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Salient Predictors of School Dropout among Secondary Students with Learning Disabilities
Author(s) -
Doren Bonnie,
Murray Christopher,
Gau Jeff M.
Publication year - 2014
Publication title -
learning disabilities research and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.018
H-Index - 21
eISSN - 1540-5826
pISSN - 0938-8982
DOI - 10.1111/ldrp.12044
Subject(s) - psychology , dropout (neural networks) , learning disability , at risk students , salient , set (abstract data type) , school dropout , intervention (counseling) , developmental psychology , population , sample (material) , mathematics education , demography , chemistry , chromatography , machine learning , artificial intelligence , socioeconomics , sociology , psychiatry , computer science , programming language
The purpose of this study was to identify the unique contributions of a comprehensive set of predictors and the most salient predictors of school dropout among a nationally representative sample of students with learning disabilities (LD). A comprehensive set of theoretically and empirically relevant factors was selected for examination. Analyses were conducted to explore the unique contribution and relative importance of these factors in predicting dropout. Results indicated that the most salient predictors of school dropout included a set of malleable individual (grades, and engagement in high‐risk behaviors), family (parent expectations), and school (quality of students’ relationship with teachers and peers) factors. The findings validate multicomponent dropout prevention and intervention models for this population while at the same time illuminating specific key components that appear to be of particular importance in school dropout among students with LD.