Structural equation models in medical research.
Structural equation models in medical research.
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DOI：
10.1177/096228029200100203
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发表时间：
1992-01-01
影响因子：
2.3
通讯作者：
Stein, J A
中科院分区：
文献类型：
--
作者：
Bentler, P M;Stein, J A
结构方程模型（SEM）是一种现代统计方法，它允许人们对一组相互关联的非实验数据进行因果假设。在对模型参数进行最佳估计后，将样本方差和协方差以及可能的平均值与基于理论的假设模型预测的值进行比较。对经验数据与假设模型的拟合优度进行了统计评价。本文综述了结构方程模型的基本统计理论和基本原理。验证性因素分析和潜变量路径模型进行了讨论。注意到结构方程模型在信度和效度评估中的适用性。提供了一个详细的例子，并简要回顾了几个例子，从医学文献。关于可能的误用或误解的技术的警告也被提到。扫描电镜在医学研究中的使用的未来可能的方向提出了建议。两个附录提供了更多的技术细节。
Structural equation modelling (SEM) is a modern statistical method that allows one to evaluate causal hypotheses on a set of intercorrelated nonexperimental data. The sample variances and covariances, and possibly the means, are compared to those predicted by a theory-based hypothetical model after optimal estimation of the parameters of the model. The goodness-of-fit of the empirical data to the hypothesized model is evaluated statistically. This review describes the underlying statistical theory and rationale of SEM. Both confirmatory factor analysis and latent variable path models are discussed. The applicability of SEM to assessment of reliability and validity is noted. A detailed example is provided, and several examples from the medical literature are briefly reviewed. Cautions regarding the possible misuse or misinterpretation of the technique are also mentioned. Possible future directions for the use of SEM in medical research are suggested. Two appendices provide more technical details.