Gross Thomas J, Duncan Jenna, Kim Samuel Y, Mason W Alex, Haggerty Kevin P
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.
Contemp Sch Psychol. 2019 Sep;23(3):270-289. doi: 10.1007/s40688-018-00215-y. Epub 2018 Nov 3.
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.
(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。