Investigation of Psychometric Properties of the Turkish Version of the Transcultural Community Resilience Scale

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 The processes affecting the world and 21st century societies involve many shocking ecological, political, geopolitical, economic, and social crises and stresses. With the fast and frequent occurrence of, and intense disturbance of communities by these crises, it can be said that this century is the age of increasing uncertainties and chronic problems that we cannot avoid (Altay-Kaya, 2021). Earthquakes with indensity of 7.7 and 7.6 affecting 10 provinces of Turkey on February 6, 2023 caused thousands of deaths and injuries, unemployment, and psychological, social and economic crises. The psychological resilience of the society can support coping in these stiations of crisis that deeply affect the society. Social resilience is a feature that enables communities to survive despite these problems. Over the last 25 years, the social, political, and academic interest in the concept of social resilience has led to its study under an umbrella of disciplines from psychology to ecology (Plodinec, 2013). 

A community is a shared entity with a common geographical boundaries and destiny (Norris et al., 2008). It is a group of people who live (or do not live) in the same district, village, or neighborhood; share a similar culture, habits, and resources; and are exposed to the same threats and risks, such as diseases, political and economic problems, and natural disasters (IFRC, 2018). It is assumed that resilient communities are stronger in coping with such situations that threaten their existence. However, in the literature, definitions of community resilience vary according to context and purpose (Lind

berg & Swearingen, 2020). It can be considered as a quality (e.g., ability or capacity), a process, and/or an outcome associated with successfully adapting to and recovering from difficulties (Pfefferbaum et al., 2013). 

The concept of “resilience” (described in Turkish as robustness, durability, endurance, flexibility, or tirelessness) is the ability of any system to respond to unexpected crises or prolonged stresses without losing its integrity, and to adapt to changing conditions due to these factors by improving and renewing itself (Altay-Kaya, 2021). On the other hand, community resilience is the capacity of a community to cope with a crisis and grow while maintaining the quality of life, the core values, and the identity of its members (Berger, 2017). The International Federation of Red Cross and Red Crescent Societies (2018) defines resilience as “the ability of individuals, communities, organizations, or countries exposed to disasters and crises and underlying vulnerabilities to anticipate, reduce the impact of, cope with, and recover from the effects of adversity without compromising their long-term prospects.” 

In addition to definitions that consider community resilience as a capability that protects the community in difficult situations, some consider it as part of the process of the existence of communities. In this context, community resilience is considered a process in which intermediary structures such as school, family, and peer groups soften the impact of oppressive situations and systems (Sonn & Fisher, 1998). This process adds a set of adaptive capacities to a positive trajectory of functioning after a disturbance (Norris et al., 2008). 

Looking at the definitions, two features of community resilience stand out: first is the ability of communities to cope with external stresses and disturbances as a result of social, political, and environmental changes; and second is the existence and engagement of community resources for sustainable development in an unpredictable environment (Lindberg & Swearingen, 2020; Plodinec, 2013). These definitions suggest that community resilience is a collection of capabilities and processes. There is debate on whether the resilience of the individuals who make up the community reflects community resilience (Norris et al., 2008). Community resilience is different from individual resilience, which is derived from people who experience adverse events. Certainly, communities are aggregates of separately existing individuals. Therefore, the resilience of a community, however it is defined, must be in terms of individual resilience (Eachus, 2014). Additionally, community resilience requires “collective action” (Pfefferbaum et al., 2013), and much like individual resilience, involves attitudes, thoughts, beliefs, behaviors, and resources. Resilience is a dynamic process that needs to be maintained over time to facilitate healthy adaptation (Pfefferbaum et al., 2013). Resilience-enhancing resources can be acquired, and skills can be taught, developed, and practiced at individual and community levels. Notably, there has been an increase in programs by government, industrial, and civil society organizations to support community resilience against crises (Patel et al., 2017). 

