This presentation is an introduction to the concept and theory of Structural Equation Modelling.

1. Structural Equation Modelling
(SEM)
An Introduction (Part 1)

2. What is Structural Equation Modelling?
• SEM is a general statistical modelling technique used to establish relationship among
variables.
• SEM is a confirmatory data analysis technique, i.e.
 it tests models that are conceptually derived, beforehand
 it tests if the theory fits the data
• SEM can be thought of as a combination of factor analysis and multiple regression
 it can simultaneously test measurement and structural relationships
• SEM is a family of related procedures. It is alternately defined by the following terms
 Path Analysis, Path Modelling, Causal Modelling, Analysis of Covariance Structures, Latent
Variable Analysis, Linear Structural Relations

3. Covariance: At the Heart of SEM
• Covariance is a measure of how much two random variables change
together. Alternately, it can be defined as the strength of association
between the two variables and their variabilities.
𝑛
𝑖=1(𝑋 𝑖 −
𝑋)(𝑌 𝑖 − 𝑌)
𝑁−1
OR
𝑐𝑜𝑣 𝑥𝑦 = 𝑟 𝑥𝑦 𝑆𝐷 𝑥 𝑆𝐷 𝑦
• The basic statistic of SEM
 Understanding patterns of correlations among a set of variables
 Explain as much of their variance as possible with the model specified

4. Logic of SEM
• Every theory (model) implies a set of correlations
 And why variables are correlated
• Necessary (but insufficient) condition for the validity of the theory is
that it should be able to reproduce the correlations that are actually
observed
 i.e., the implied covariance matrix should = the actual covariance
matrix

5. Why SEM over Regression?
• Regression allows for only a single dependent variable,
whereas SEM allows for multiple dependent variables.
• SEM allows for variables to correlate, whereas regression
adjusts for other variables in the model.
• Regression assumes perfect measurement, whereas SEM
accounts for measurement error.

6. USES OF SEM
• Theory testing
 Strength of prediction/association in models with multiple DVs
 Model fit
• Mediation/tests of indirect effects
• Group differences
 Multiple-sample analysis
• Longitudinal models
• Multilevel nested models

7. Looking for Online SEM
Training?
Contact us: info@costarch.com
Visit: http://tinyurl.com/costarch-sem
www.costarch.com