Further evaluation of a decision‐making algorithm supporting visual analysis of time‐series data

Abstract

AbstractVisual analysis is a cornerstone of decision‐making in Applied Behavior Analysis. Individuals responsible for implementing behavioral interventions and analyzing data are often behavior technicians who may not be provided with the training necessary to be proficient in visual analysis. Therefore, there is a need for an effective and streamlined method to train visual analysis. Previous research has suggested using a decision‐making algorithm (DMA) to aid individuals in making decisions about time‐series data. The current study further evaluated the effects of a DMA on accurate visual analysis of time‐series data. We presented graduate students with time‐series graphs, each graph depicting 10 data points which resembled one of the four options depicted in the DMA. The results indicated five of the six participants demonstrated an increase in correct responding when the DMA was introduced. One participant (Participant 4) required an asynchronous feedback session. Correct responding maintained for five of the six participants when the DMA was removed. Following the maintenance probe, high levels of percentage of correct responding maintained for four of the six participants.