Psychometric evaluation of the extended inclusion of nature in self scale in Chinese adults through the Rasch analysis

Abstract How humans connect with nature impacts the well‐being of both people and ecosystems. Being disconnected from nature can lead to attitudes and behaviours that ultimately damage our physical and mental health and cause irreparable harm to the planet. To measure the human‐nature connection, measurements with good psychometric properties are warranted. By synthesizing the existing instruments, we suggested the distinctive advantages of the Extended Inclusion of Nature in Self scale (EINS), such as using multiple graphical spatial metaphors. Based on the back‐translation method, we translated the EINS into a Chinese version (EINS‐C). Then, using the Rasch model and the classical test theory, we elaborately examined the psychometric properties of the scale with a sample of 786 Chinese adults. Exploratory factor analysis, principal components analysis of Rasch residuals, and confirmative factor analysis together provided robust evidence for the one‐factor structure of the EINS‐C. Category probability curves showed the satisfying functioning of the rating scale. The scale further exhibited high criterion validity, convergent validity, internal consistency and test–retest reliability. The Rasch model fit statistics were also substantial for items. Each item presented a moderate difficulty, suggesting high discrimination power for levels of self‐nature connection. Scores on the scale could be meaningfully compared across genders, as no substantial differential item functioning was found. The EINS‐C consequently demonstrated generally robust psychometric properties, which could be applied to the Chinese‐speaking community, approximately 16 per cent of the world's population. This high‐quality and convenient tool for evaluating and monitoring the relationship between humans and nature lays a foundation for and might facilitate conservation‐related research and policy formulation. Read the free Plain Language Summary for this article on the Journal blog.