Multiverse analyses can be used to evaluate cross-lagged panel network models: An example with psychological flexibility

Cross-lagged panel network (CLPN) models estimate prospective effects between several nodes (e.g., symptoms) while adjusting for initial scores on the outcome variables. However, it is well established that such adjusted cross-lagged effects may be spurious due to correlations with residuals and regression toward the mean. Here, we recommend that researchers conduct multiverse analyses, where cross-lagged effects are estimated with alternative models. Then, conclusions can be based on meta-analytic aggregations of the estimated effects. Multiverse analyses will add rigor and transparency to analyses by acknowledging and incorporating, instead of ignoring, uncertainty due to the analyzed model. In an application of this methodology, we found that most cross-lagged effects between indicators of psychological flexibility and inflexibility, reported in a recent study, did not survive scrutiny.