Further evaluation of a decision‐making algorithm supporting visual analysis of time‐series data
A one-page checklist turns novices into accurate graph readers in one sitting.
01Research in Context
What this study did
Kril et al. (2022) built a short checklist that tells you how to look at an A-B graph. They gave the checklist to six graduate students who had never run visual analysis before.
The students practiced with the checklist, then tried to judge real graphs on their own. The team later took the checklist away to see if the skill stuck.
What they found
Five of the six students scored higher when they used the checklist. Four still scored high two weeks after the checklist was gone.
The skill helped them on brand-new graphs they had never seen.
How this fits with other research
O'Grady et al. (2021) ran a larger test with random groups and got the same lift. Their lecture and computer lessons match the small-group result here, so the effect looks solid.
Blair et al. (2019) taught the same skill with equivalence-based cards instead of a checklist. Both methods worked and lasted, so you can pick the tool that fits your style.
Wolfe et al. (2019) wrote the earlier step-by-step rules that the checklist turns into a fast yes/no flow. The new study shows those rules work even when squeezed into a tiny decision tree.
Why it matters
If you train RBTs or supervise students, give them the one-page algorithm before they graph probe data. It takes minutes to teach and cuts rookie mistakes. Keep a laminated copy by the computer and let them use it until they sound like you.
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02At a glance
03Original 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.
Behavioral Interventions, 2022 · doi:10.1002/bin.1895