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

304 görüntüleme

Sayı / Erken Görünüm
Sayı 1/3

Yıl / Cilt / Sayı
2022 / 1 / 3

Yazar/lar
Osman Hatun1, Gülşen Topal Özgen2

1 Sinop Üniversitesi

2 Milli Eğitim Bakanlığı

Öz

 In recent years, there has been a growing public, political, and academic interest in the concept of community resilience to better understand and enhance community development in the face of natural disasters, epidemics, economic crises, and other challenges. The importance of community resilience has become even more critical, especially after the earthquake disaster that occurred in Turkey on February 6, 2023, affecting 10 provinces and resulting in the death and injury of thousands of people. This study aims to investigate the psychometric properties of the Turkish Transcultural Community Resilience Scale (TCRS) in Turkish adult samples. The study participants consisted of 405 (76% female) adults aged between 18 and 58 (X2 = 27.44, ss = 10.12). The data were collected through the Turkish versions of the TCRS, the Brief Psychological Resilience Scale, and the Patient Health Questionnaire-4. The psychometric properties of the Turkish TCRS were analyzed by using confirmatory factor, reliability, and correlation analysis. Based on confirmatory factor analysis, the 28-item, three-factor structure of the scale had acceptable goodness-of-fit values in the Turkish participant group. Cronbach’s alpha and McDonald’s omega reliability coefficients for the total score of the scale were high. The reliability coefficients were good in terms of community strengths and support, community trust and faith, and community values subscales. Correlation analysis revealed that TCRS had a positive relationship with psychological resilience scores, and a negative relationship with anxiety and depression scores. In conclusion, the TCRS has a similar factor structure to the original form, acceptable fit indices, and high reliability coefficients. 

Tam Metin

 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 

Variables 

Group 

f 

% 

Gender 

Female 

311 

76.79 

Male 

94 

23.21 

Educational Status 

Primary School 

43 

10.62 

High-School 

216 

53.33 

University 

146 

36.05 

Marital Status 

Single 

269 

66.42 

Married 

124 

30.62 

Divorced 

12 

2.96 

Socio-Economic Status 

Low 

43 

10.62 

Average 

339 

83.70 

High 

23 

5.68 

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 

Factor 

Items 

λ 

θ 

z-value 

λfemale 

λmale 

CSS 

m1 

.665 

.049 

14.760* 

.652 

.735 

m2 

.610 

.050 

13.209* 

.586 

.726 

m3 

.724 

.050 

16.542* 

.729 

.699 

m4 

.750 

.046 

17.382* 

.763 

.682 

m5 

.713 

.050 

16.199* 

.710 

.726 

m6 

.619 

.050 

13.480* 

.634 

.567 

m7 

.662 

.048 

14.675* 

.660 

.680 

m8 

.643 

.046 

14.141* 

.616 

.754 

m9 

.635 

.051 

13.903* 

.644 

.576 

m10 

.719 

.042 

16.377* 

.725 

.681 

m11 

.705 

.045 

15.954* 

.717 

.663 

m12 

.749 

.046 

17.335* 

.741 

.790 

m13 

.663 

.049 

14.705* 

.664 

.681 

m14 

.707 

.048 

16.018* 

.670 

.901 

CTF 

m15 

.617 

.057 

12.851* 

.617 

.627 

m16 

.728 

.049 

15.914* 

.726 

.762 

m17 

.719 

.048 

15.662* 

.717 

.747 

m18 

.755 

.048 

16.745* 

.736 

.870 

m19 

.647 

.051 

13.632* 

.675 

.474 

CV 

m20 

.585 

.046 

12.555* 

.593 

.539 

m21 

.636 

.047 

13.925* 

.629 

.673 

m22 

.656 

.047 

14.504* 

.648 

.694 

m23 

.710 

.044 

16.094* 

.695 

.798 

m24 

.769 

.044 

18.022* 

.771 

.771 

m25 

.810 

.045 

19.468* 

.810 

.823 

m26 

.829 

.043 

20.146* 

.830 

.820 

m27 

.768 

.045 

17.970* 

.761 

.797 

m28 

.820 

.044 

19.831* 

.822 

.827 

Table 3. 

