2. SEM: Basic Concepts
⢠Measured Variable or Indicator Variable
⢠Latent Variable
⢠Measurement Model
⢠Structural Model
3. Basic Concepts: Measured Variable/Indicator
⢠Measured variable(s) are the variables that are actually measured in the
study.
Latent Variable
Measured Variable 1
Measured Variable 2
Measured Variable 3
4. Basic Concepts: Latent Variable
⢠Intangible constructs that are measured by a variety of indicators
(more is better!)
Latent Variable
Measured Variable 1
Measured Variable 2
Measured Variable 3
5. Basic Concepts: Measurement Model
⢠The measurement model can be described as follows. It shows the
relationship between a latent variable and its measured
items(variables).
Latent Variable
Measured Variable 1
Measured Variable 2
Measured Variable 3
6. Basic Concepts: Structural Models
⢠Often used to specify models in SEM
ď§ Causal flow is from left to right; top to bottom
⢠Straight arrows represent direct effects
⢠Curved arrows represent bidirectional âcorrelationalâ
relationships
⢠Ellipses represent latent variables
⢠Boxes/rectangles represent observed variables
8. Variants of Structural Equation Modelling
⢠Confirmatory Factor Analysis (CFA)
⢠Path Analysis with observed variables
⢠Path analysis with latent variables
9. Confirmatory Factor Analysis
âMeasurement Modelâ
⢠Tests model that specifies relationships between variables (items) and
factors
ď§ And relationships among factors
⢠Confirmatory
ď§ Because model is specified a priori
11. Confirmatory vs. Exploratory Factor
Analysis
⢠In CFA the model is specified a priori
ď§ Based on theory
⢠EFA is not a member of the SEM family
ď§ Includes a class of procedures involving centroids, principal components, and
principal axis factor analysis
ď§ Does not require a priori hypothesis about relationships within your model
ď§ Inductive vs. deductive approach
ď§ More restrictions on the relationships between indicators and latent factors
13. Observed Variable Path Analysis (OVPA)
⢠Tests only a structural model
ď§ Relationships among constructs represented by direct measured
(observed variables)
ď§ i.e., each âboxâ in model is an idem, subscale, or scale
⢠Analogous to a series of multiple regressions
ď§ But, with MR, we would need k different analyses, where k is # of
DVs
ď§ With SEM, can test entire model at once
15. Latent Variable Path Analysis (LVPA)
⢠Simultaneous test of measurement and structural parameters
⢠CFA and OVPA at same time
⢠LVPA models incorporateâŚ.
⢠Relationships between observed and latent variables (i.e., measures and factors)
⢠Relationships between latent variables
⢠Error & disturbances/residuals
17. Data Considerations
Sample Size
⢠SEM is a large-sample technique
⢠The required Sample size needed depends onâŚ.
ďComplexity of model
ď§ Ratios of sample size to estimated parameters ranging from
5:1 to 20:1 (Bentler & Chou, 1987; Kline, 2005)
ďData Quality
ď§ Larger samples for non-normal data
18. Looking for Online SEM
Training?
Contact us: info@costarch.com
Visit: http://tinyurl.com/costarch-sem
www.costarch.com