How to model cross-lagged relations in panel data continues to be a source of disagreement in psychological research. While the cross-lagged panel model (CLPM) was the modeling approach of choice for many years, it has also been criticized repeatedly for its inability to separate within-person dynamics from stable between-person differences. Hence, various alternative models that disentangle these forms of variability have been proposed, and these are now rapidly gaining popularity. But not everyone agrees this is the right way forward. CLPM advocates point out that many psychological theories are concerned with longer-lasting differences between individuals, while these differences are not allowed to contribute to the estimation of cross-lagged effects in the novel within-between approaches. Reasoning this way, it is argued that the CLPM is superior when studying such processes, precisely because it includes the chronic between-person differences when estimating prospective effects. The goal of the current paper is to consider this within-between dispute in its broader context and to examine various directions in which this discussion needs expansion. To this end, three different perspectives are adopted: that of the study design, patterns in empirical data, and the nature of our research questions. It will be argued that to move forward, we need to look beyond the narrow focus on how to model our correlational panel data. Progress will involve theorizing more deliberately about the timescale that a process operates on, being more explicit about our research questions, considering alternative designs and models, and familiarizing ourselves with relevant discussions in other disciplines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).