Abstract(s)
Since the start of the 21st century, few advances have had as far-reaching impact in science as the
widespread adoption of artificial neural networks in fields as diverse as fundamental physics,
clinical medicine, and psychology. In research methods, one promising area for the adoption of
artificial neural networks involves the analysis of single-case experimental designs. Given that
these types of networks are not generally part of training in the psychological sciences, the
purpose of our paper is to provide a step-by-step introduction to using artificial neural networks
to analyze single-case designs. To this end, we trained a new model using data from a Monte
Carlo simulation to analyze multiple baseline graphs and compared its outcomes to traditional
methods of analysis. In addition to showing that artificial neural networks may produce less error
than other methods, this tutorial provides information to facilitate the replication and extension
of this line of work to other designs and datasets.