Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations

被引:2
作者
Frankot, Michelle A. [1 ,2 ]
Young, Michael E. [3 ]
Haar, Cole Vonder [1 ,2 ]
机构
[1] West Virginia Univ, Dept Psychol, Injury & Recovery Lab, Morgantown, WV 26506 USA
[2] Ohio State Univ, Dept Neurosci, Injury & Recovery Lab, 460 West 12th Ave, Columbus, OH 43210 USA
[3] Kansas State Univ, Dept Psychol Sci, Manhattan, KS USA
关键词
Individual differences; Multilevel modeling; Monte Carlo simulation; Open Science; Big data; Rat; MOUSE STRAIN DIFFERENCES; DECISION-MAKING; LATENT PROFILE; GAMBLING TASK; INFERENCE; ORGANISM; RAT; CONSEQUENCES; ABILITY; CHOICE;
D O I
10.1007/s40614-023-00388-9
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
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.
引用
收藏
页码:225 / 250
页数:26
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