University of Houston, Department of Educational Psychology, 491 Farish Hall, Houston, TX 77204-5029, United States.
J Sch Psychol. 2013 Dec;51(6):751-63. doi: 10.1016/j.jsp.2013.09.001. Epub 2013 Oct 4.
Several psychological assessment instruments are based on the assumption of a general construct that is composed of multiple interrelated domains. Standard confirmatory factor analysis is often not well suited for examining the factor structure of such scales. This study used data from 1885 elementary school students (mean age=8.77 years, SD=1.47 years) to examine the factor structure of the Behavioral Assessment System for Children, Second Edition (BASC-2) Behavioral and Emotional Screening System (BESS) Teacher Form that was designed to assess general risk for emotional/behavioral difficulty among children. The modeling sequence included the relatively new exploratory structural equation modeling (ESEM) approach and bifactor models in addition to more standard techniques. Findings revealed that the factor structure of the BASC-2 BESS Teacher Form is multidimensional. Both ESEM and bifactor models showed good fit to the data. Bifactor models were preferred on conceptual grounds. Findings illuminate the hypothesis-generating power of ESEM and suggest that it might not be optimal for instruments designed to assess a predominant general factor underlying the data.
有几种心理评估工具基于一个由多个相互关联的领域组成的通用结构假设。标准验证性因子分析通常不太适合检验此类量表的因子结构。本研究使用了来自 1885 名小学生（平均年龄=8.77 岁，SD=1.47 岁）的数据，检验了旨在评估儿童情绪/行为困难一般风险的《儿童行为评估系统第二版》（BASC-2）行为和情绪筛查系统（BESS）教师表的因子结构。模型序列包括相对较新的探索性结构方程建模（ESEM）方法和双因素模型，以及更标准的技术。研究结果表明，BASC-2 BESS 教师表的因子结构是多维的。ESEM 和双因素模型都对数据拟合良好。基于概念原因，双因素模型更受青睐。研究结果阐明了 ESEM 的假设生成能力，并表明对于旨在评估数据中主要通用因素的工具来说，它可能不是最优的。