Item-Total Correlations and Descriptive Statistics 

Factor 

α 

ω 

Items 

Mean 

SD 

Skewness 

Kurtosis 

rit 

CSS 

.925 

.925 

m1 

3.440 

1.078 

−0.408 

−0.298 

.666 

m2 

3.412 

1.083 

−0.325 

−0.532 

.601 

m3 

3.857 

1.152 

−0.734 

−0.411 

.696 

m4 

3.756 

1.066 

−0.647 

−0.129 

.733 

m5 

3.556 

1.148 

−0.473 

−0.508 

.691 

m6 

3.830 

1.085 

−0.675 

−0.256 

.596 

m7 

3.756 

1.056 

−0.461 

−0.597 

.641 

m8 

3.741 

1.022 

−0.457 

−0.446 

.608 

m9 

3.844 

1.123 

−0.756 

−0.218 

.591 

m10 

3.763 

0.956 

−0.535 

−0.159 

.684 

m11 

3.647 

1.020 

−0.515 

−0.222 

.665 

m12 

3.751 

1.074 

−0.575 

−0.426 

.715 

m13 

3.778 

1.097 

−0.614 

−0.356 

.628 

m14 

4.020 

1.081 

−1.021 

0.374 

.668 

CTF 

.819 

.818 

m15 

3.358 

1.185 

−0.264 

−0.746 

.547 

m16 

3.973 

1.075 

−0.811 

−0.242 

.618 

m17 

3.467 

1.052 

−0.265 

−0.593 

.648 

m18 

3.299 

1.070 

−0.241 

−0.462 

.676 

m19 

3.620 

1.085 

−0.439 

−0.479 

.571 

CV 

.914 

.914 

m20 

3.454 

0.983 

−0.304 

−0.299 

.565 

m21 

3.365 

1.041 

−0.313 

−0.368 

.646 

m22 

3.415 

1.049 

−0.336 

−0.376 

.663 

m23 

3.760 

1.007 

−0.631 

0.041 

.695 

m24 

3.973 

1.040 

−0.849 

0.042 

.736 

m25 

3.884 

1.071 

−0.751 

−0.077 

.746 

m26 

3.933 

1.050 

−0.718 

−0.171 

.768 

m27 

3.763 

1.055 

−0.584 

−0.222 

.709 

m28 

3.877 

1.055 

−0.692 

−0.174 

.763 

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 

Variables 

Mean (SD) 

Skew. 

Kurt. 

α 

1 

2 

3 

4 

5 

6 

(1) TCRS 

103.29 (19.99) 

−1.01 

0.71 

.954 

1 

(2) CSS 

52.15 (10.69) 

−0.93 

0.53 

.925 

.946* 

1 

(3) CTF 

17.72 (4.16) 

−0.47 

−0.43 

.819 

.776* 

.608* 

1 

(4) CV 

33.43 (7.19 

−0.81 

0.31 

.914 

.923* 

.790* 

.672* 

1 

(5) BRS 

18.45 (5.19) 

−0.05 

−0.29 

.843 

.463* 

.453* 

.369* 

.401* 

1 

(6) Anxiety 

2.81 (1.64) 

0.43 

−0.72 

.790 

-.486* 

-.487* 

-.398* 

-.396* 

-.459* 

1 

(7) Depression 

2.85 (1.66) 

0.42 

−0.59 

.770 

−.490* 

−.504* 

−.418* 

−.370* 

−.503* 

.655* 

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. 

Kaynakça

Altay Kaya, D. (2021). Zorunlu göç ve dayanıklılık planlaması: Türkiye’nin Suriye zorunlu göçü deneyimi. METU Journal of the Faculty of Architecture, 38(2), 115–144. https://doi.org/10.4305/METU.JFA.2021.2.2 

Arslan, G. (2015). Çocuk ve Genç Psikolojik Sağlamlık Ölçeği’nin (ÇGPSÖ) psikometrik özellikleri: Geçerlilik ve güvenirlik çalışması. Ege Eğitim Dergisi, 16(1), 1–12. https://doi.org/10.12984/eed.23397 

Arslan, G. (2018). Social exclusion, social support and psychological wellbeing at school: A study of mediation and moderation effect. Child indicators research, 11(3), 897–918. https://doi.org/10.1007/s12187-017-9451-1 

Berger, R. (2017). An ecological-systemic approach to resilience: A view from the trenches. Traumatology, 23(1), 35–42. https://doi.org/10.1037/trm0000074 

Burton, C. G. (2015). A validation of metrics for community resilience to natural hazards and disasters using the recovery from Hurricane Katrina as a case study. Annals of the Association of American Geographers, 105, 67–86. 

Cénat, J. M., Dalexis, R. D., Derivois, D., Hébert, M., Hajizadeh, S., Kokou-Kpolou, C. K. … Rousseau, C. (2021). The transcultural community resilience scale: Psychometric properties and multinational validity in the context of the COVID-19 pandemic. Frontiers in Psychology, 12, 1–10. https://doi.org/10.3389/fpsyg.2021.713477 

Community & Regional Resilience Institute. (2013). Definitions of community resilience: An analysis (A CARRI report) http://www.resilien-tus.org/wp-content/uploads/2013/08/definitions-of-community-resilience.pdf 

Demirci, İ., & Ekşi, H. (2018). Don’t bother your pretty little head otherwise you can’t enjoy life. ERPA International Congresses on Education (28 June -1 July 2018), Istanbul / Turkey. 

