Psychometric Evidence of the 10-Item Connor-Davidson Resilience Scale (CD-RISC10, Greek Version) and the Predictive Power of Resilience on Well-Being and Distress

The purpose of this study was to evaluate the construct validity of CD-RISC10 in a sample of 1089 Greek adults of the general population. The CD-RISC10 factor structure was evaluated first with EFA in a 20% subsample and confirmed with CFA (CFA1) in a different 40% subsample. A cross-validation CFA followed (CFA2) in a third 40% subsample (i.e. of equal power with CFA1). Model fit comparison using −2ΔLL difference test suggested a bidimensional structure but bifactor ancillary measures indicated that multidimensionality was weak to exclude the unidimensional structure. Full weak measurement invariance across gender for this unidimensional model was successfully established in the entire sample. Partial strong measurement invariance was established after freeing intercepts of 2 items and partial strict after freeing the error variance of 1 item. Internal consistency reliability (α) was equal to three different model-based reliability calculations (CR) at adequate levels (.85), corroborating one another, although CD-RISC10 was not tau-equivalent. The average variance extracted was .37 to evaluate model-based convergent validity. Convergent and discriminant validity were evaluated further with correlation analysis with a resilience measure, life satisfaction, affectivity, depression, anxiety, and stress with all associations to the expected direction. The predictive validity of CD-RISC10 was evaluated with a SEM model of resilience regressed on two higher-order latent factors of subjective well-being (SWB) and psychological distress, yielding significant strong positive and negative effects respectively. Male scored significantly higher than females thus, normative data were calculated over the total sample and also separately by gender. How to cite this paper: Kyriazos, T., & Stalikas, A. (2021). Psychometric Evidence of the 10-Item Connor-Davidson Resilience Scale (CD-RISC10, Greek Version) and the Predictive Power of Resilience on Well-Being and Distress. Open Journal of Social Sciences, 9, 280-308. https://doi.org/10.4236/jss.2021.911022 Received: September 30, 2021 Accepted: November 27, 2021 Published: November 30, 2021 Copyright © 2021 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access T. Kyriazos, A. Stalikas DOI: 10.4236/jss.2021.911022 281 Open Journal of Social Sciences

For the CD-RISC10 development, Campbell-Sills and Stein (2007) used three undergraduate samples, including a subsample of 131 individuals who self-reported childhood trauma and psychiatric symptoms to evaluate construct validity. In the first two samples Exploratory Factor Analysis (EFA) was carried out, and in the third Confirmatory Factor Analysis (CFA). The EFAs showed an unstable factor structure. Based on CFA, empirically-driven exclusion of items followed, proposing a unidimensional scale, preferred over a two-dimensional alternative with hardiness and persistence factors. The unidimensional CD-RISC10 had good internal consistency (.85) and construct validity verifying that resilience moderated the impact of childhood maltreatment on current psychiatric symptoms.
Furthermore, in consistency with the resilience literature (see Singh et al., 2016) a meta-analysis reported that gender significant moderates the association of resilience with mental health (Hu et al., 2015). Campbell-Sills, Forde and Stein (2009) also found that females of the general population scored significantly lower than males on the CD-RISC10. This finding was replicated, e.g. with medical students from Canada (Rahimi, Baetz, Bowen, & Balbuena, 2014), elderly (Meng et al., 2019), undergraduates, and depressive patients from China (Cheng et al., 2020), youngsters from Russia (Nartova-Bochaver et al., 2021), or public accountants from the US (Smith et al., 2018).

CD-RISC10 Validation Studies
Regarding the factor structure, validation studies on the general population confirmed the unidimensional structure for the versions from Australia (Burns & Anstey, 2010), Germany (Wollny & Jacobs, 2021), Russia (Nartova-Bochaver et al., 2021), Slovenia (Kavčič et al., 2021), China (Cheng et al., 2020) and Spain (Notario-Pacheco et al., 2011). Equally, a large body of literature on special populations also supported the unidimensional structure, i.e. US distance runners (Gonzalez et al., 2016), US college students with stress or trauma (Madewell & Ponce-Garcia, 2016), Chinese parents of children with cancer (Ye et al., 2017), Chinese elderly (Meng et al., 2019), or Chinese undergraduates and depressive patients (Cheng et al., 2020), and Spanish non-professional caregivers (Blanco et al., 2019). Conversely, a few validation studies on special populations proposed a bidimensional structure, i.e. nursing students from Nigeria (Aloba et al., 2016), or accounting/business students from the US (Smith et al., 2019), the later proposing a second order bidimensional structure. Lastly, a study on elderly from Finland suggested that CD-RISC was unidimensional for ages < 75 years and bidimensional for ages ≥ 75 (Tourunen et al., 2021).

