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Behavior analysts visually analyze graphs to interpret data in order to make data-based decisions. Though front-line employees implement behavioral interventions on a daily basis, they are not often trained to interpret these data. A clinical decision-making model may aid front-line employees in learning how to interpret graphs. A multiple-baseline-across-participants design was used to evaluate the effectiveness of a clinical decision-making model on the percentage of correct responses when interpreting line graphs. All of the participants increased their percentage of correct responses after the introduction of the clinical decision-making model. Two of the eight participants required additional feedback. The implications of these findings are discussed.

We compared the results of a brief electronic pictorial multiple-stimulus without replacement (EP-MSWO) preference assessment to a brief tangible MSWO preference assessment in five children with autism. Results of both assessments yielded a match between high preferred (HP) toys for four participants and low preferred toys for three participants. The overall correlation between assessments across participants was strong and statistically significant (? = 0.67, p < 0.01). A reinforcer assessment conducted with three participants confirmed HP toys identified in the EP-MSWO functioned as reinforcers.