL 2 showed a remarkably improved fit after the addition of error covariances to MODEL 1 with all measures within limits and with a significant fit, factor loadings from .572 -.739. MODEL 3 achieved an adequate fit with almost all measures within acceptability and RMSEA on the verge of acceptability, factor loadings per factor from .626 -.685 (Factor 1) and .630 -.739 (Factor 2), factor intercorrelation .828 (see in Table 4 the goodness of fit statistics for all models). MODELS 4 -7 either failed to be identified or to converge. Thus, two competing optimal models emerged, a) the single factor with error covariances (MODEL 2) and b) the two factor model with reversed and non-reversed items separated in 2 factors (MODEL 3).

Cross-Validating the Optimal CFA Models in a Different Subsample (CFA 2)
In this phase of the 3-faced construct validation method (Kyriazos et al, 2018a(Kyriazos et al, , 2018b), a second CFA was carried out in the different subsample of equal power to the previous one (40%, n CFA2 = 910). Here, the fit of all the models tested in CFA 1 was evaluated further. MODEL 1 showed a poor fit. MODEL 2 ( Figure   1(a)) had Chi-square/df, TLI and RMSEA beyond acceptable limits, showing a fit divergence in comparison to CFA 1. The fit of MODEL 3 (Figure 1(b)) was satisfactory, with all measures within expected limits and with a good fit, factor loadings from .559 -.706 (Factor 1) and .671 -.733 (Factor 2), Factor intercorrelations at .745 < .80 (See Table 5 for details).

Measurement Invariance across Age and Gender
In this phase of the 3-faced construct validation method (Kyriazos et al., 2018a(Kyriazos et al., , 2018b, we examined BRS measurement invariance across gender and age in the entire sample (N = 2272) using the two-factor model as a baseline model. Invariance was examined with the ΔCFI ≤ −.01, and ΔRMSEA ≤ .015 criteria, N =   showed acceptable fit (see Table 6), indicating that configural invariance was supported. Then, factor loadings were constrained to equality. As shown in Ta and MODEL 4 to 3 indicated that ΔCFI (but not ΔRMSEA) was beyond acceptability to support strong invariance and strict invariance. This means that age comparisons in indicator means and indicator residuals should be made with caution (see measurement invariance results in Table 6).

Convergent and Discriminant Validity with Correlation Analysis
The correlation between BRS and other constructs was evaluated in the total sample (N = 2272) with 12 measures separated in five groups (Table 8)  Note. BRS Factor 1 = items 1, 3, 5 and BRS Factor 2 = items 2, 4, 6. Psychology

Modeling BRS Distinctiveness from DASS-Depression and DASS-Stress
The BRS was developed to measure resilience, in other words, the ability to recover from stress and adversity (Smith et al., 2008). In this line, when during hardship the absence of depression or anxiety was conceptualized as the presence of resilience (Chmitorz et al., 2018). Therefore, we examined to what extend resilience as measured by BRS was distinct from Stress and Depressionthus supporting construct validity further-we carried out an EFA and a CFA.

Exploratory Factor Analysis of the Compound BRS Models
During this phase, 10 alternative EFA models were extracted (MLR extraction with Geomin rotation), either single factor (Table 9, MODELS 1-3) or two-factor (Table 9, MODELS 4-6). The single factor models had one factor either with BRS (Smith et al., 2008) and DASS-21 (Lovibond & Lovibond, 1995) or BRS and DASS-21 Stress or BRS and DASS-21 Depression collapsed in one factor. They all had a poor fit with negative factor loadings, as expected to suggest the distinctiveness of BRS from DASS-21, DASS-21-Stress, and DASS-21 Depression. The two-factor models extracted had one factor with BRS and the second with either DASS, DASS-21 Stress or BRS and DASS-21 Depression (Table 9, Table 9, MODELS 7-10. The single factor models extracted (BRS and DASS-9 Stress, BRS and DASS-9 Depression) showed poor fit with negative factor loadings, suggesting that BRS and DASS-9 measure distinct constructs. The two dual-factor models extracted had BRS in Factor 1 and either DASS-9 Stress or DASS9 Depression in Factor 2 (Table 9, MODELS 9 and 10). As expected factor intercorrelations were negative and strong, for BRS with DASS-9 Stress −.482, and with DASS-9 Depression −.344. The goodness of fit Psychology Note. *Factor 1 = BRS Factor 2 = DASS or DASS-Depression or DASS-stress; FI = Factor intercorrelation; Estimator = MLR, Factor rotation = Geomin.
measures of the two-factor models was tolerable, the two-factor structure was clear with adequate primary loadings (See Table 9).