How we define community resilience affects how we try to measure and improve it (Patel et al., 2017). Thus, it is necessary to operationalize the concept to be used in measurement. Many authors agree that the difficulties of measuring community resilience are due to the limitations of the measurement scales (Kulig et al., 2013; Plodinec, 2013). One limitation is that a scale is developed from only one community, making its generalizability to other communities questionable. Another is that perceived community resilience relates to a particular moment in time, rather than an independent construct that is stagnant over time. This makes the objectivity of the scales controversial (Kulig et al., 2013). Despite problems in generalizability, measurement scales are important for clarifying this concept and providing more objective indicators. Moreover, most individual and community mental health studies agree on the positive role of the social environment and community resources (Arslan, 2018; Yalçın, 2015). Although scales measuring community resilience were developed in non-English languages, none were done in Turkish. Moreover, research in Turkish communities is lacking, with only one qualitative study on the perceived community resilience of victims of the Van earthquake (İkizer et al., 2016). Also, the psycho-social and economic damage of the earthquake that took place in Turkey on February 6 still continues to affect the whole society. Therefore, the purpose of the present study was to examine the psychometric properties of the TCRS adapted to Turkish. 

Method 

Participants and Procedure 

The study participants consisted of 405 Turkish adults aged between 18 and 58 (x̄ = 27.44, sd = 10.12). 311 (76.79%) of the participants were female and 94 (23.21%) were male. Of the participants, 43 (10.62%) were primary school graduates, 216 (53.33%) were high school graduates and 146 (36.05%) were university graduates. In terms of marital

status, 269 (66.42%) of the participants stated that they were single, 124 (30.62%) were married and 12 (2.96%) were divorced. In terms of socio-economic level, 43 (10.62%) of the participants declared that they had low income, 339 (83.70%) had average income and 23 (5.68%) had high income (Table 1). 

Table 1. 

Demographic Characteristics of the Participants 

Turkish Brief Resilience Scale (BRS) 

The psychological resilience of the participants were measured using the Turkish version of the BRS (Doğan, 2015) originally developed by Smith et al. (2008). The scale, which consists of a single dimension and six items, is scored according to a five-point Likert scale (1 = Strongly disagree, 5 = Strongly agree). The total score that can be obtained from the scale ranges from 6 to 30. This scale has a high reliability (α = 0.83) and good fit indices (RMSEA = 0.05, SRMR = 0.03, NFI = 0.99, CFI = 0.99, I = 0.99, RFI = 0.97, GFI = 0.99, AGFI = 0.96). In this study also, the reliability coefficients of the scale are good (α = 0.85, ω = 0.84). 

Turkish Patient Health Questionnaire-4 (PHQ-4) 

The PHQ-4 adapted into Turkish by Demirci and Ekşi (2018), originally developed by Kroenka et al. (2009), was used for measuring the psychological distress of the participants. The scale developed for measuring depression and anxiety symptoms briefly consists of four items using a four-point Likert scale (0 = Not at all, 3 = Nearly every day). High scores indicate a high level of psychological distress. The scale has a good fit for the one-factor model (SRMR = 0.008, RMSEA = 0.000, CFI = 1.00, TLI = 1.00) and high internal consistency (α = 0.83) (Demirci & Ekşi, 2018). In the present study also, the reliability coefficients for the total score of the scale are high (α = 0.84 and ω = 0.85). 

Data Analysis 

Descriptive statistics and internal consistency coefficients of the variables were calculated first. For the normality assumption, skewness, and kurtosis values of the variables were calculated. It is suggested that acceptable skewness and kurtosis values for a normal distribution should range between −1.5 and 1.5 (Tabachnick & Fidell, 2013). 

Confirmatory factor analysis (CFA) was conducted for testing the validity of the three-factor structure of the Turkish TCRS. To determine the degree of the goodness-of-fit of the model tested, the Chi-Square/Degree of Freedom (X²/sd) ratio, Comparative Fit Index (CFI), Turker-Lewis Index (TLI), Incremental Fit Index (IFI), Standardized Root Mean Squared Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) indices were examined in accordance with Kline’s (2015) suggestions. RMSEA and SRMR values below 0.08, the X²/sd ratio below 3, and the CFI, TLI, and IFI values above 0.90 indicate an acceptable fit (Hu & Bentler, 1999; Kline, 2015; Schermelleh-Engel & Moosbrugger, 2003). 

