Structural equation models in medical research.
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.
Bentler
P M
PM
Department of Psychology, University of California, Los Angeles 90024-1563.
Stein
J A
JA
D005163:Factor Analysis, Statistical; D005544:Forecasting; D005821:Genetic Techniques; D016014:Linear Models; D015233:Models, Statistical; D012107:Research Design / Q000639:trends