2134/21550 John Maltby John Maltby Liz Day Liz Day Magdalena Zemojtel-Piotrowska Magdalena Zemojtel-Piotrowska Jarosław Piotrowski Jarosław Piotrowski Hidefumi Hitokoto Hidefumi Hitokoto Tomasz Baran Tomasz Baran Ceri Jones Ceri Jones Anjalee Chakravarty-Agbo Anjalee Chakravarty-Agbo Heather Flowe Heather Flowe An ecological systems model of trait resilience: Cross-cultural and clinical relevance Loughborough University 2016 Resilience Adaptation Recovery Depression Anxiety Psychometrics Medical and Health Sciences not elsewhere classified 2016-06-09 11:06:57 Journal contribution https://repository.lboro.ac.uk/articles/journal_contribution/An_ecological_systems_model_of_trait_resilience_Cross-cultural_and_clinical_relevance/9624857 © 2016. The study explored how scores on the three dimensions of the Engineering, Ecological, and Adaptive Capacity (EEA) trait resilience scale, derived from Holling's ecological systems theory of resilience, demonstrate fit within higher-order bifactor models of measurement, cultural invariance, and associations with clinical caseness of affect. Three samples (295 US adults, and 179 Japanese and 251 Polish university students) completed the EEA trait resilience scale. In addition, a subsample of US adults were administered the Ten-Item Personality Inventory and the Hospital Anxiety and Depression Scale). Across all samples, a higher-order bifactor model provided the best fit of the data, with salience of loadings on the three group factors. A multi-group comparison found configural invariance, but neither metric nor scalar invariance, for EEA resilience scores across the three samples. Among the US sample, engineering and adaptive trait resilience scores predicted clinical caseness of depression, and adaptive trait resilience scores predicted clinical caseness of anxiety, after controlling for sex, age, income, education, employment, and personality. The findings suggest the cross-cultural replicability of the structure (but not the meaning) of the three-factor EEA measure of trait resilience, and its relevance for predicting clinical caseness of affect among a US sample.