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中小学学术和行为风险的共同出现:对普遍筛查实践的影响。

Co-occurrence of academic and behavioral risk within elementary schools: Implications for universal screening practices.

机构信息

Department of Educational Psychology, University of Wisconsin-Madison.

Department of Educational, School, and Counseling Psychology, University of Missouri.

出版信息

Sch Psychol. 2019 May;34(3):261-270. doi: 10.1037/spq0000314. Epub 2019 Feb 28.

Abstract

The purposes of this study were twofold. The first was to use latent class analysis to identify groupings of students defined by the presence or absence of academic or behavioral risk. The second was to determine whether these groups differed across various dichotomous academic and behavioral outcomes (e.g., suspensions, office discipline referrals, statewide achievement test failure). Students (N = 1,488) were sampled from Grades 3-5. All students were screened for academic risk using AIMSweb Reading Curriculum-Based Measure and AIMSweb Mathematics Computation, and behavioral risk using the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS). Latent class analyses supported the fit of a three-class model, with resulting student classes defined as low-risk academic and behavior (Class 1), at-risk academic and high-risk behavior (Class 2), and at-risk math and behavior (Class 3). Logistic regression analyses indicated the classes demonstrated statistically significant differences statewide achievement scores, as well as suspensions. Further analysis indicated that the odds of all considered negative outcomes were higher for both groups characterized by risk (i.e., Classes 2 and 3). Negative outcomes were particularly likely for Class 2, with the odds of negative behavioral and academic outcomes being 6-15 and 112-169 times more likely, respectively. Results were taken to support an integrated approach to universal screening in schools, defined by the evaluation of both academic and behavioral risk. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

摘要

本研究旨在实现两个目的。一是使用潜在类别分析,根据学生学业或行为风险的有无,确定学生的分组。二是确定这些分组在各种二分学术和行为结果(例如,停学、校内纪律处分、全州成就测试失败)上是否存在差异。从 3 至 5 年级抽取了 1488 名学生作为样本。所有学生都使用 AIMSweb 阅读课程基础测量和 AIMSweb 数学计算进行学业风险筛查,使用社会、学术和情感行为风险筛查器(SAEBR)进行行为风险筛查。潜在类别分析支持三类别模型的拟合,结果将学生群体定义为低学业和行为风险(1 类)、高学业和高行为风险(2 类)和高数学和行为风险(3 类)。逻辑回归分析表明,这些班级在全州的学业成绩以及停学方面存在统计学上的显著差异。进一步的分析表明,两类风险(即 2 类和 3 类)学生的所有考虑到的负面结果的可能性都更高。对于 2 类学生来说,负面行为和学术结果的可能性分别高出 6 至 15 倍和 112 至 169 倍。研究结果支持在学校中采用综合的普遍筛查方法,包括评估学业和行为风险。(APA,2019)

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