Predictors of Autism Enrollment in Public School Systems

Autor/inn/en	Boswell, Katelyn; Zablotsky, Benjamin; Smith, Christopher
Titel	Predictors of Autism Enrollment in Public School Systems
Quelle	In: Exceptional Children, 81 (2014) 1, S.96-106 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0014-4029
DOI	10.1177/0014402914532230
Schlagwörter	Predictor Variables; Autism; Enrollment Trends; Pervasive Developmental Disorders; Public Schools; Incidence; Socioeconomic Status; Racial Differences; Geographic Location; Clinical Diagnosis; Special Education; Data Analysis; Resource Allocation; School Districts; Asperger Syndrome; Statistical Analysis; Elementary School Students; Secondary School Students; Preschool Children; Kindergarten; Teacher Student Ratio; Student Mobility; Attendance; Income; Maryland + Suchen Sie Ihr Suchwort? Prädiktor; Autismus; Public school; Öffentliche Schule; Vorkommen; Socio-economic status; Sozioökonomischer Status; Rassenunterschied; Special needs education; Sonderpädagogik; Sonderschulwesen; Auswertung; Ressourcenallokation; School district; Schulbezirk; Asperger-Syndrom; Statistische Analyse; Sekundarschüler; Pre-school age; Preschool age; Child; Children; Pre-school education; Preschool education; Vorschulalter; Kind; Kinder; Vorschulkind; Vorschulkinder; Vorschulerziehung; Vorschule; Lehrer-Schüler-Relation; Student; Students; Mobility; Schüler; Schülerin; Studentin; Mobilität; Anwesenheit; Einkommen
Abstract	With a number of disparities present in the diagnosis and treatment of children with autism spectrum disorders, the education system plays a crucial role in the provision of both these service elements. Based on school and federal census data, this article examines one state's public school autism enrollment and possible predictors of enrollment within each jurisdiction. The authors' analyses found that actual prevalence is inconsistent with expectations across jurisdictions, with socioeconomic status indicators, race, geographic location, and racial diagnostic discrepancies in special education significantly predicting enrollment. This report exemplifies how secondary analysis of educational data can allow states to better allocate funding, begin to address issues pertaining to lags and unmet standards, and find model systems within their states. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10