A Quantitative Simulation Study of the Relationship Between Anxiety, Psychological Resilience, and Life Satisfaction Using Graph-Theoretic and Statistical Models
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Abstract
This paper investigates the relationship between anxiety, psychological resilience and life satisfaction with each other using Monte Carlo simulation. Latent population values for the dataset were obtained by the correlations provided by Sarid et al. (2024), and from these, the item responses for the GAD-7, CD-RISC-10 and the SWLS were simulated for 500 cases. Pearson correlation, hierarchical regression, bootstrapped mediation and moderated regression were used and a Monte Carlo experiment (2 000 replications at each of five sample sizes) was conducted to examine the performance of the mediation analysis. Anxiety was negatively related to life satisfaction (r = −.29) and resilience positively (r = .43). The indirect effect with resilience as the first step in the chain proved significant (ab = −.082, 95% CI [−.117, −.049]), as did the indirect effect with the order reversed, and the two models fit equally well. No buffering interaction was found, and a data-generating model without any buffering interaction is consistent with this. Measurement error reduced the true indirect effect by approximately 27%, and at N = 500, the intervals only contained the latent indirect effect 52% of the time. The findings indicate that cross-sectional mediation analysis does not allow determining the sequence of effects, and measurement errors affect indirect effects, even if the scales are highly reliable.
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