Ullman Jodie B
Department of Psychology, California State University, San Bernardino, CA 92407, USA.
J Pers Assess. 2006 Aug;87(1):35-50. doi: 10.1207/s15327752jpa8701_03.
This tutorial begins with an overview of structural equation modeling (SEM) that includes the purpose and goals of the statistical analysis as well as terminology unique to this technique. I will focus on confirmatory factor analysis (CFA), a special type of SEM. After a general introduction, CFA is differentiated from exploratory factor analysis (EFA), and the advantages of CFA techniques are discussed. Following a brief overview, the process of modeling will be discussed and illustrated with an example using data from a HIV risk behavior evaluation of homeless adults (Stein & Nyamathi, 2000). Techniques for analysis of nonnormally distributed data as well as strategies for model modification are shown. The empirical example examines the structure of drug and alcohol use problem scales. Although these scales are not specific personality constructs, the concepts illustrated in this article directly correspond to those found when analyzing personality scales and inventories. Computer program syntax and output for the empirical example from a popular SEM program (EQS 6.1; Bentler, 2001) are included.
本教程首先概述结构方程模型（SEM），包括统计分析的目的和目标以及该技术特有的术语。我将重点介绍验证性因子分析（CFA），这是SEM的一种特殊类型。在进行一般介绍后，将CFA与探索性因子分析（EFA）进行区分，并讨论CFA技术的优势。在简要概述之后，将讨论建模过程，并通过一个使用无家可归成年人艾滋病毒风险行为评估数据的示例进行说明（斯坦因和尼亚马蒂，2000年）。展示了非正态分布数据分析技术以及模型修正策略。实证示例检验了药物和酒精使用问题量表的结构。虽然这些量表不是特定的人格结构，但本文中阐述的概念与分析人格量表和问卷时所发现的概念直接对应。还包括来自一个流行的SEM程序（EQS 6.1；本特勒，2001年）的实证示例的计算机程序语法和输出。
