Multiverse analyses can be used to evaluate within-individual prospective effects: Examples with trust, loneliness, and life satisfaction

This research paper explores the use of multiverse analyses to evaluate within-individual prospective effects, using trust, loneliness, and life satisfaction as case studies. The study compared various statistical models, including the random-intercept cross-lagged panel model, latent change score model, and stable trait, autoregressive trait, and state model, as well as corresponding multilevel models. The findings suggest that these models can produce vastly different results, highlighting the need for a more comprehensive approach. The researchers recommend using multiverse analyses with meta-analytic aggregation and the model of spurious longitudinal associations to increase rigor and transparency in the assessment of within-individual prospective effects. This innovative methodology provides a powerful tool for researchers to uncover the complex relationships between psychological constructs and their implications for individual well-being.