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Applying mixed‐effects modeling to single‐subject designs: An introduction
Author(s) -
DeHart William B.,
Kaplan Brent A.
Publication year - 2019
Publication title -
journal of the experimental analysis of behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.75
H-Index - 61
eISSN - 1938-3711
pISSN - 0022-5002
DOI - 10.1002/jeab.507
Subject(s) - mixed model , generalized linear mixed model , statistical inference , aggregate (composite) , subject (documents) , random effects model , statistical model , statistical hypothesis testing , computer science , aggregate data , inference , task (project management) , statistics , psychology , econometrics , machine learning , artificial intelligence , mathematics , engineering , medicine , materials science , meta analysis , systems engineering , library science , composite material
Behavior analysis and statistical inference have shared a conflicted relationship for over fifty years. However, a significant portion of this conflict is directed toward statistical tests (e.g., t ‐tests, ANOVA) that aggregate group and/or temporal variability into means and standard deviations and as a result remove much of the data important to behavior analysts. Mixed‐effects modeling, a more recently developed statistical test, addresses many of the limitations of more basic tests by incorporating random effects. Random effects quantify individual subject variability without eliminating it from the model, hence producing a model that can predict both group and individual behavior. We present the results of a generalized linear mixed‐effects model applied to single‐subject data taken from Ackerlund Brandt, Dozier, Juanico, Laudont, & Mick, 2015, in which children chose from one of three reinforcers for completing a task. Results of the mixed‐effects modeling are consistent with visual analyses and importantly provide a statistical framework to predict individual behavior without requiring aggregation. We conclude by discussing the implications of these results and provide recommendations for further integration of mixed‐effects models in the analyses of single‐subject designs.