Dissertation/Thesis Abstract

Predicting autism in young children based on social interaction and selected demographic variables
by Princiotta, Dana Kristina, Ph.D., The University of Arizona, 2011, 106; 3454330
Abstract (Summary)

The purpose of the present study was to examine whether an autism diagnosis could be predicted by social interaction as measured by the Ghuman-Folstein Screen for Social Interaction in conjunction with selected demographic variables (i.e., sex, age, ethnicity, mother's educational level, and socio-economic status). Univariate and bivariate analyses were conducted to explore each predictor variable and to explore possible relationships between predictor variables and autism. Binary logistic regression was utilized to examine various models' ability to predict autism. The final model was able to correctly identify 74% of the cases. The GF-SSI was the greatest predictor of autism. The selected demographic variables were not significant predictors of autism. These results were discussed in relation to the literature on sex, age, ethnicity, maternal education and socio-economic status. Future directions for research were also discussed.

Indexing (document details)
Advisor: Morris, Richard J.
Commitee: Bechtel, Robert, Ghuman, Jaswinder, Johnson, Christopher, Perfect, Michelle
School: The University of Arizona
Department: School Psychology
School Location: United States -- Arizona
Source: DAI-B 72/07, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Developmental psychology, Psychology
Keywords: Autism, Demographic variables, Prediction, Social interaction, Young children
Publication Number: 3454330
ISBN: 9781124633763
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest