Date of Award
5-3-2013
Document Type
Dissertation
Degree Name
Educational Leadership, Ed.D.
First Advisor
George Foldesy
Committee Members
Amany Saleh; David Cox; Joan Henley; Myleea Hill
Abstract
The purpose of this study was to derive one or more single indicators or sets of correlated indicators that could predict a heightened probability for an autism spectrum disorder (ASD) diagnosis, a critical first step in the development of a risk-model. Educators need a risk-model that will identify infants and young children with ASD before their symptoms are fully manifested. Such a model would help educators to fulfill their federal mandate to provide timely identification and early-intervention services, improving long-term outcomes for affected children. Fortunately, advanced statistical modeling techniques and extant early-intervention data suggest a solution: Exploratory latent class factor analysis (ELCFA) and logistic regression analysis can be used to analyze early-intervention case data and elucidate a set of indicators that predict an ASD diagnosis. Recognizing the aforementioned, this investigator used ELCFA to analyze early-intervention data for 30 risk-indicators for children diagnosed with ASD (n = 167). Use of ELCFA elucidated three sets of correlated indicators that appeared to contribute to a tendency for an ASD diagnosis: Maternal Psychiatric Illness, Gestational Maturity, and Advanced Parental Age. The three constructs and 16 remaining single indicators of interest were juxtaposed against ASD and non-ASD case data, forming the empirical foundation for the logistic regression model. Statistical diagnostics confirmed the adequacy and usefulness of the model [Hosmer and Lemeshow Test, ÷2 (8) = 13.23, p = .104; Omnibus Tests of Model Coefficients table, ÷2 (19) = 35.20, p = .013] and indicated that two predictors contributed to the model significantly. The single indicator Family Member with ASD predicted an ASD diagnosis [OR = 5.182; 95% CI (1.997, 13.445); p = .001] and the multiple-indicator construct Maternal Psychiatric Illness predicted an ASD diagnosis [OR = 1.275; 95% CI (1.003, 1.621); p = .047]. Ultimately, the Gestational Maturity and Advanced Parental Age constructs failed to demonstrate statistical significance. The results of this study suggest that extant early-intervention data may be used to derive a model that can predict a future ASD diagnosis, establishing a warrant for refinement of the model.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Phillips, Cristy S., "Predictors For an Autism Spectrum Disorder Diagnosis: A Risk Modeling Endeavor" (2013). Student Theses and Dissertations. 798.
https://arch.astate.edu/all-etd/798
