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Direct linkKathleen Lynne Lane; Katie Scarlett Lane Pelton; Nathan Allen Lane; Mark Matthew Buckman; Wendy Peia Oakes; Kandace Fleming; Rebecca E. Swinburne Romine; Emily D. Cantwell – Behavioral Disorders, 2025
We report findings of this replication study, examining the internalizing subscale (SRSS-I4) of the revised version of the Student Risk Screening Scale for Internalizing and Externalizing behavior (SRSS-IE 9) and the internalizing subscale of the Teacher Report Form (TRF). Using the sample from 13 elementary schools across three U.S. states with…
Descriptors: Data Analysis, Decision Making, Data Use, Measures (Individuals)
Yu Bao; Jin Liu; Christine DiStefano; Ruyi Ding – Psychology in the Schools, 2025
Behavioral and emotional disorders in childhood can have lasting impacts in areas such as education and future employment, often extending into adulthood. Identifying the potential disorders in children's early grades is beneficial to provide proactive assistance. In this study, we employed a well-validated scale - the Strengths and Difficulties…
Descriptors: Identification, Behavior Problems, Emotional Disturbances, Goodness of Fit
Jessica R. Bagneris; Edward D. Scott Jr. – Psychology in the Schools, 2025
Bias influencing teachers' classroom management is increasingly clear, but the circumstances that influence the likelihood of relying on those biases are less understood. This study employed Classification and Regression Tree (CART) analysis, resulting in four models examining how teachers' appraisals of first-grade students' externalizing problem…
Descriptors: Predictor Variables, Behavior Problems, Classification, Regression (Statistics)
Yen-Chin Wang; Chung-Yuan Cheng; Chi-Shin Wu; Chi-Chun Lee; Susan Shur-Fen Gau – Autism: The International Journal of Research and Practice, 2025
Machine-learning models can assist in diagnosing autism but have biases. We examines the correlates of misclassifications and how training data affect model generalizability. The Social Responsive Scale data were collected from two cohorts in Taiwan: the clinical cohort comprised 1203 autistic participants and 1182 non-autistic comparisons, and…
Descriptors: Artificial Intelligence, Autism Spectrum Disorders, Clinical Diagnosis, Error Patterns
Kathleen Lynne Lane; Nathan Allen Lane; Mark Matthew Buckman; Katie Scarlett Lane Pelton; Kandace Fleming; Rebecca E. Swinburne Romine – Behavioral Disorders, 2025
We report the results of a convergent validity study examining the externalizing subscale (SRSS-E5, five items) of the adapted Student Risk Screening Scale for Internalizing and Externalizing (SRSS-IE 9) with the externalizing subscale of the Teacher Report Form (TRF) with two samples of K-12 students. Results of logistic regression and receiver…
Descriptors: Data Analysis, Decision Making, Data Use, Test Validity
Brittany N. Zakszeski; Heather E. Ormiston; Tyler L. Renshaw; Mei-Ki Chan; Daniel Osgood – School Mental Health, 2025
To inform the use of universal social, emotional, and behavioral (SEB) screening in secondary schools, we examined the functioning of the Social, Academic, and Emotional Behavior Risk Screener--Student Rating Scale (mySAEBRS) across three occasions (fall, winter, and spring) in a sample of secondary students (Grades 6-12). With consideration for…
Descriptors: Student Characteristics, Classification, Social Emotional Learning, Secondary School Students
