Measuring What Matters: An Environmental Scan and Quality Appraisal of Population Well-being Surveys

Population well-being surveys collect information from large, representative groups on social, emotional, and psychological factors contributing to quality of life. Such surveys provide essential data for shaping policies and developing national frameworks to enhance well-being. Ensuring that high-quality measures are used is crucial, as population-level data inform decisions affecting entire societies. This study aimed to identify existing population well-being surveys and assess the quantity and quality of their established measures. Using a quasi-systematic environmental scan guided by scoping review principles (e.g., double-blind screening), we identified 14 population well-being surveys across four continents, administered at intervals ranging from monthly to every five years, with sample sizes between 3,928 and 65,000 participants. We systematically appraised both psychometric and pragmatic quality through double-blind extraction using an established quality assessment tool. The overall mean measure quality was 33.05 (range: 20–46), with comparable psychometric (M = 16.21) and pragmatic (M = 16.84) scores. Psychometrically, measures showed strong convergent but weaker predictive validity. Pragmatically, they were typically brief and highly readable. Findings should be interpreted in light of the quasi-systematic method and English-language restriction. By identifying how survey measures can be improved to better capture population well-being, this study highlights opportunities for advancing both measure development and survey practice. We recommend increased collaboration and open-access sharing to enhance predictive validity, broaden construct coverage, and promote alignment between the conceptualization and assessment of well-being using measures that are both pragmatic and psychometrically robust