The Resilience Protective Factors Inventory: Psychometric Evaluation of a Uni-Dimensional and Multi-Dimensional Factor Structure
The concept of resilience was introduced into the psychological lexicon over 40 years ago and is associated with the ability to bounce back or recover from adverse conditions. Studying resilience is important since understanding human capacity for positive adaptation in adverse circumstances can assist mental health professionals in developing interventions premised in developing and cultivating specific pathways leading to positive adaptation. Of increasing interest is the application of resilience to an older adult population. Research has shown that aging requires adaptation to multifaceted challenges and resilience may be one factor that facilitates adapting to these multifaceted challenges. This study examined the psychometric properties of a uni-dimensional and multi-dimensional model of resilience protective factors in an older adult population.
The hypothesized factor structure was derived through a systematic review of resilience instruments and a qualitative interpretive meta-synthesis. The a priorifactor structure consisted of 9 factors and was evaluated using confirmatory factor analysis. Older adults with sufficient English literacy were recruited from independent living facilities and senior centers (N=151). Reliability was assessed using Chronbach’s alpha. Construct validity was evaluated using the Resilience Scale and the Geriatric Depression Scale.
The hypothesized uni-dimensional 9-factor model was not a good fit with the data (χ2 =1338.71, df=862, p= .00; RMSEA = 0.06; CFI = 0.87; GFI = 0.73). Post hoc analysis of the data was performed to test for the presence of interrelated sub dimensions. Individual factors were categorized based on theoretical similarities (internal versus external factors). The first measurement model was termed the Behaviors and Experience Resilience Protective Factors Inventory (BERPI) and included factors that represented actions, behaviors and experiences: 1) External Connections; 2) Self-Acceptance; 3) Self-Care; and 4) Previous Experience with Hardship. The BERPI was a good fit with the data (χ2=130.30, df=112, p= .14; RMSEA = 0.03; CFI = 0.98; GFI = 0.91). The second model was named the Internal Resilience Protective Factors Inventory (IRPFI) and included two higher order factors: Conviction and Fortitude. The higher order factor Fortitude contained the protective factors termed Grit and Independence. The second higher order factor termed Conviction contained 3 protective factors: 1) Positive Perspective on Life; 2) Meaningfulness; and 3) Self-Acceptance. The IRPFI was a good fit with the data (χ2=116.50, df=97, p= .09; RMSEA = 0.03; CFI = 0.98; GFI = 0.92). Reliability was assessed using Chronbach’s alpha for the IPFI (alpha=.890) and BERPFI (alpha=.901). Construct validity was supported by positive correlations with the Resilience Scale (IRPFI=.77; BEPFI=.75) and negative correlations with Geriatric Depression Scale (IRPFI=-.26; BEPFI=-.26).
Conclusions and Implications:
Findings from this study offers insight relative to the multi-dimensional nature of resilience protective factors specific to an older adult population. Further testing of the psychometric properties of the IRPFI and the BERPI are required prior to utilization as a clinical diagnostic tool. However, examining the protective factors may assist gerontologists and other allied health professionals in assisting older adults in positive adaptation to multifaceted challenges.