Confirmatory Factor Analysis
The two factor-models were next evaluated further with CFA (MLR parameter estimation) in a subsample of n = 1772. The first three models had BRS (Smith et al., 2008) in one factor and either DASS-21 (Lovibond & Lovibond, 1995), DASS-21 Stress, or and DASS-21 Depression in a second orthogonal factor (Table 10, MODELS 1-3). Additionally, three alternative two-factor models were tested with BRS in one factor and either DASS-21, DASS-21 Stress, or and DASS-21 Depression in a second correlated factor (Table 10,  . The fit of the models with orthogonal factors was poor with most goodness of fit measures out of acceptable limits or on the verge of acceptability. The same was true for the model with two correlated factors of BRS and the entire DASS-21. The models having two correlated factors of BRS and DASS-21 Stress (Table 10, MODEL 5) or BRS and DASS-21 Depression (Table 10, MODEL 6) had a good fit with all goodness of fit measures in acceptable limits. As expected the factor intercorrelations were all negative and strong, for the BRS with DASS-21 −.561, with DASS-21 Stress −.505 (see Figure 2(a)), and with DASS-21 Depression −.545 (see Figure 2(b)). Factor loadings were strong in all models (See Table 10 for more details).

T. A. Kyriazos et al. Psychology
Similar findings emerged for the dual factor models tested with BRS (Smith et al., 2008) in the first factor and DASS-9 Stress or DASS-9 Depression (Yussof, 2013;Kyriazos et al., 2018a) in a second, orthogonal factor (Table 10, MODELS 7 and 8). Two variations of these models were also tested with BRS in the first factor and DASS-9 Stress or DASS-9 Depression in a second, correlated factor (Table 10, MODELS 9 and 10). The goodness of fit measures of these two-factor models of BRS with DASS-9 Stress (MODEL 9) and BRS with DASS-9 Depression (MODEL 10) in two correlated factors was adequate (See Table 10). As expected factor intercorrelations were negative and strong, for BRS with DASS-9 Stress −469 (see Figure 3(a)), and with DASS-9 Depression −.532 (see Figure   3(b)). All factor loadings were acceptable (Table 10). This is an evidence that resilience, as measured by BRS and Depression and Anxiety, are distinct but correlated constructs, suggesting BRS has construct validity.

