Predicting Autism in Young Children Based on Social Interaction and Selected Demographic Variables

Persistent Link:
http://hdl.handle.net/10150/145365
Title:
Predicting Autism in Young Children Based on Social Interaction and Selected Demographic Variables
Author:
Princiotta, Dana Kristina
Issue Date:
2011
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The purpose of the present study was to examine whether an autism diagnosiscould be predicted by social interaction as measured by the Ghuman-Folstein Screen forSocial Interaction in conjunction with selected demographic variables (i.e., sex, age,ethnicity, mother's educational level, and socio-economic status). Univariate andbivariate analyses were conducted to explore each predictor variable and to explorepossible relationships between predictor variables and autism. Binary logistic regressionwas utilized to examine various models' ability to predict autism. The final model wasable to correctly identify 74% of the cases. The GF-SSI was the greatest predictor ofautism. The selected demographic variables were not significant predictors of autism.These results were discussed in relation to the literature on sex, age, ethnicity, maternaleducation and socio-economic status. Future directions for research were also discussed.
Type:
Electronic Dissertation; text
Keywords:
Autism; Demographic; Prediction; Social Interaction; Young children
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; School Psychology
Degree Grantor:
University of Arizona
Advisor:
Morris, Richard J.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titlePredicting Autism in Young Children Based on Social Interaction and Selected Demographic Variablesen_US
dc.creatorPrinciotta, Dana Kristinaen_US
dc.contributor.authorPrinciotta, Dana Kristinaen_US
dc.date.issued2011-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe purpose of the present study was to examine whether an autism diagnosiscould be predicted by social interaction as measured by the Ghuman-Folstein Screen forSocial Interaction in conjunction with selected demographic variables (i.e., sex, age,ethnicity, mother's educational level, and socio-economic status). Univariate andbivariate analyses were conducted to explore each predictor variable and to explorepossible relationships between predictor variables and autism. Binary logistic regressionwas utilized to examine various models' ability to predict autism. The final model wasable to correctly identify 74% of the cases. The GF-SSI was the greatest predictor ofautism. The selected demographic variables were not significant predictors of autism.These results were discussed in relation to the literature on sex, age, ethnicity, maternaleducation and socio-economic status. Future directions for research were also discussed.en_US
dc.typeElectronic Dissertationen_US
dc.typetexten_US
dc.subjectAutismen_US
dc.subjectDemographicen_US
dc.subjectPredictionen_US
dc.subjectSocial Interactionen_US
dc.subjectYoung childrenen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSchool Psychologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorMorris, Richard J.en_US
dc.contributor.committeememberGhuman, Jaswinderen_US
dc.contributor.committeememberPerfect, Michelleen_US
dc.contributor.committeememberJohnson, Christopheren_US
dc.contributor.committeememberBechtel, Roberten_US
dc.identifier.proquest11284-
dc.identifier.oclc752261292-
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