Spurious inference in consensus emergence modeling due to the distinguishability problem.

Researchers use consensus emergence models (CEMs) to detect when the scores of group members become similar over time. The purpose of this article is to review how CEMs often lead to spurious conclusions of consensus emergence due to the problem of distinguishability, or the notion that different data-generating mechanisms sometimes give rise to similar observed data. As a result, CEMs often cannot distinguish between observations generated from true consensus processes versus those generated by stochastic fluctuations. It will be shown that a distinct set of mechanisms, none of which exhibit true consensus, nonetheless yield spurious inferences of consensus emergence when CEMs are fitted to the observed data. This problem is demonstrated via examples and Monte Carlo simulations. Recommendations for future work are provided. (PsycInfo Database Record (c) 2022 APA, all rights reserved).