The Present Study
Note that the CD-RISC10 validation studies in the general population are relatively fewer than those in special populations. Therefore, useful additions to the CD-RISC10 validation literature would be validation in the general population considering the following: 1) given the structural instability whether CD-RISC10 is unidimensional or bidimensional, after evaluating all alternative models proposed in the literature (i.e. unidimensional, bidimensional, bidimensional higher-order), and some untested alternatives (bidimensional bifactor) to gain insights on the scale dimensionality versus multidimensionality (c.f. Hammer & Toland, 2016); 2) given the gender differences in scoring, and the limited measurement invariance studies for the general population; whether CD-RISC10 items measure resilience invariantly across gender and 3) given the potential gender differences to provide normative data both for the general Greek population and for each gender separately.
Therefore, extending the CD-RISC10 validation studies for the general population with this validation in the Greek context adopted a cross-sectional design with the following objectives: (a) to evidence the construct validity of the CD-RISC10, using a multistage validation process testing alternative models (Kyriazos, 2018a)

Participants and Procedure
Inclusion criteria were age ≥ 18 years, and absence of mental illness or insufficient cognitive ability. The sample involved 1089 Greek adults (65% females).
The questionnaire was administered online, after obtaining informed consent.
Data were collected with the network sampling method. Psychology students (2018-2019) voluntarily recruited participants of their social environment, receiving extra course credit. Recruitment rules permitted students to recruit at least 10 non-student participants each, without taking the questionnaire themselves.

Sample Power Analysis
A priori power analysis based on the RMSEA (MacCallum, Browne, & Sugawara, 1996) for the unidimensional CD-RISC10 model (Campbell-Sills & Stein, 2007) suggested that a sample of N = 279 was required for achieving a power of approximately 80% to reject a wrong model (df = 35, RMSEA = .05, alpha = .05).
See Results for post hoc power analysis.

Connor-Davidson Resilience Scale, 10-Item Version (CD-RISC10)
CD-RISC10 (Campbell-Sills & Stein, 2007) is a short version of the original CD-RISC (Connor & Davidson, 2003). It is a self-report, measure of resilience with 10 items (e.g. "Coping with stress can strengthen me") rated on a 5-point scale (0 = Not True at All; 4 = True Nearly All of The Time). The possible score ranges from 0 (minimum resilience) to 40 (maximum resilience).

Data Diagnostics and Analytic Strategy
Note that in all instances MLR estimator was used to estimate the CFA and SEM models, treating data as continuous. CD-RISC10 is rated on a five-point scale, which is a "grey zone", heavily debated whether it is continuous (Li, 2016;Raykov, 2012;Rigdon, 1998) or ordinal (Kline, 2016). However, in practice, empirical researchers suggested using MLR in CFA models when the number of response categories was ≥ 5 (Li, 2016). Data was also treated as continuous because treating variables in the CFAs and SEM as ordinal would mean that every other analysis on the same variables should also treat them as ordinal, thus affecting correlation coefficients, mean comparisons, and even reliability coefficients (see Gadermann, Guhn, & Zumbo, 2012). It would also be unfamiliar to the reader, generating incomparable results to existing validation studies. Data were analyzed with R software (R Development Core Team, 2021). The sample was randomly divided into three (20%, 40%, 40%) to carry out EFA (20%), an initial CFA1 (40%) and cross-validating CFA2 (40%) in three different subsamples: i.e. EFA followed by two CFAs of equal sample power, in a multistage validation process (3-faced construct validation method, Kyriazos, 2018a).
The assumption of univariate and multivariate normality was examined in the whole data set and in the three subsamples separately. Multivariate outliers were evaluated using Mahalanobis distance at α = .001 for the critical χ 2 value (Tabachnick & Fidell, 2013).
Subsequently, predictive validity was estimated by specifying a SEM model with CD-RISC10 regressed on two higher-order latent factors of Subjectivewellbeing (SWB; Diener et al., 1999) and Psychological Distress. SWB comprised the latent factors of life satisfaction (SWLS, Diener et al., 1985), and affectivity Finally, the scores of males and females were compared using Mann-Whitney-Wilcoxon test, and assuming a significance at p < .001. The effect size was calculated with Vargha and Delaney (2000) interpretations (A estimate). Then normative data were calculated by converting raw scores to percentiles for the total sample and per gender separately.

