The purpose of this study was to support the development and initial validation of the Intervention Selection Profile (ISP)-Skills, a brief 14-item teacher rating scale intended to inform the selection and delivery of instructional interventions at Tier 2. Teacher participants (n = 196) rated five students from their classroom across four measures (total student n = 877). These measures included the ISP-Skills and three criterion tools: Social Skills Improvement System (SSIS), Devereux Student Strengths Assessment (DESSA), and Academic Competence Evaluation Scales (ACES). Diagnostic classification modeling (DCM) suggested an expert-created Q-matrix, which specified relations between ISP-Skills items and hypothesized latent attributes, provided good fit to item data. DCM also indicated ISP-Skills items functioned as intended, with the magnitude of item ratings corresponding to the model-implied probability of attribute mastery. DCM was then used to generate skill profiles for each student, which included scores representing the probability of students mastering each of eight skills. Correlational analyses revealed large convergent relations between ISP-Skills probability scores and theoretically-aligned subscales from the criterion measures. Discriminant validity was not supported, as ISP-Skills scores were also highly related to all other criterion subscales. Receiver operating characteristic (ROC) curve analyses informed the selection of cut scores from each ISP-Skills scale. Review of classification accuracy statistics associated with these cut scores (e.g., sensitivity and specificity) suggested they reliably differentiated students with below average, average, and above average skills. Implications for practice and directions for future research are discussed, including those related to the examination of ISP-Skills treatment utility.

中文翻译：

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干预技能概况 - 技能的开发和验证：对学生社交情感和学术能力技能的简要衡量。

本研究的目的是支持干预选择概况 (ISP)-技能的开发和初步验证，这是一个简短的 14 项教师评定量表，旨在为 Tier 2 教学干预的选择和提供提供信息。 教师参与者 (n = 196) 对来自他们班级的五名学生进行了四项评估（学生总数 = 877）。这些措施包括 ISP 技能和三个标准工具：社会技能改进系统 (SSIS)、德弗罗学生优势评估 (DESSA) 和学术能力评估量表 (ACES)。诊断分类建模 (DCM) 建议使用专家创建的 Q 矩阵，该矩阵指定了 ISP-Skills 项目和假设的潜在属性之间的关系，为项目数据提供了良好的拟合。DCM 还指出 ISP-Skills 项目按预期运行，项目评分的大小对应于模型隐含的属性掌握概率。然后使用 DCM 为每个学生生成技能档案，其中包括代表学生掌握八项技能中每一项的概率的分数。相关性分析揭示了 ISP-Skills 概率分数与来自标准度量的理论对齐子量表之间的大收敛关系。不支持区分效度，因为 ISP 技能分数也与所有其他标准子量表高度相关。接收者操作特征 (ROC) 曲线分析告知从每个 ISP 技能量表中选择切分分数。审查与这些分数相关的分类准确性统计数据（例如，敏感性和特异性）表明，它们可靠地区分了低于平均水平、平均水平、和高于平均水平的技能。讨论了对未来研究的实践和方向的影响，包括与检查 ISP-Skills 治疗效用相关的那些。