Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations

Frankot, Michelle A.; Young, Michael E.; Vonder Haar, Cole

doi:10.1007/s40614-023-00388-9

Cited by 1 publication

(1 citation statement)

References 79 publications

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“…This study did not take a detailed approach like Linstead et al [ 11 ] where they analyzed the effects and impacts of intensity and duration of ABA intervention on learning across different treatment domains, including academic, adaptive, cognitive, executive function, language, motor, play, and social. This study did, however, follow the suggestions of Linstead et al [ 11 ] and Granpeesheh et al [ 1 ], in that we were able to use the number of mastered behavioral learning objectives determined based on direct observation as the dependent variable and variables including age, open behavioral targets, and behavioral targets failed in maintenance along with intensity and duration variables using a large N design [ 17 , 18 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Behavioral Target Mastery With Age, Intensity and Duration, Open Targets, and Maintenance Failure With Applied Behavior Analysis in Individuals With Autism

Peterson,

Dodson,

Strale,

2024

Cureus

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Background Researchers have studied the effects of age, treatment intensity, and treatment duration with applied behavior analysis (ABA) outcomes in autistic individuals. This study's primary objective was to evaluate the predictive capabilities of age, intensity, and duration of treatment, open behavioral targets, and behavior maintenance failure on behavioral target mastery. Methods A retrospective cohort of 100 autistic individuals treated with ABA with functional analysis and discrete trial training, mass trials, and naturalistic training were treated and analyzed. Target behavioral mastery data was collected via a retrospective chart review contained within the "Catalyst" tracking software. ABA treatment was administered for three months between March 19, 2023, and June 11, 2023. Multiple linear regression was performed using the percentage of behavioral targets mastered as the dependent variable. The independent variables were age, average trials to behavioral mastery, average teaching days to behavioral mastery, and percentage of behavioral targets that failed in maintenance. Results The multiple linear regression model was statistically significant (R=0.443, R²=0.196, adjusted R 2 =0.150, F(5, 87)=4.239, p=0.002). The average teaching days to mastery (β=0.416, p=0.019) and percentage of targets failed in maintenance (β=0.201, p=0.047) significantly predicted the percentage of behavioral targets mastered. However, age (β=0.079, p=0.419), average trials to mastery (β=-0.271, p=0.114), and open targets (β=0.184, p=0.081) did not significantly predict the percentage of behavioral targets mastered. The analysis of variance (ANOVA) resulted in non-significant (p>0.05) age group differences between the above variables. Conclusions The predictor variables average teaching days to mastery (intensity and duration) and percentage of targets failed in maintenance had a statistically significant effect on the percentage of behavioral targets mastered. The predictor variables age, average trials to behavioral mastery (intensity and duration), and open behavioral targets had a non-significant influence on the percentage of behavioral targets mastered. A non-significant difference between age groups was found in all study variables.

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