University of Missouri, United States.
University of Missouri, United States.
J Sch Psychol. 2018 Jun;68:129-141. doi: 10.1016/j.jsp.2018.03.002. Epub 2018 Mar 19.
In accordance with an argument-based approach to validation, the purpose of the current study was to yield evidence relating to Social, Academic, and Emotional Behavior Risk Screener (SAEBRS) score interpretation. Bifactor item response theory analyses were performed to examine SAEBRS item functioning. Structural equation modeling (SEM) was used to simultaneously evaluate intra- and inter-scale relationships, expressed through (a) a measurement model specifying a bifactor structure to SAEBRS items, and (b) a structural model specifying convergent and discriminant relations with an outcome measure (i.e., Behavioral and Emotional Screening System [BESS]). Finally, hierarchical omega coefficients were calculated in evaluating the model-based internal reliability of each SAEBRS scale. IRT analyses supported the adequate fit of the bifactor model, indicating items adequately discriminated moderate and high-risk students. SEM results further supported the fit of the latent bifactor measurement model, yielding superior fit relative to alternative models (i.e., unidimensional and correlated factors). SEM analyses also indicated the latent SAEBRS-Total Behavior factor was a statistically significant predictor of all BESS subscales, the SAEBRS-Academic Behavior predicted BESS Adaptive Skills subscales, and the SAEBRS-Emotional Behavior predicted the BESS Internalizing Problems subscale. Hierarchical omega coefficients indicated the SAEBRS-Total Behavior factor was associated with adequate reliability. In contrast, after accounting for the total scale, each of the SAEBRS subscales was associated with somewhat limited reliability, suggesting variability in these scores is largely driven by the Total Behavior scale. Implications for practice and future research are discussed.
根据基于论证的验证方法，本研究的目的是提供与社会、学术和情绪行为风险筛查器（SAEBRS）评分解释相关的证据。使用双因素项目反应理论分析来检查 SAEBRS 项目的功能。结构方程模型（SEM）用于同时评估内在和跨尺度关系，通过（a）一个测量模型来指定 SAEBRS 项目的双因素结构，以及（b）一个结构模型来指定与结果测量（即行为和情绪筛查系统[BESS]）的收敛和区分关系。最后，通过计算层次 ω 系数来评估每个 SAEBRS 量表的基于模型的内部可靠性。IRT 分析支持双因素模型的适当拟合，表明项目能够充分区分中度和高风险学生。SEM 结果进一步支持潜在双因素测量模型的拟合，与替代模型（即单维和相关因素）相比，具有更好的拟合度。SEM 分析还表明，潜在的 SAEBRS-总行为因素是 BESS 所有子量表的统计学上显著预测因子，SAEBRS-学术行为预测 BESS 适应技能子量表，而 SAEBRS-情绪行为预测 BESS 内化问题子量表。层次 ω 系数表明 SAEBRS-总行为因素与适当的可靠性相关。相比之下，在考虑到总量表后，SAEBRS 的每个子量表都与有限的可靠性相关，这表明这些分数的变化在很大程度上是由总行为量表驱动的。讨论了对实践和未来研究的影响。
