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

There are several models for estimating prospective within-individual effects between constructs. Researchers in psychology usually pick one model and ignore the others. Here, we propose that multiverse analyses with meta-analytic aggregation may be a better option for assessing prospective effects. The employed models may include the random-intercept cross-lagged panel model (RI-CLPM), the latent change score model (LCSM), the stable trait, autoregressive trait, and state (STARTS) model, a reversed version of the RI-CLPM, as well as corresponding multilevel models (MLM). In an application on data on trust, loneliness, and life satisfaction, the models suggested diametrically different prospective effects. Meta-analytic aggregations, on the other hand, indicated increasing prospective within-individual effects between loneliness and trust and between loneliness and life satisfaction and decreasing prospective effects between trust and life satisfaction. However, a good fit of the model of spurious longitudinal associations (MoSLA) suggested that the effects may have been spurious. For increased rigor and transparency, we recommend researchers assessing within-individual prospective effects to use multiverse analyses with meta-analytic aggregation and the MoSLA.