Introduction to Structural Equation Modeling
Introduction to Structural Equation Modeling (Path Analysis)
SGIM Precourse PA08 May 2005 Jeffrey L. Jackson, MD MPH Kent Dezee, MD MPH Kevin Douglas, MD William Shimeall, MD MPH Traditional multivariate modeling (linear regression, ANOVA, Poisson regression, logistic regression, proportional hazard modeling) is useful for examining direct relationships between independent and dependent variables. All share a common format:
Dependent Variable = Independent variable1 + Independent Variable2 + Independent Variable3
The net result of such modeling is to examine relationships in the following format Independent Variable1 Independent Variable2 Independent Variable3 Dependent Variable
However, real life may not be so parsimonious, relationships between various variables may be much more complex, more “web-like,” for example: Independent Variable6 Independent Variable1 Independent Variable2 Independent Variable3 Dependent Variable
Independent Variable4
Independent Variable5
In this example, Variables 1-3 and 6 have direct effects on the dependent variable, while variables 4 and 5 have indirect effects, mediated by effects on Variables 1, 2 and 3. Variable 6 1
has both direct and indirect effects on the dependent variable. This "web" of relationships could not be easily modeled with standard regression techniques. On the other hand, structural equation modeling (SEM) readily allows one to explore such complex interrelationships. Structural equation modeling emerged in the mid-late 1980's in the social sciences arena as a method of modeling complex relationships. There are several common types of structural equation models: 1) Path analysis, also known as causal modeling, focuses on examining the web of relationships among measured variables.
Functional Status Mental Disorders Symptom Severity Satisfaction
This is a typical example of a path analysis, an analysis in which one explores the web of relationships between the presence of mental
SGIM Precourse PA08 May 2005 Jeffrey L. Jackson, MD MPH Kent Dezee, MD MPH Kevin Douglas, MD William Shimeall, MD MPH Traditional multivariate modeling (linear regression, ANOVA, Poisson regression, logistic regression, proportional hazard modeling) is useful for examining direct relationships between independent and dependent variables. All share a common format:
Dependent Variable = Independent variable1 + Independent Variable2 + Independent Variable3
The net result of such modeling is to examine relationships in the following format Independent Variable1 Independent Variable2 Independent Variable3 Dependent Variable
However, real life may not be so parsimonious, relationships between various variables may be much more complex, more “web-like,” for example: Independent Variable6 Independent Variable1 Independent Variable2 Independent Variable3 Dependent Variable
Independent Variable4
Independent Variable5
In this example, Variables 1-3 and 6 have direct effects on the dependent variable, while variables 4 and 5 have indirect effects, mediated by effects on Variables 1, 2 and 3. Variable 6 1
has both direct and indirect effects on the dependent variable. This "web" of relationships could not be easily modeled with standard regression techniques. On the other hand, structural equation modeling (SEM) readily allows one to explore such complex interrelationships. Structural equation modeling emerged in the mid-late 1980's in the social sciences arena as a method of modeling complex relationships. There are several common types of structural equation models: 1) Path analysis, also known as causal modeling, focuses on examining the web of relationships among measured variables.
Functional Status Mental Disorders Symptom Severity Satisfaction
This is a typical example of a path analysis, an analysis in which one explores the web of relationships between the presence of mental