Corrected item-total correlations are calculated for the item analysis of the scale. For the reliability of the scale, Cronbach’s α and McDonald’s ω internal consistency coefficients were calculated. Internal consistency was deemed sufficient when the internal consistency coefficients are 0.70 and above (Tabachnick & Fidell, 2013). For the criterion validity, Pearson product-moment correlation analysis was used to examine the relationship between the TCRS and the BRS and PHQ–4. The data were analyzed by using IBM SPSS (IBM Corp., 2016) and Jamovi (The Jamovi Project, 2020) software. 

Findings 

The psychometric properties of the TCRS are presented below. 

Factor Structure 

The factor structure and factor loadings of the scale are presented in Table No 2. The fit indices of the 28-item and three-factor measurement model in the original form were at the borderline, but the TLI value was low (χ2 = 1024.27, df = 347, χ2/df = 2.96, CFI = 0.90, TLI = 0.89, IFI = 0.90, SRMR = 0.053, RMSEA = 0.069 [90% CI = 0.065 – 0.074]). The modification indices of the first model were examined and some corrections were made in the measurement model according to the proposed modification indices. Modifications were made between the error variances of item 1 and item 2 in the community strengths and support subscale, and between the error variances of item 21 and item 22 in the community values subscale. When the analyses were repeated, the corrected model provided better fit values (χ2 = 896.42, df = 345, χ2/df = 2.59, CFI = 0.92, TLI = 0.91, IFI = 0.92, SRMR = 0.052, RMSEA = 0.063 [90% CI = 0.058 − 0.068]). The item factor loadings of the TCRS varied between 0.61 and 0.75 for the community strengths and support subscale, between 0.62 and 0.76 for the community trust and faith subscale, and between 0.59 and 0.83 for the community values subscale. The item factor loadings of the scale varied between 0.59 and 0.83 for female participants and between 0.47 and 0.90 for male participants.

Table 2. 

Confirmatory Factor Analysis Results 

Table 3. 

Item-Total Correlations and Descriptive Statistics 

Criterion Validity 

The relationships between the Turkish version of TCRS, BRS, and PHQ-4 were examined (presented in Table No 4). The skewness and kurtosis values of all variables varied between −1.5 and 1.5 and the data exhibit a normal distribution. Furthermore, the reliability coefficients of TCRS, BRS and PHQ-4 were at good levels. The TCRS correlated positively with BRS (r = 0.463, p < 0.001), and negatively with the anxiety dimension (r = −0.486, p < 0.001) and depression dimension (r= −0.490, p < 0.001) of PHQ-4. Moreover, the subscales of TCRS were significantly related to the subscales of BRS and PHQ-4. 

Table 4. 

Descriptives and Correlations 

For criterion validity analyses, the relationships of the TCRS with the BRS and the PHQ-4 were examined. The TCRS was positively correlated with the BRS and negatively correlated with anxiety and depression dimensions of the PHQ-4. In the validity studies of the original form of the scale, TCRS was positively correlated with personal resilience scores and negatively correlated with depression scores (Cénat et al., 2021). Studies in the literature revealed that psychological resilience is associated with positive emotions (Arslan, 2015) and is a predictor of well-being (Korkut-Owen et al., 2017). Similarly, in a study by Kimhi and Shamai (2004) examining the relationship between stress and community resilience, it was found that individuals with high stress exhibited lower social resilience. 

In conclusion, Turkish TCRS is a valid and reliable scale measuring community resilience. This scale aims to measure community resilience, the capacity of communities to provide the necessary resources, support, and interactions to help individual members cope, rebuild, and recover from individual and collective forms of trauma. 

However, some limitations of this study must be acknowledged, such as the sampling method used, the online collection of data, and the use of a cross-sectional design. In future studies, a longitudinal design would allow assessment of the test-retest reliability of Turkish TCRS. The relationship between the Turkish TCRS and variables other than psychological resilience, anxiety, and depression, such as trauma and stress can be investigated. Finally, the mediating and moderating effects of Turkish TCRS can be tested in different samples.