Data Diagnostics & Sample Slitting
There were N = 1089 cases in the total sample. There were no missing values because all the fields of the digital survey were set as "required" (Kyriazos, 2018b). Out of the 1089 cases, there were 32 multivariate outliers, χ 2 (10) = 29.59, p < .001 for Mahalanobis. However, outliers were not data entry errors, therefore exclusion was unsupported, final N = 1089. The total sample (N = 1089) was randomly divided into three subsamples (20%, 40%, and 40%) to carry out the EFA, initial CFA (CFA1) and the cross-validating CFA (CFA2), see Kyriazos (2018a).

Univariate and Multivariate Normality
The assumption of univariate normality was examined in the whole data set (N = 1089), and of multivariate normality in the three samples separately (n EFA = 220, n CFA1 = 435, n CFA2 = 434). All normality tests were significant, p < .001, see Table   A2 in the Appendix.

Confirmatory Factor Analysis (CFA1, nCFA1 = 435)
The CFA was performed in a different subsample (40%, n CFA1 = 435). Four alternative CFA models were tested (Table 2)    Model was tested to examine if CD-RISC10 was unidimensional, multidimensional, or somewhere in-between. We could not test a 2-factor, higher-order model (see Smith et al., 2019), because of the under-identification problems for all models with m ≤ 3 (e.g. Wang & Wang, 2020). See the fit of all the models tested, the range of factor loadings, and inter-factor correlations in Table 2.
Subsequently, 2 trial CFAs were carried out with and without multivariate outliers to test if outliers influenced the CFA1 model fit. The comparison of the model without outliers vs. the model with outliers suggested no significant fit difference, ΔCFI = −0.004 and ΔRMSEA = 0.002. See the model comparison in Table A3 in the Appendix.

Measurement Invariance across Gender
We examined measurement invariance of the optimal single-factor model of CD-RISC10 across gender over the entire sample (N = 1089). When the single-factor model was tested separately for each gender (N males = 383, N females = 706), it had an equally good fit for males, χ 2 (35) = 84.72, χ 2 /df = 2.42, RMSEA = .061  After, testing the configural structure (Table 5), CFI and RMSEA suggested full weak invariance (Model 2 vs 1), but not full strong (Model 3 vs 2). Therefore, to achieve partial strong invariance, the intercepts of items 1 and 9 were freely estimated and ΔRMSEA and ΔCFI values (Model 4 vs 2) suggested partial strong invariance. To achieve partial strict invariance, the error variance of item 1 was freely estimated and ΔRMSEA and ΔCFI values (Model 5 vs 4) indicated partial strict invariance. All model comparisons are listed in Table 5.

Convergent, Discriminant Validity and Concurrent Validity with Correlation Analysis
Bivariate correlations (Spearman rho) of the CDRISC10 were calculated to test   Diener et al., 1985). All relationships were significant at p < .001, in the expected direction (Figure 4), ranging from .59 (BRS resilience) to −.37 (depression).

