Structural Equation Models: A Review With Applications to Environmental Epidemiology

Structural equation models (SEMs) have been discussed extensively in the psychometrics and quantitative behavioral sciences literature. However, many statisticians and researchers in other areas of application are relatively unfamiliar with their implementation. Here we review some of the SEM litera...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the American Statistical Association 2005-12, Vol.100 (472), p.1443-1455
Main Authors: Sánchez, Brisa N, Budtz-Jørgensen, Esben, Ryan, Louise M, Hu, Howard
Format: Article
Language:English
Subjects:
Applications
Biological markers
Biology, psychology, social sciences
Blood
Bones
Cord blood
Correlated outcomes
Covariance
Data analysis
Environmental science
Epidemiology
Exact sciences and technology
General topics
Latent variable
Linear equations
Lisrel model
Mathematical foundations
Mathematical independent variables
Mathematical models
Mathematics
Measurement error
Measurement errors
Medical sciences
Methodology
Modeling
Multiple testing
Multivariate analysis
Multivariate data
Parametric models
Probability and statistics
Review Article
Sciences and techniques of general use
Statistical discrepancies
Statistical methods
Statistical models
Statistics
Structural equation models
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Structural equation models (SEMs) have been discussed extensively in the psychometrics and quantitative behavioral sciences literature. However, many statisticians and researchers in other areas of application are relatively unfamiliar with their implementation. Here we review some of the SEM literature and describe basic methods, using examples from environmental epidemiology. We make connections to recent work on latent variable models for multivariate outcomes and to measurement error methods, and discuss advantages and disadvantages of SEMs compared with traditional regressions. We give a detailed example in which two models fit the same data well, yet one is physiologically implausible. This underscores the critical role of subject matter knowledge in the successful implementation of SEMs. A brief discussion on open research areas is included.
ISSN:0162-1459
1537-274X
DOI:10.1198/016214505000001005