"Many interventions based on the principles of applied behavior analysis (ABA) have been regarded as evidence-based practices that are highly effective for children with autism spectrum disorder (ASD). Interventions based on ABA typically rely on the observation and measurement of human behavior for implementation and analysis. Thus, the strength and validity of the ABA intervention is reliant on the accurate and reliable collection of data. However, ABA researchers traditionally collect behavioral data through human observation, and this can increase the potential for error such as bias. Recent developments in technology have introduced various automated data collection apparatus that can efficiently collect reliable and more accurate data. Although other fields such as health, business, and policy have utilized automated data collection apparatus, the field of ABA has yet to fully incorporate these apparatuses to aid research. Utilizing automated data collection in ABA interventions may increase potency and inform current interventions and provide ideas for new interventions. Therefore, the current dissertation investigated the use of automated data collection in ABA research, the reliability of using an automated data collection apparatus for children with ASD, and the application of an automated data collection apparatus for language research in children with ASD. These three independent studies are presented as three chapters (Chapter 2, 3, and 4). Chapter 2 investigated the current use of automated data collection apparatus in ABA research between 2010 to 2018 in a systematic literature review. A hand-search of selected ABA journals revealed that only 149 studies out of 1466 total published studies used an automated data collection apparatus. The results provided support that ABA research may not be fully utilizing technology that can possibly increase accuracy and efficiency in measurement and data collection. Benefits and implications of using an automated data collection apparatus in the field of ABA are also discussed. Chapter 3 provided a reliability analysis for the Language Environment Analysis (LENARTM) system for children with ASD. The LENA system is an automated data collection device developed for use in language studies involving typically developing children. Although many researchers use the LENA system for language research in children with ASD there has not been a stand-alone reliability analysis of the device's use in this population. The primary investigator compared child vocalization and conversation counts collected by the LENA system and human coders and calculated the intraclass correlation coefficient (ICC) between the LENA system and the human coders to assess reliability. The results indicated that although the mean ICC between human coders and the LENA system was high, researchers should exercise caution when using some measures collected by the LENA system for some children with ASD. Finally, Chapter 4 presents an exploratory analysis regarding the effects of environmental variables on the quantity of language in children with ASD. The primary investigator used the LENA system to investigate whether environmental variables such as location, instructional grouping, intervention delivery method, and learning objectives affect the quantity of child vocalization and conversational turns. Results indicated that children with ASD had a statistically significant increase in vocalizations during inclusion, in group settings, and when the intervention was delivered naturally. The implications of manipulating environmental variables to increase language teaching and learning opportunities for children with ASD are also discussed."--Pages ii-iii.
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