A core feature of behavior analysis is the single-subject design, in which each subject serves as its own control. This approach is powerful for identifying manipulations that are causal to behavioral changes but often fails to account for individual differences, particularly when coupled with a small sample size. It is more common for other subfields of psychology to use larger-N approaches; however, these designs also often fail to account for the individual by focusing on aggregate-level data only. Moving forward, it is important to study individual differences to identify subgroups of the population that may respond differently to interventions and to improve the generalizability and reproducibility of behavioral science. We propose that large-N datasets should be used in behavior analysis to better understand individual subject variability. First, we describe how individual differences have been historically treated and then outline practical reasons to study individual subject variability. Then, we describe various methods for analyzing large-N datasets while accounting for the individual, including correlational analyses, machine learning, mixed-effects models, clustering, and simulation. We provide relevant examples of these techniques from published behavioral literature and from a publicly available dataset compiled from five different rat experiments, which illustrates both group-level effects and heterogeneity across individual subjects. We encourage other behavior analysts to make use of the substantial advancements in online data sharing to compile large-N datasets and use statistical approaches to explore individual differences.
Keywords: Big data; Individual differences; Monte Carlo simulation; Multilevel modeling; Open Science; Rat.
© Association for Behavior Analysis International 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.