A Framework for Integrating Necessary Condition Analysis and Nomological Networks to Establish Construct Validity Beyond Correlation

Abstract: Introduction: Borsboom et al. (2004) argued that construct validity requires causal evidence, not just correlational patterns within nomological networks. However, causal validation is methodologically challenging. Necessary Condition Analysis (NCA) identifies conditions that must be present for an outcome to occur, complementing correlation-based approaches that test whether a condition is sufficient. This study integrates NCA with nomological networks to strengthen construct validity evidence. Aim: We propose and test a novel framework combining NCA and nomological networks to validate a new life satisfaction measurement scale. Methods: Three studies were conducted: Study 1 ( N = 482, U.S.) assessed convergent validity and nomological alignment. Studies 2 ( N = 372, China) and 3 ( N = 300, longitudinal) applied NCA to test if (1) positive affect is necessary for life satisfaction and (2) changes in life satisfaction are necessary for changes in the new measure. Results: NCA confirmed positive affect as a necessary condition for life satisfaction in both measures (negative affect was not). Longitudinal NCA showed that changes in life satisfaction were necessary for changes in the new measure ( d ≥ .26). Discussion/Conclusion: Integrating NCA with nomological networks extends construct validity beyond correlations, offering a robust, methodologically accessible approach for causal validation in psychological measurement.