Discussion
The purpose of this research was: a) to evaluate construct validity with EFA and confirm it with CFA with a construct validation procedure we termed "3-faced construct validation method" (see Kyriazos et al., 2018aKyriazos et al., , 2018b; b) to examine measurement invariance across gender and age; c) to assess reliability and validity; d) to establish convergent and discriminant validity; e) to evaluate model the distinctiveness of BRS (Smith et al., 2008) from DASS-12 (Lovibond & Lovibond, 1995) and from DASS-9 (Yussof, 2013;Kyriazos et al., 2018a) as an additional evidence of BRS construct validity.
After sample splitting, the 3-faced construct validation method was implemented (Kyriazos et al., 2018a(Kyriazos et al., , 2018b. In the first phase of the method, EFA was carried out in the first subsample (20%) to retrieve a factor structure (Howard et al., 2016). A total of three EFA models were evaluated. In the next phase of the 3-faced construct validation method (Kyriazos et al., 2018a(Kyriazos et al., , 2018b, a CFA was carried out in a second subsample (40%) to validate the BRS structures extracted in the previous EFA. Based on the existing BRS literature and EFA, seven models were estimated. The BRS unifactorial model with error covariances (in items 3 -4, items 4 -5, and 4 -6) showed a significant fit. The two-factor model with unreversed and reversed items also showed acceptable fit. Four models either failed to be identified or to converge, namely ESEM, CFA Bifactor, ESEM Bifactor, and the method model proposed by Chmitorz et al. (2018).
Thus, two competing optimal models emerged, a) the single factor (Smith et al., 2008) with error covariances and b) the two factor model with unreversed and reversed items separated in two factors. This two-factor model was also proposed by Rodriguez-Ray et al. (2016) as a first order factor structure of a second-order, "traditional" CFA model. Rodriguez-Ray et al. (2016) attribute the 2-factor structure to a response bias effect method (Alonso-Tapia & Villasana, 2014;Marsh, 1996;Wu, 2008; as quoted by Rodríguez-Rey et al., 2016).
In the next phase of the 3-faced construct validation method (Kyriazos et  The fit of this model in CFA 1 was probably a local optimum. Anyhow, this fit divergence is empirically evidencing the usefulness of the 3-faced construct validation method (Kyriazos et al., 2018a(Kyriazos et al., , 2018b. The two-factor model showed a consistently adequate fit across all CFA, thus it was considered more reliable. Generally, a discrepancy in the proposed factor structures of BRS emerges from existing empirical literature, suggesting both single factor (Amat, et al., 2014;De Holanda Coelho et al., 2016) and two-factor structures for BRS (Rodríguez-Rey et al., 2016;Chmitorz et al., 2018). We did not evaluate a traditional higher-order CFA model because for a two first-order factorial structure, like BRS, evaluating if the secondorder factor improves the model fit when compared to the first-order solution is not possible due to under-identification (Wang & Wang, 2012;Brown, 2015).
In the optimal model, the correlation between exogenous constructs did not exceed .85. Thus, we rejected the possibility that the two exogenous constructs are redundant or have a serious multicollinearity problem (Claes & Larker, 1981). As far as construct validity is concerned, all fit measures for both versions reached the suggested levels of significance (Hair, Black, Babin, & Anderson, 2010) indicating that items are measuring adequately the latent constructs.
The BRS was developed to measure resilience, in other words, the ability to recover from stress and adversity (Smith et al., 2008). To examine the above hypothesis supporting BRS construct validity further, an EFA and a CFA were carried out, to examine how resilience, as measured by BRS, was related to Stress and Depression as measured both by DASS-21 (Lovibond & Lovibond, 1995) and by DASS-9 (Yusoff, 2013;Kyriazos et al., 2018a). Prior to this analysis, the total sample was split into two new subsamples to perform EFA and CFA in a different subsample.
EFA models extracted either had a single factor or two factors. The single factor models had one factor with all items either of BRS and DASS-21 (Lovibond & Lovibond, 1995), BRS and DASS-21 Stress or BRS and DASS-21 Depression, T. A. Kyriazos et al. Psychology BRS and DASS-9 (Yussof, 2013;Kyriazos et al., 2018a), BRS and DASS-9 Stress or BRS and DASS-9 Depression. They all had a poor fit with negative factor loadings, supporting the distinctiveness of BRS from DASS-21 and DASS-9. The two-factor EFA models extracted had one factor with BRS and the second with either DASS-21, DASS-21 Stress, DASS-21 Depression, DASS-9, DASS-9 Stress, DASS-9 Depression. The two-factor structures emerged was optimal. Crucially, resilience measured with BRS (the ability to bounce back from stress, Smith et al., 2008) showed a negative relationship with Stress and Depression, and these findings propose that BRS has construct validity.
Next, the two-factor models were evaluated further with a CFA in two different conditions: with the two factors being either orthogonal or correlated. The two-factor correlated models tested had either BRS and DASS-21 Stress in two factors or BRS and DASS-21 Depression, BRS and DASS-9 Stress, BRS and DASS-9 Depression. All these compound models of resilience and mental distress in two correlated factors showed a good fit with negative factor intercorrelations. On the other hand, the orthogonal models showed a hardly tolerable fit.
This verified the EFA findings, suggesting that BRS resilience, had a negative relationship with Stress and Depression, evidencing again BRS construct validity.
Moreover, the similarity of the findings for BRS (Smith et al., 2008) and DASS-21 (Lovibond & Lovibond, 1995) with those of BRS and DASS-9 (Yussof, 2013; Kyriazos et al., 2018a) is an additional evidence of the construct validity of DASS-9 in measuring mental distress in a similar manner to DASS-21.

Conclusion
To conclude, BRS, as measured in Greek adults, has a two-factor structure. BRS is gender and age-invariant as long as indicator means are compared cautiously, in line with previous literature findings. BRS construct validity was also demonstrated modeling its distinctiveness with EFA and CFA from depression and stress. In this line, with existing resilience literature in general and BRS empirical findings in particular, when during hardship the absence of depression or anxiety was conceptualized as the presence of resilience (Chmitorz et al., 2018). Therefore, resilience as measured by BRS was distinct from Stress and Depression and had a negative factor correlation in all compound BRS-DASS models evaluated. Thus BRS construct validity was confirmed further. BRS has also adequate reliability as indicated by alpha, Omega total and convergent validity as suggested by Average Variance Extracted. Additional evidence of convergent/discriminant validity by using 12 different scales verified that BRS, Greek Version is a valid scale.
Limitations of the present research are that students were involved in the data collection and the effects of this method (if any) must be taken into account when attempting to generalize findings. Secondly, the sample of men and women in the invariance across gender was not absolutely balanced. Despite the above limitations, BRS is a reliable resilience measure for adults of the general population in the Greek cultural context.