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Introduction It is widely recognized that the prevalence and diagnosis of autism spectrum disorder (ASD) are more common in males than in females. Despite this, there is a significant gap in the body of autism research that investigates gender differences for treatment effects of applied behavior analysis (ABA) across a variety of measured variables. This research aims to comprehensively evaluate gender distinctions concerning target behavioral objectives, goals, and deficit variables. Materials and methods This study analyzed retrospective data from 100 participants, including 89 juveniles and four adults, with seven cases lacking age documentation, who underwent a three-month ABA program from March 19 to June 11, 2023. The ABA program included various methodologies such as functional analysis, discrete trial training, mass trials, and naturalistic training. Data on outcome measures, including target behavioral proficiency, age, average trials to proficiency, average teaching days to proficiency, open behavioral objectives, and target trends, were collected using the “Catalyst” software (Catalyst Software Corporation, New York, NY). Participant demographics were summarized using statistical analyses for categorical (gender and race/ethnicity) and continuous variables (percentage of mastered behavioral objectives, age, average trials, average teaching days, open objectives, percentage of failed objectives during maintenance, percentage of objectives with upward, downward, and flat trends). These statistics included mean, standard deviation, median, and range and were analyzed inferentially using nine separate two-sample independent t-tests and corresponding effect sizes using Cohen's d. Results There were no statistically significant disparities based on gender (p > 0.05) across all nine variables examined: Percentage of Targets Mastered, Age, Average Trials to Mastery, Average Teaching Days to Mastery, Open Targets, Percentage of Targets Failed in Maintenance, Percentage of Targets Trending Up, Percentage of Targets Trending Down, and Percentage of Targets Trending Flat, and wide confidence intervals were detected. Conclusions Non-significant gender differences in response to ABA treatments regarding these nine behavioral goals, mastery, and deficit variables may be relevant. They suggest that ABA treatments could be equally beneficial for both male and female autistic individuals. These results should be interpreted cautiously. The general pattern observed, characterized by broad confidence intervals, carries a degree of statistical uncertainty, which may suggest substantial gender differences. These results might question the prevailing beliefs about the variation in treatment response based on gender. This could profoundly impact clinical practices, implying that healthcare professionals should not favor one gender over another when suggesting ABA therapies. Instead, the treatment advice should be tailored to each child's uniqu...
Introduction It is widely recognized that the prevalence and diagnosis of autism spectrum disorder (ASD) are more common in males than in females. Despite this, there is a significant gap in the body of autism research that investigates gender differences for treatment effects of applied behavior analysis (ABA) across a variety of measured variables. This research aims to comprehensively evaluate gender distinctions concerning target behavioral objectives, goals, and deficit variables. Materials and methods This study analyzed retrospective data from 100 participants, including 89 juveniles and four adults, with seven cases lacking age documentation, who underwent a three-month ABA program from March 19 to June 11, 2023. The ABA program included various methodologies such as functional analysis, discrete trial training, mass trials, and naturalistic training. Data on outcome measures, including target behavioral proficiency, age, average trials to proficiency, average teaching days to proficiency, open behavioral objectives, and target trends, were collected using the “Catalyst” software (Catalyst Software Corporation, New York, NY). Participant demographics were summarized using statistical analyses for categorical (gender and race/ethnicity) and continuous variables (percentage of mastered behavioral objectives, age, average trials, average teaching days, open objectives, percentage of failed objectives during maintenance, percentage of objectives with upward, downward, and flat trends). These statistics included mean, standard deviation, median, and range and were analyzed inferentially using nine separate two-sample independent t-tests and corresponding effect sizes using Cohen's d. Results There were no statistically significant disparities based on gender (p > 0.05) across all nine variables examined: Percentage of Targets Mastered, Age, Average Trials to Mastery, Average Teaching Days to Mastery, Open Targets, Percentage of Targets Failed in Maintenance, Percentage of Targets Trending Up, Percentage of Targets Trending Down, and Percentage of Targets Trending Flat, and wide confidence intervals were detected. Conclusions Non-significant gender differences in response to ABA treatments regarding these nine behavioral goals, mastery, and deficit variables may be relevant. They suggest that ABA treatments could be equally beneficial for both male and female autistic individuals. These results should be interpreted cautiously. The general pattern observed, characterized by broad confidence intervals, carries a degree of statistical uncertainty, which may suggest substantial gender differences. These results might question the prevailing beliefs about the variation in treatment response based on gender. This could profoundly impact clinical practices, implying that healthcare professionals should not favor one gender over another when suggesting ABA therapies. Instead, the treatment advice should be tailored to each child's uniqu...
Introduction Applied behavior analysis (ABA) is a therapy that focuses on improving specific behaviors using positive and negative reinforcement through antecedents, behaviors, and consequences, particularly in individuals with autism and other developmental disorders. It uses the principles of learning theory to bring about meaningful and positive changes in behavior. In ABA treatment, intensity refers to the amount and frequency of therapy an individual receives. This includes weekly hours, session trials, and overall duration. Intensive treatment involves more hours and trials tailored to individual needs and responses. Younger individuals, particularly those with autism, often receive more intensive therapy because early intervention leads to better outcomes. Programs may recommend 25-40 hours per week for young children. As children age, therapy may become less intensive, focusing on specific skills. The study explores how age and treatment intensity affect the mastery of behavioral targets in ABA interventions. Materials and methods This study involved 100 participants (89 children, four adults, and seven instances where the individuals' ages were not recorded due to random data entry errors (MCAR)) who received ABA treatment over three months. The treatments included functional analysis, discrete trials, and mass and naturalistic training. Data on the mastery of target behaviors were collected using the Catalyst software (New York, New York). The primary outcome was the percentage of mastered behavioral targets, indicating the effectiveness of the ABA treatment. Several predictors were examined, including the participant's age and treatment intensity variables, such as the average number of trials and teaching days to achieve behavioral mastery. The interaction effects between age and these treatment intensity variables were analyzed. The study used descriptive and inferential statistics to explore these interactions, including correlational and multiple regression analyses with causal moderator modeling. Results In Model 1, a baseline multiple regression analysis showed that average teaching days significantly predict the percentage of targets mastered. However, its limited explanatory power suggests other variables also play a role. Model 2 introduced interaction effects using causal models, revealing that age moderates the relationship between treatment variables and behavioral outcomes. This model provided a more nuanced understanding but still had room for improvement. Model 3 further refined the approach, achieving higher R-values and lower standard error. It highlighted age's significant role in modifying the impact of teaching days on mastery. This model's superior performance emphasizes the importance of considering age as a moderating factor in ABA interventions, leading to more effective and personalized behavior therapy. Conclusions This study significantly enhances our understanding of the complex interactions b...
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