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Information for researchers and professionals about the Strengths and Difficulties Questionnaires.面向研究人员和专业人士的关于优势与困难问卷的信息。
Child Adolesc Ment Health. 2007 May;12(2):98. doi: 10.1111/j.1475-3588.2007.00443_2.x.
2
Psychometric defensibility of the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS) Teacher Rating Scale and multiple gating procedure within elementary and middle school samples.社会、学业和情绪行为风险筛查量表(SAEBRS)教师评定量表在小学和初中样本中的心理测量学可辩护性及多重筛选程序
J Sch Psychol. 2016 Oct;58:21-39. doi: 10.1016/j.jsp.2016.07.001. Epub 2016 Jul 19.
3
Randomized trial of parent training to prevent adolescent problem behaviors during the high school transition.预防高中过渡阶段青少年问题行为的家长培训随机试验。
J Fam Psychol. 2016 Dec;30(8):944-954. doi: 10.1037/fam0000204. Epub 2016 Aug 8.
4
Out-of-School Suspensions of Black Youths: Culture, Ability, Disability, Gender, and Perspective.黑人青少年的校外停学处分:文化、能力、残疾、性别与视角
Soc Work. 2016 Jul;61(3):235-43. doi: 10.1093/sw/sww021.
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Child and Parent Report of Parenting as Predictors of Substance Use and Suspensions from School.儿童与家长对养育方式的报告作为物质使用和学校停学情况的预测指标
J Early Adolesc. 2016 Jun;36(5):625-645. doi: 10.1177/0272431615574886. Epub 2015 Mar 10.
6
Developmental trajectories of clinically significant attention-deficit/hyperactivity disorder (ADHD) symptoms from grade 3 through 12 in a high-risk sample: Predictors and outcomes.高风险样本中三年级至十二年级临床显著注意力缺陷/多动障碍(ADHD)症状的发展轨迹:预测因素与结果
J Abnorm Psychol. 2016 Feb;125(2):207-219. doi: 10.1037/abn0000112.
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Competing Factor Models of Child and Adolescent Psychopathology.儿童和青少年精神病理学的竞争因素模型
J Abnorm Child Psychol. 2016 Nov;44(8):1559-1571. doi: 10.1007/s10802-016-0129-9.
8
An integrated approach to universal prevention: Independent and combined effects of PBIS and SEL on youths' mental health.一种普遍预防的综合方法:积极行为干预与支持(PBIS)和社会情感学习(SEL)对青少年心理健康的独立及综合影响。
Sch Psychol Q. 2015 Jun;30(2):166-183. doi: 10.1037/spq0000102. Epub 2015 Jan 19.
9
Emotional and behavioural resilience to multiple risk exposure in early life: the role of parenting.早期生活中多重风险暴露的情绪和行为适应力:养育的作用。
Eur Child Adolesc Psychiatry. 2015 Jul;24(7):745-55. doi: 10.1007/s00787-014-0619-7. Epub 2014 Oct 10.
10
Confirmation of models for interpretation and use of the Social and Academic Behavior Risk Screener (SABRS).用于解释和使用社会与学术行为风险筛查器(SABRS)的模型确认。
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使用优势与困难问卷从八年级到十年级预测学校停学风险。

Predicting School Suspension Risk from Eighth through Tenth Grade Using the Strengths and Difficulties Questionnaire.

作者信息

Gross Thomas J, Duncan Jenna, Kim Samuel Y, Mason W Alex, Haggerty Kevin P

机构信息

Western Kentucky University, Psychology Department, 3045 Gary A. Ransdell Hall, 1906 College Heights Blvd., #21030, Bowling Green, KY 42101-1030.

Lipscomb University, College of Education, One University Park Drive, Nashville, TN 37204.

出版信息

Contemp Sch Psychol. 2019 Sep;23(3):270-289. doi: 10.1007/s40688-018-00215-y. Epub 2018 Nov 3.

DOI:10.1007/s40688-018-00215-y
PMID:32775014
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7406192/
Abstract

The current study examined (1) if the Strengths and Difficulties Questionnaire (SDQ) would yield alternative factor structures related to either symptoms or strengths with early adolescent students when an exploratory factor analysis (EFA) is used; (2) which scales best predicted suspensions of typically developing early adolescents; and (3) what cut-off scores were useful for identifying youth at risk for suspensions. The current study included 321 parent-student dyads, who were followed from the middle of eighth grade until the end of tenth grade. A symptoms-based EFA yielded three factors: Misbehavior, Isolation, and Agitation. A strength-based EFA yielded three factors, as, well: Emotional, Social, and Moral competence. Logistic regression path analyses were used to predict risk of any suspension at the end of eighth, ninth, and tenth grades. The predictor variables were the original SDQ Conduct Problems and Hyperactivity scales in one model, the Misbehavior and Agitation scales in a second model, and the Emotional and Moral competence scales in the third model. Only the Misbehavior scale consistently predicted suspensions across each grade ( = .27, OR = 1.32, < .001; = .15, OR = 1.18, = .029; = .17, OR = 1.18, = .029, respectively). For the Misbehavior scale, cut-off scores were established that reflected the 75 and 90 percentile; however, each cut-off demonstrated strengths and weaknesses for identifying at-risk students. The expectation of screening to identify youth at-risk for suspensions, a complex school discipline decision, is discussed.

摘要

本研究考察了

(1)当使用探索性因素分析(EFA)时,优势与困难问卷(SDQ)是否会产生与青少年早期学生的症状或优势相关的替代因素结构;(2)哪些量表能最好地预测发育正常的青少年早期学生的停学情况;(3)哪些临界分数有助于识别有停学风险的青少年。本研究纳入了321对亲子,从八年级中期跟踪至十年级末。基于症状的探索性因素分析产生了三个因素:行为不端、孤立和激动。基于优势的探索性因素分析也产生了三个因素:情绪、社交和道德能力。使用逻辑回归路径分析来预测八年级、九年级和十年级末任何停学的风险。预测变量在一个模型中是原始的SDQ品行问题和多动量表,在第二个模型中是行为不端和激动量表,在第三个模型中是情绪和道德能力量表。只有行为不端量表在各年级都能持续预测停学情况(分别为β = 0.27,OR = 1.32,p < 0.001;β = 0.15,OR = 1.18,p = 0.029;β = 0.17,OR = 1.18,p = 0.029)。对于行为不端量表,确定了反映第75和第90百分位数的临界分数;然而,每个临界分数在识别有风险学生方面都有优点和缺点。文中讨论了通过筛查来识别有停学风险青少年的期望,这是一个复杂的学校纪律决定。