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Eldeeb, Sherief Y.; Ludwig, Natasha N.; Wieckowski, Andrea Trubanova; Dieckhaus, Mary F. S.; Algur, Yasemin; Ryan, Victoria; Dufek, Sarah; Stahmer, Aubyn; Robins, Diana L. – Autism: The International Journal of Research and Practice, 2023
Males are more likely to be diagnosed with autism than females, and at earlier ages, yet few studies examine sex differences in screening. This study explored sex differences in psychometric properties, recommended cutoff scores, and overall scores of the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up. Participants were 28,088…
Descriptors: Gender Differences, Autism Spectrum Disorders, Screening Tests, Disability Identification
Ghalwash, Mohamed F. – ProQuest LLC, 2013
Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems,…
Descriptors: Classification, Accuracy, Multivariate Analysis, Time
He, Jian-Ping; Burstein, Marcy; Schmitz, Anja; Merikangas, Kathleen R. – Journal of Abnormal Child Psychology, 2013
The Strengths and Difficulties Questionnaire (SDQ) is one of the most commonly used instruments for screening psychopathology in children and adolescents. This study evaluated the hypothesized five-factor structure of the SDQ and examined its convergent validity against comprehensive clinical diagnostic assessments. Data were derived from the…
Descriptors: Factor Analysis, Psychopathology, Validity, Behavior Disorders
Carl, Bradley; Richardson, Jed T.; Cheng, Emily; Kim, HeeJin; Meyer, Robert H. – Journal of Education for Students Placed at Risk, 2013
This article describes the development of early warning indicators for high school and beyond in the Milwaukee Public Schools (MPS) by the Value-Added Research Center (VARC) at the University of Wisconsin-Madison, working in conjunction with staff from the Division of Research and Evaluation at MPS. Our work in MPS builds on prior early warning…
Descriptors: High Schools, Public Schools, School Districts, Urban Education
VanDerHeyden, Amanda – Theory Into Practice, 2010
RTI as a framework for decision making has implications for the diagnosis of specific learning disabilities. Any diagnostic tool must meet certain standards to demonstrate that its use leads to predictable decisions with minimal risk. Classification agreement analyses are described as optimal for demonstrating the technical adequacy of RTI…
Descriptors: Learning Disabilities, Screening Tests, Classification, Models
VanDerHeyden, Amanda M. – Exceptional Children, 2011
Perhaps the greatest value of response to intervention (RTI) as a decision framework is that it brings attention to variables (e.g., mastery of prerequisite skills, frequency of instructional corrective feedback, reinforcement schedules for correct responding) that if changed might make a meaningful difference for students (e.g., child rate of…
Descriptors: Feedback (Response), Intervention, Classification, Response to Intervention
Linn, Shai – Journal of Statistics Education, 2004
Courses in clinical epidemiology usually include acquainting students with a single 2X2 table. All diagnostic test characteristics are explained using this table. This pedagogic approach may be misleading. A new didactic approach is hereby proposed, using two tables, each with specific analogous notations (uppercase and lowercase) and derived…
Descriptors: Epidemiology, Diagnostic Tests, Bayesian Statistics, Prediction
Peer reviewedVolger, George P.; And Others – Journal of Learning Disabilities, 1984
Self-reported reading ability of parents of 174 reading-disabled children and of 182 controls was used to estimate the probability that a child will become reading disabled. Using Bayesian inverse probability analysis, it was found that the risk for reading disability is increased substantially if either parent has had difficulty in learning to…
Descriptors: Disability Identification, Family Characteristics, High Risk Persons, Parents

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