Download presentation

Presentation is loading. Please wait.

1
**Confirmatory Factor Analysis**

Intro

2
**Factor Analysis Exploratory Confirmatory Principle components**

Rotations Confirmatory Split sample Structural equations

3
**Structural Equation Approach**

Structural equation or covariance structure models

4
**Components Latent variables (endogenous)**

Manifest variables (exogenous) Residual variables Covariances Influences

5
**Path Diagrams (components)**

Observed Variable E Residual or Error Latent Variable Influence Path Covariance between exogenous variables or errors

6
**Path Diagram for Multiple Regression y = a0 + a1. x1 +a2. x2 + a3**

Path Diagram for Multiple Regression y = a0 + a1*x1 +a2*x2 + a3*x3 + a4*x4 + e1 X1 X2 Y E1 X3 X4

7
**Regression All variables are manifest One error term**

All covariances allowed among independent variables

8
**Two Factor Confirmatory Path Model**

V1 V2 V3 V4 V5 V6 E1 E1 E1 E1 E1 E1

9
**Confirmatory Model F1 and F2 correlated (oblique)**

Components of F1 and F2 are separate indicator variables

10
**Example Y = v + e1 X = u + e2 X’ = u + e3 X, Y & X’ are manifest**

U, V are latent e1, e2, e3 are residual/errors e1, e2, e3 independent with mean = 0 e2, e3, u uncorrelated e1, v uncorrelated

11
**Example Covariance Y X X’ Var(Y)= Var(v) + Var(e1) Cov(XY) = Cov(uv)**

Var(X) = Var(u) + Var(e2) Cov(X’Y) = Cov(X/X) = Var(u) Var(X’) = Var(u) + Var(e3)

12
**FACTOR Model Specification**

You can specify the FACTOR statement to compute factor loadings F and unique variances U of an exploratory or confirmatory first-order factor (or component) analysis. By default, the factor correlation matrix P is an identity matrix. C = FF’ + U, U = diag C= data covariance matrix

13
**First-order Confirmatory Factor Analysis**

For a first-order confirmatory factor analysis, you can use MATRIX statements to define elements in the matrices F, P, and U of the more general model C = FPF' + U, P = P' , U = diag factor loadings F unique variances U factor correlation matrix P data covariance matrix C

14
**PROC FACTOR RESIDUALS / RES**

displays the residual correlation matrix and the associated partial correlation matrix. The diagonal elements of the residual correlation matrix are the unique variances.

Similar presentations

OK

Multivariate Data Analysis Chapter 4 – Multiple Regression.

Multivariate Data Analysis Chapter 4 – Multiple Regression.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on air pollution control act Ppt on south african cultures Ppt on msme in india Ppt on marie curie's accomplishments Ppt online examination project Ppt on maths class 10 circle Ppt on spiritual leadership qualities Ppt on topic search engine Ppt on types of business organisations Ppt on refraction and reflection of light