Predictive Validity with a Structural Equation Model (SEM)
Predictive validity was examined by specifying a SEM model to test the predictive power of resilience measured by CD-RISC10 on two second-order factors: 1) a second-order latent factor of Subjective Well-being (SWB; Diener et al., 1999) containing life satisfaction, (SWLS) positive and negative affect (SPANE-8) and 2) a second-order latent factor of Psychological Distress (PD) containing Anxiety, Depression, and Stress (DASS-9). This model showed a very good fit,  Figure 5 presents the path diagram of the SEM structural model and Figure A1 in the Appendix the full SEM model.    were converted to the 10th, 25th, 50th, 75th, and 90th percentiles for the total sample, and for males and females separately (Table 6). At item level, item 8 (not easily discouraged by failure) had the lowest mean (M = 2.5, SD = 1.06) and item 5 (tend to bounce back after illness or hardship) had the highest (M = 3.08, SD = .85), equal to somewhere between scale points sometimes true (2) and often true (3). See descriptive statistics for each item in Table 6.

Discussion
The purpose of this study was to evaluate: 1) the construct validity of the CD-RISC-10, Greek version in the general population testing alternative models and cross-validating them with a multistage process (Kyriazos, 2018a), 2) the measurement invariance across gender; 3) internal consistency reliability and the model-based reliability; 4) the convergent and discriminant validity; 5) the predictive validity of resilience on psychological distress and subjective well-being; 6) normative data for the entire sample, and for each gender separately.

Interpretation and Similarity of the Findings
To establish construct validity, we used a multistage validation (Kyriazos, 2018a), based on sample-splitting. Sample-splitting (Guadagnoli & Velicer, 1988) is a well-known cross-validation method with factor analysis because a hypothesized structure is replicated across subsamples (Byrne, 2012;DeVellis, 2017). Given the instability of the CD-RISC10 dimensionality across samples and sometimes within the same sample (e.g. Tourunen et al., 2021;Smith et al., 2019), cross-validation was essential to safeguard structural replicability. A multistage cross-validating procedure using 3 subsamples was also implemented during CD-RISC10 development (Campbell-Sills & Stein, 2007).
Subsequently, two CFAs of equal sample power verified the EFA structure. In the first CFA four alternative models were specified, including a bifactor model to evaluate if CD-RISC10 was unidimensional, bidimensional, or somewhere in between (Hammer & Toland, 2016). This procedure (although overlooked by the CD-RISC10 validation studies) is pertinent here, because bifactor models may contribute uniquely to dimensionality conflicts (Hammer & Toland, 2016;McDermott, Levant, Hammer, Hall, McKelvey, & Jones, 2017), although they are most popular as an alternative higher-order specification (Brown, 2015). All the CFA1 models had a comparably good fit. The fit difference test comparing the fit of all the models suggested the two-factor model fitted the data better. Nevertheless, the ancillary bifactor fit measures suggested a weak presence of bi-dimensionality to reject a unidimensional interpretation. This unidimensional structure was also proposed by many studies in the general population of Germany Further support for the robustness of the unidimensional model was the good  (Ye et al., 2017;Kavčič et al., 2021). We avoided error co-variances because this would most likely generate a solution difficult to replicate (Byrne, 2012) due to overfitting, possibly located at a local optimum. The a priori and post hoc statistical power of this model (MacCallum et al., 1996) suggested a subsample size of 1.6 times greater than the suggested minimum.
Next, we examined the measurement invariance of CD-RISC10 in the entire sample. The comparison of the nested models suggested that CD-RISC10 had fully configural and metric invariance but partial strong and strict, because items 1 and 9 were functioning differently in male and female respondents. Therefore, it is not possible to safely compare latent means across gender, since it is unknown if mean differences could be attributed to true population differences or to measurement bias (e.g. Fischer & Karl, 2019). Similarly, studies in the Australian general population (Burns & Anstey, 2010) also failed to establish full strong measurement invariance. Note that male scores were significantly different and higher than female (keeping in mind the restriction of partial scalar invariance). Generally, women self-reported lower resilience than men in numerous CD-RISC10 studies (Cheng et al., 2020;Kavčič et al., 2021;Notario-Pacheco et al., 2011;Nartova-Bochaver et al., 2021). These gender differences might be attributed to some resilience qualities measured by CD-RISC10 that seem to be less pronounced in females under stress or adversity than males, e.g. internal control and personal competence (Cheng et al., 2020;Kavčič et al., 2021;Pulido-Martos et al., 2020;Taylor et al., 2000). Moreover, previous research reported higher resilience scores for adult males than females (Cheng et al., 2020;Kavčič et al., 2021;Nartova-Bochaver et al., 2021;Notario-Pacheco et al., 2011). However, for older adults, the results are inconsistent since Tourunen et al. (2021) reported no gender differences in CD-RISC10 scores an elderly from Finland but these findings were not replicated in the Chinese context (Meng et al., 2019). Therefore, normative data were calculated over the total sample and by gender. This could offer a benchmark for health professionals and numerous programs using resilience as an outcome measure.
