Psychometric Properties and Network Structure of the Domain-Specific Climate Change Distress Scale

Climate change increasingly poses a threat to psychological health. The Domain-Specific Climate Change Distress Scale (DCCDS) was developed to capture climate-related distress across six thematic (ecology, existence, food supply, future generations, society, and wealth) and one generic domain. Building on its theoretical foundation, the present study extends initial validation efforts by providing large-scale psychometric data and normative values, drawing on a large, diverse sample to further substantiate the scale's psychometric properties and practical utility.

We pooled data from seven independent German-speaking samples (N = 894; Mage = 39.2, SD = 13.5; 53% women). We replicated the bifactor S-1 model using confirmatory factor analysis (CFA), tested measurement invariance across gender, and estimated a regularized network. Exploratory graph analysis (EGA) served as a dimensionality check. Bootstrap stability analyses and a network comparison test (NCT) assessed the robustness of network indices and gender differences.

CFA confirmed acceptable fit of the bifactor S-1 model. Metric invariance across gender was fully supported; partial scalar invariance was established after freeing intercepts for three items. In the regularized network, items related to food supply, future generations, and generic climate anxiety showed the highest centrality. EGA identified six stable empirical dimensions, with society and wealth items loading onto a single community. NCT revealed equivalent global network structure across gender. Gender-stratified and age-stratified normative percentiles are provided.

The DCCDS demonstrates robust psychometric properties across independent samples and is largely measurement-equivalent across gender. Findings support its use for individual diagnostics and group comparisons in climate psychology research and clinical settings. The empirical identification of six rather than seven dimensions further invites reconsideration of the Society and Wealth domain distinction. Normative data facilitate score interpretation in applied settings.