No need to be indirect: On the role of data in the validation of theory

In recent years, the case has been made to move from narrative accounts of psychological theories to rigorous mathematical descriptions thereof. Yet, a persistent tension remains in how the models that result from the formalization of psychological theories are to be evaluated -- whether by their theoretical elegance, their ability to reproduce established phenomena, or their quantitative fit to empirical data. This paper challenges an exclusive use of validation paradigms that are based on phenomena or data alone as evaluative unit by reexamining the relationships between theory, phenomena, and data. We argue that testing the tenability of a theory or model while placing too much emphasis on only phenomena or data is inherently fallible. Instead, we argue that both phenomena and data should occupy a central role in theory testing. This perspective demands that models make substantive and explanatory claims while engaging with the full structure of empirical observations. This perspective not only clarifies the dynamic interdependence among theory, phenomena, and data, but also establishes a principled basis for cumulative, iterative scientific progress in psychological science.