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What is Structural Equation Modeling?
What is Structural Equation Modeling?
A multivariate technique.
Combines factor analysis and multiple regression.
Examines the relationship between observed (manifest) variable and unobserved (latent) variable.
Examines the relationship between latent variables.
In short, SEM seeks to explain the relationship among multiple variable.
In short, SEM seeks to explain the relationship among multiple variable.
Modeling with SEM: Some Examples
Modeling with SEM: Some Examples
SEM with SmartPLS
SEM with SmartPLS
Modeling with AMOS
Modeling with AMOS
Initial Measurement Model
Initial Measurement Model
Modified Measurement Model
Modified Measurement Model
Structural Model
Structural Model
Why SEM?
Why SEM?
First vs Second Generation Techniques
First vs Second Generation Techniques
First Generation Technique
First Generation Technique
Can only analyze one layer of linkages between independent and dependent variables at once.
Examples: Linear regression, LOGIT, ANOVA, and MANOVA, etc.
Second Generation Technique
Second Generation Technique
Can analyze the relationships among multiple variables simultaneously.
This refers to Structural Equation Modeling (SEM).
Analytical Trend
Analytical Trend
In line with this extensive use of multi-item measures, most papers (96%) reported at least some degree of measurement analysis, most typically using SEM techniques.
Confirmatory factor analysis (CFA) results were reported for 86.6% of the studies, and exploratory factor analysis (EFA) results for 22.8%.
For papers that do not focuse on scale development, the use of both CFA and EFA is likely unnecessary. In general, use of CFA is preferred.
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