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IBM SPSS Amos - Structural Equation Modeling

( Duration: 2 Days )

Introduction to Structural Equation Modeling Using IBM SPSS Amos training course guides you through the fundamentals of using IBM SPSS Amos for the typical data analysis process. You will learn the basics of Structural Equation Modeling, drawing Diagrams in Amos Graphics, performing regression and confirmatory factor analysis in Amos, evaluating model fit, and ways to improve model fit.

  • Experience with Linear Regression and Factor Analysis
  • Experience using with IBM SPSS Amos is not necessary, though basic familiarity with Structural Equation Modeling would be helpful.

The Structural Equation Modeling Using IBM SPSS Amos workshop is ideal for:

  • Analysts with familiarity with Structural Equation Modeling
  • Anyone with little or no experience in using IBM SPSS Amos

COURSE AGENDA

1

Introduction to Structural Equation Modeling

  • Some Examples of SEM Models
  • Terminology in SEM
2

Drawing Diagrams in Amos Graphics

  • Launching Amos Graphics
  • Drawing the Diagram
3

Regression Analysis in Amos

  • Setting up a Regression in Amos
  • Requesting a Linear Regression
  • Regression Output
4

Testing Model Adequacy

  • Implied versus Sample Moments
  • Requesting Implied and Sample Moments
  • Constraining the Regression Weight to Zero
  • Testing a Hypothesis with the Chi-Square Test
  • Displaying the Chi-Square Test in the Diagram
  • Degrees of Freedom
  • Verifying the Degrees of Freedom
  • Model Identification
5

Additional Fit Measures in Amos

  • Alternative FIT Measures
6

Confirmatory Factor Analysis in Amos

  • Latent vs. Observed Variables
  • Exploratory vs. Confirmatory Factor Analysis
  • Estimating and Identifying a Latent Model in CFA
  • Requesting a Confirmatory Factor Analysis
7

The General Model

  • Requesting the General Model
8

Analyzing Data With Missing Values in Amos

  • Demonstration: How to Use the Full Information Maximum Likelihood Method to Handle Missing Values
  • Estimating Means and Intercepts
  • Imputing Missing Data
  • Analyzing the Imputed Data Files
9

Improving the Fit of a Model

  • Correcting the Model
  • Modification Index
  • Demonstrating How to Use Modification Indices
  • Trimming a Model for Better Fit
  • Using Modification Indices with Missing Data
10

Getting the Best Model with Specification Search

  • Exploratory Factor Analysis
  • Performing a Specification Search

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Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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