Upcoming SlideShare
×

# Structural Equation Modelling (SEM) Part 1

2,591 views

Published on

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

3 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total views
2,591
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
223
0
Likes
3
Embeds 0
No embeds

No notes for slide

### Structural Equation Modelling (SEM) Part 1

1. 1. Structural Equation Modelling (SEM) An Introduction (Part 1)
2. 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. 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. 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. 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. 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. 7. Looking for Online SEM Training? Contact us: info@costarch.com Visit: http://tinyurl.com/costarch-sem www.costarch.com