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Also known as: SEM
A multivariate statistical technique modeling relationships among observed and latent variables within measurement and structural components.
DESCRIPTION
Structural Equation Modeling combines factor analysis and path analysis to model complex relationships, including latent constructs measured by indicators. It supports hypothesis testing of causal structures and evaluates model fit using indices like CFI, RMSEA, and SRMR. SEM is widely used in social sciences, marketing, psychometrics, and behavioral research. Estimation methods include maximum likelihood, robust variants, and Bayesian approaches. Proper model specification, identification, and validation are critical to draw reliable conclusions.
ALTERNATIVE DEFINITIONS
Wikipedia
Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze structural relationships.
Defines SEM broadly and emphasizes analysis of structural relationships.
UCLA Statistical Consulting
Structural equation modeling (SEM) is a general statistical modeling technique used to study relationships among variables, some measured directly and others latent.
Highlights measured versus latent variables within a general modeling framework.
Penn State Statistics
SEM is a class of models that seeks to explain relationships among multiple variables by accounting for measurement error and latent constructs.
Emphasizes measurement error and latent construct modeling.
RELATED TERMS
Latent Variables Confirmatory Factor Analysis Measurement Model Model Fit Maximum Likelihood Estimation Covariance Structure Analysis Endogeneity Causal Modeling
COMPETING TERMS
Regression Analysis Path Analysis
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist Data Analyst Information Analyst Business Analyst Data Strategy Consultant
EXTERNAL SOURCES
- Wikipedia - Structural equation modeling
- UCLA Statistical Consulting - Introduction seminar on SEM
- lavaan - R SEM package documentation
- Mplus - Mplus SEM resources
TOP QUESTIONS
What does Structural Equation Modeling combine?
SEM integrates measurement models (confirmatory factor analysis) with structural models (path analysis) to test relationships among observed and latent variables.
Category: Overview
Why is Structural Equation Modeling valuable?
It explicitly models latent constructs and measurement error, improving validity of causal inferences in complex systems.
Category: Relevancy
How is Structural Equation Modeling validated?
Researchers assess fit using indices like CFI, RMSEA, SRMR, and review residuals and modification indices.
Category: Requirements
What are common pitfalls in Structural Equation Modeling?
Poor identification, overfitting with modification indices, and ignoring theory can produce misleading models.
Category: Mistakes
What estimation methods are used in Structural Equation Modeling?
Maximum likelihood, robust ML, weighted least squares, and Bayesian methods are common depending on data and assumptions.
Category: Example
What is the risk of mis-specified Structural Equation Modeling?
Mis-specification can yield biased parameter estimates and invalid conclusions, undermining decisions based on the model.
Category: Risk
What is an alternative to Structural Equation Modeling?
Regression Analysis is simpler but lacks explicit latent variable modeling and measurement error handling.
Category: Alternatives
When was Structural Equation Modeling introduced?
SEM emerged in the mid-20th century, evolving from path analysis and factor analysis traditions.
Category: Origin
Where to learn Structural Equation Modeling?
University tutorials (UCLA, Penn State), lavaan documentation, and textbooks provide practical guidance and examples.
Category: GettingStarted