Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching
Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching
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DOI：
10.1080/10705510903008220
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发表时间：
2009-01-01
影响因子：
6
通讯作者：
Trautwein, Ulrich
中科院分区：
文献类型：
--
作者：
Marsh, Herbert W.;Muthen, Bengt;Trautwein, Ulrich
本研究是一种方法 - 实质的协同作用，展示了探索性结构方程建模（ESEM）方法的强大功能和灵活性，该方法整合了验证性和探索性因子分析（CFA和EFA），并应用于基于学生对大学教学（SETs）的多维评价的具有实质重要性的问题。对于这些数据，存在一种完善的ESEM结构，但典型的CFA模型并不适合这些数据，并且极大地夸大了九个SET因子之间的相关性（ESEM的中位数相关系数rs = 0.34，CFA为0.72），这种方式破坏了判别效度以及作为诊断反馈的有用性。提出了一种ESEM测量不变性的13种模型分类法，显示了基于在13年期间的前半段和后半段收集的SETs在多个组上的完全不变性（因子载荷、因子相关性、项目唯一性、项目截距、潜在均值）。将测量误差与共同性区分开的完全潜在ESEM增长模型在这13年期间几乎没有显示出线性或二次效应。潜在多指标多原因模型表明，与背景变量（工作量/难度、班级规模、先前学科兴趣、预期成绩）的关系规模较小，并且对于不同的ESEM SET因子有系统的变化，这支持了它们的判别效度以及对这些关系的结构效度解释。展示了一种高阶ESEM的新方法，但它并不完全适用于这些数据。基于ESEM方法，解决了一些用传统CFA方法无法恰当解决的具有实质重要性的问题。
This study is a methodological-substantive synergy, demonstrating the power and flexibility of exploratory structural equation modeling (ESEM) methods that integrate confirmatory and exploratory factor analyses (CFA and EFA), as applied to substantively important questions based on multidimentional students' evaluations of university teaching (SETs). For these data, there is a well established ESEM structure but typical CFA models do not fit the data and substantially inflate correlations among the nine SET factors (median rs = .34 for ESEM, .72 for CFA) in a way that undermines discriminant validity and usefulness as diagnostic feedback. A 13-model taxonomy of ESEM measurement invariance is proposed, showing complete invariance (factor loadings, factor correlations, item uniquenesses, item intercepts, latent means) over multiple groups based on the SETs collected in the first and second halves of a 13-year period. Fully latent ESEM growth models that unconfounded measurement error from communality showed almost no linear or quadratic effects over this 13-year period. Latent multiple indicators multiple causes models showed that relations with background variables (workload/difficulty, class size, prior subject interest, expected grades) were small in size and varied systematically for different ESEM SET factors, supporting their discriminant validity and a construct validity interpretation of the relations. A new approach to higher order ESEM was demonstrated, but was not fully appropriate for these data. Based on ESEM methodology, substantively important questions were addressed that could not be appropriately addressed with a traditional CFA approach.