Doğan, T. (2015). Adaptation of the Brief Resilience Scale into Turkish: A validity and reliability study. The Journal of Happiness & Well-Being, 3(1), 93–102. 

Eachus, P. (2014). Community resilience: Is it greater than the sum of the parts of individual resilience? Procedia economics and Finance, 18, 345–351. https://doi.org/10.1016/S2212-5671(14)00949-6 

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. 

IBM Corp. Released. (2016). IBM SPSS Statistics for Windows (Version 24.0). Author. 

İkizer, G., Karancı, A. N., & Dogulu, C. (2016). Afetzedeler toplumsal dayanıklılığı nasıl algılıyor? 2011 Van depremleri örneği. 19. Ulusal Psikoloji Kongresi (05- 07 Eylül 2016), p. 49. https://hdl.handle.net/11511/71760 

Kimhi, S., & Shamai, M. (2004). Community resilience and the impact of stress: Adult response to Israel’s withdrawal from Lebanon. Journal of Community Psychology, 32(4), 439- 451.https://doi.org/10.1002/jcop.20012 

Kline, P. (2015). A handbook of test construction (psychology revivals): Introduction to psychometric design, New York: Routledge. 

Korkut-Owen, F., Demirbaş-Çelik, N., & Doğan, T. (2017). Üniversite öğrencilerinde iyilik halinin yordayicisi olarak psikolojik sağlamlik. Elektronik Sosyal Bilimler Dergisi, 16(64-Ek Sayı), 1461-1479. https://doi.org/10.17755/esosder.300405 

Kroenka, K., Spitzer, R. L., Williams, J. B., & Löwe, B. (2009). An ultra-brief screening scale for anxiety and depression: the PHQ–4. Psychosomatics, 50(6), 613-621. https://doi.org/10.1176/appi.psy.50.6.613 

Kulig, J. C., Edge, D. S., Townshend, I., Lightfoot, N., & Reimer, W. (2013). Community resiliency: Emerging theoretical insights. Journal of Community Psychology, 41(6), 758-775. https://psycnet.apa.org/doi/10.1002/jcop.21569 

Lindberg, K., & Swearingen, T. (2020). A reflective thrive-oriented community resilience scale. American Journal of Community Psychology, 65(3-4), 467-478.https://doi.org/10.1002/ajcp.12416 

Norris, F. H., Stevens, S. P., Pfefferbaum, B., Wyche, K. F., & Pfefferbaum, R. L. (2008). Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. American Journal of Community Psychology, 41(1), 127-150. https://doi.org/10.1007/s10464-007-9156-6 

Patel, S. S., Rogers, M. B., Amlôt, R., & Rubin, G. J. (2017). What do we mean by’community resilience’? A systematic literature review of how it is defined in the literature. PLoS currents, 9. 10.1371/currents.dis.db775aff25efc5ac4f0660ad9c9f7db2 

Pfefferbaum, R. L., Neas, B. R., Pfefferbaum, B., Norris, F. H., & Van Horn, R. L. (2013). The Communities Advancing Resilience Toolkit (CART): development of a survey instrument to assess community resilience. International journal of emergency mental health, 15(1), 15-29. 10.1097/PHH.0b013e318268aed8 

Plodinec, M. J. (2013). Definitions of community resilience: an analysis. Community & Regional Resilience Institute https://s31207.pcdn.co/wp-content/uploads/2019/08/Definitions-of-community-resilience.pdf 

Schermelleh-Engel, K. & Moosbrugger, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23- 74. 

Smith, B. W., Dalen, J., Wiggins, K., Tooley, E., Christopher, P., & Bernard, J. (2008). The Brief Resilience Scale: Assessing the ability to bounce back. International Journal of Behavioural Medicine, 15(3), 194–200. https://doi. org/10.1080/1070550080 2222972

Sonn, C., & Fisher, A. (1998). Sense of community: Community resilient responses to oppression and change. Journal of Community Psychology, 26, 457–472. https://doi.org/10.1002/(SICI)1520- 6629(199809)26:5%3C457::AID-JCOP5%3E3.0.CO;2-O 

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson 

The International Federation of Red Cross and Red Crescent Societies (IFRC) (2018). IFRC Framework for Community Resilience. https://www.ifrc.org/document/ifrc-framework-community-resilience 

The jamovi project (2020). jamovi (Version 1.2) [Computer Software]. https://www.jamovi.org 

Yalçın, İ. (2015). İyi oluş ve sosyal destek arasındaki ilişkiler: Türkiye’de yapılmış çalışmaların meta analizi [Relationships between well-being and social support: A meta analysis of studies conducted in Turkey]. Türk Psikiyatri Dergisi, 26(1), 21–32. https://doi.org/10.5080/u7769

 

Sitemizden en iyi şekilde faydalanabilmeniz için çerezleri kullanmaktayız.    Daha Fazla Bilgi