| dc.identifier.uri | http://hdl.handle.net/11401/77595 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Autism is a common condition with often debilitating symptoms, and both the methods and results of behavioral treatments vary widely. It remains largely unknown which patients will respond best to which treatments, which is especially problematic because treatments are time-consuming and expensive. The present study is the first to use predictive data analysis to examine how the outcomes of behavioral interventions targeted at social competence can be statistically predicted from pretreatment measures. The study used five previously collected datasets, including patients aged 5 to 18, and including treatments such as skillstreaming, a Second Step program, and socio-dramatic affective-relational intervention. However, results indicated that by and large, pretreatment measures (other than the same instrument used for the outcome variable) were not predictive of outcomes. Follow-up analyses simulating the effects of treatment on broad populations weakly indicated that socio-dramatic affective-relational intervention would increase externalizing behavior overall, but also slightly increase self-control. Other kinds of pretreatment measures may be necessary to accurately predict treatment outcomes. | |
| dcterms.available | 2017-09-20T16:52:57Z | |
| dcterms.contributor | Luhmann, Christian C. | en_US |
| dcterms.contributor | Gerrig, Richard | en_US |
| dcterms.contributor | Lerner, Matthew | en_US |
| dcterms.contributor | Shic, Frederick. | en_US |
| dcterms.creator | Arfer, Kodi | |
| dcterms.dateAccepted | 2017-09-20T16:52:57Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:57Z | |
| dcterms.description | Department of Experimental Psychology. | en_US |
| dcterms.extent | 42 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/77595 | |
| dcterms.issued | 2016-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:57Z (GMT). No. of bitstreams: 0
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | autism, behavioral intervention, machine learning, prediction, social skills | |
| dcterms.subject | Quantitative psychology | |
| dcterms.title | Predicting outcomes of interventions to increase social competence in children and adolescents | |
| dcterms.type | Dissertation | |