Internal consistency reliability and all three model-based reliability estimates were equal, corroborating each other, and they stayed far above the .70 acceptability threshold (Hair et al., 2010). However, the optimal CFA1 model was not tau-equivalent, rendering alpha a somewhat undependable reliability evaluation for this 10-item measure (Brown, 2015: p. 338). In contrast, the greatest lower bound estimate managed to stay above the internal consistency reliability (Mair, 2018). Additionally, the greatest lower bound estimate was greater than the internal consistency reliability (Mair, 2018). CR was greater AVE with AVE marginally missing the .50 threshold (Fornell & Larcker, 1981). This might be an indication that CD-RISC10 items were sufficiently reliable, however, variance due to construct and the variance due to error of measurement is hard to distinguish (Santos et al., 2015). The reliability (both internal consistency and model-based) were generally comparable both to the original (Campbell-Sills & Stein, 2007) and to other studies to general populations, e.g. for a German (Wollny & Jacobs, 2021), Russian (Nartova-Bochaver et al., 2021), or Slovenian sample (Kavčič et al., 2021).
To evaluate convergent and discriminant validity further a correlation analysis followed. All the associations were highly significant of low to strong magnitude at the expected direction, i.e. positive with resilience, life satisfaction, and PA and negative with NA and distress. The existing CD-RISC10 literature corroborates these associations (e.g. Campbell-Sills & Stein, 2007;Wollny & Jacobs, 2021;Nartova-Bochaver et al., 2021;Kavčič et al., 2021;Kuiper et al., 2019;Tourunen et al., 2021). In fact, a large body of quality of life research generally views wellbeing as a state of prevalence of positive psychological traits including resilience (among others). In contrast, illbeing is viewed as a state of prevalence of negative psychological traits like negative effect, stress, pessimism, or hopelessness (see Sirgy, 2021).
Furthermore, to examine the predictive effect of resilience on subjective well-being and psychological distress a SEM model specified. The direct effects of resilience on SWB (Diener et al., 1999) and on distress were significant of strong magnitude, supporting the predictive validity of resilience operationalized by CD-RISC10. Therefore, high-resilient individuals were more likely to have increased subjective well-being and decreased psychological distress than lowresilient. The above findings were consistent with existing literature on the effects of resilience on SWB (e.g. Bajaj & Pande, 2016;Samani et al., 2007), affect (Gonzalez et al., 2016;Gucciardi et al., 2011) and distress (Campbell-Sills & Stein, 2007;Aloba et al., 2016).

Generalizability, Limitations, and Implications
The generalizability of the findings is rather safe due to the rigorous cross-validation process, the alternative models tested, concrete method of model comparison, high reliability, convergent and discriminant validity, and adequate sample power. Nonetheless, the interpretation should be cautious due to the non-probability sampling, and the cross-sectional study design, disallowing causal inferences regarding the SEM model (Kline, 2020), although such rigid views on causality are rather over-simplifications with SEM (Kline, 2020;. A limitation was the imbalanced sample in terms of gender. The study was also limited by its reliance on a monocultural sample, a single data collection procedure, and self-report measurement.

Conflicts of Interest
The authors declare no conflicting interests. To establish a structure for CD-RISC10. The number of factors to retain was examined with Parallel Analysis (Horn, 1965), Very Simple Structure (Revelle & Rocklin, 1979), Minimum Average Partial Correlations (Velicer, 1976) and Bayesian information criterion (BIC).

Differences in resilience across males and females
To test if there are differences in resilience across gender a Mann-Whitney-Wilcoxon test was calculated. The effect size was calculated with Vargha and Delaney (2000) interpretation (A estimate), assuming an alpha level of .01.

17
Normative Data and Descriptive Statistics To convert raw scores to percentiles for the total sample and for males-females separately.