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

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.