IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced

Course Curriculum

Chapter 1: Introduction & Installation of Software

Lecture 1: Introduction

Lecture 2: Downloading and Installing AMOS 24 (Free 14 Day Trial Version)

Lecture 3: How to get answer to your queries fast?

Chapter 2: Practice Datasets, References and Resources

Lecture 1: Well-being Dataset

Lecture 2: Guide for downloading well-being data

Lecture 3: Personality Data set

Lecture 4: Link to Dropbox Folder Containing All Practice Datasets and Resources

Lecture 5: References for Further Study

Chapter 3: Getting Familiar with AMOS Interface

Lecture 1: Opening AMOS

Lecture 2: Developing Familiarity with Top Menus

Lecture 3: Getting Familiar with AMOS Graphics Tools

Lecture 4: Understanding Input and Output Values on Path Diagram

Lecture 5: Understanding "Group Number" Box

Lecture 6: Understanding "Default Model" Box

Lecture 7: Understanding Unstandardized and Standardized Estimates

Lecture 8: Understanding "Computation Summary" and "Files in Current Directory" Boxes

Lecture 9: Understanding "Path Model Canvas" and "Output" Tab

Lecture 10: Understanding Bottom Tabs: "Path Diagram" & "Tables"

Chapter 4: Meanings & Definitions: Getting Familiar with Terminology of SEM

Lecture 1: Getting familiar with Terminology Used for Variables in Model

Lecture 2: What is Structural Equation Modelling (SEM)?

Lecture 3: What is an Exogeneous Variable?

Lecture 4: What are Observed and Unobserved Variables?

Lecture 5: What are Residual Variables?

Lecture 6: An Example: A structural Model of Managerial Innovation Process

Lecture 7: What is Meaning of "Factor Loading"?

Chapter 5: Using AMOS Graphic Tools to Build a Structural Model

Lecture 1: Drawing and Naming Observed Variables

Lecture 2: Drawing Observed Variables and Error Terms

Lecture 3: Using Drag and Touch-up Tools

Lecture 4: Understanding Constrained Values on Error Terms

Lecture 5: Using "Draw Paths" Tool

Lecture 6: "Draw a Latent Variable" Tool

Lecture 7: Using "Rotate" Tool

Lecture 8: Using "Erase Object" Tool

Lecture 9: Using Three Types of "Select Object" Tool

Chapter 6: Understanding "Analyse Properties" Tab in AMOS

Lecture 1: What is meaning of Good Model Fit?

Lecture 2: Meaning of Indicator & Factor Variances and Co-variances

Lecture 3: When to Use Maximum Likelihood (ML) Method?

Lecture 4: When to Use Asymptotic Distribution Free (ADF) Method?

Lecture 5: What is Maximum Likelihood Method?

Lecture 6: Assumptions of Maximum Likelihood Method?

Lecture 7: Other Model Discrepancy Calculation Methods: GLS, ULS, SLS & ADF

Lecture 8: "Estimate Means and Intercepts": Dealing with Missing Data

Lecture 9: Understanding "Emulisrel 6" Option

Lecture 10: Understanding "Chicorrect" and Leaarning to Constrain Values

Lecture 11: Understanding "Fit Saturated and Independence Models" Option

Chapter 7: Issues in Structural Equation Modelling (SEM) Using AMOS

Lecture 1: How Large Should be Sample Size in SEM?

Lecture 2: Can I Use AMOS if My Data is Non-Normal?

Lecture 3: Can I use AMOS if My Variables are Non-continuous?

Lecture 4: Regression Vs. SEM & Adding More Variables to Model

Chapter 8: Exploratory Factor Analysis (EFA): A Precursor to CFA using AMOS

Lecture 1: What is Exploratory Factor Analysis (EFA)?

Lecture 2: Understanding Latent Variables and Indicators in FA

Lecture 3: Sample Researches Using FA in Social Science & Engineering

Lecture 4: Historical Origin of FA & Its Application in Test Construction

Lecture 5: Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)

Lecture 6: Setting Data for Factor Analysis

Lecture 7: Understanding "Selection Variable"

Lecture 8: Univariate Descriptives & Initial Solutions: Descriptive

Lecture 9: Understanding Inverse, Reproduced, Anti-Image

Lecture 10: Extraction Method: Principle Component Analysis

Lecture 11: Extraction Method: Principle Axis Factoring

Lecture 12: Extraction Method: Maximum Likelihood Estimation

Lecture 13: Choosing Correlation vs. Covariance Matrix for Factor Analysis

Lecture 14: Interpreting Correlation Matrix & Unrotated Factor Solution

Lecture 15: Determining number of factors: Scree Plot vs. Kaiser's eigen value criteria

Lecture 16: Factor Rotation: What it is and why its done?

Lecture 17: Rotation Methods: Varimax, Quartimax, Equamax, Direct Oblimin, Promax

Lecture 18: Calculating Factor Scores: Regression, Bartlett, Anderson-Rubin

Lecture 19: Factor Score Coefficient Matrix

Lecture 20: Missing Value Analysis: Listwise, Pairwise, Replace with Mean

Lecture 21: Sort by Size & Suppressing Smaller Coefficients

Lecture 22: Project in Factor Analysis Part 1: Identifying Dimensions of Personality

Lecture 23: Project in Factor Analysis Part 2: Identifying Dimensions of Personality

Lecture 24: Project in Factor Analysis Part 3: Identifying Dimensions of Personality

Lecture 25: Project in Factor Analysis Part 4: Factor Naming

Lecture 26: Project in Factor Analysis Part 5: Reliability Analysis of Factors

Lecture 27: Project in Factor Analysis Part 6: Presenting Results in APA Style

Chapter 9: Scale Validation in AMOS

Lecture 1: Importing EFA model in AMOS

Lecture 2: Reliability and Validity: Two Sides of Model Quality

Lecture 3: Understanding Reliability and Validity

Lecture 4: What is Validity?

Lecture 5: Type of Construct Validity: Convergent Validity

Lecture 6: Statistical Criteria for Convergent Validity in AMOS

Lecture 7: What is Average Variance Extracted (AVE) & Why AVE More than .5 is Required?

Lecture 8: Understanding Formula for AVE Calculation

Lecture 9: Manual Calculation of AVE using Excel

Lecture 10: What is Maximum Shared squared Variance (MSV)?

Lecture 11: Why MSV Should be Less Than AVE for Discriminant Validity?

Lecture 12: Manual Calculation of MSV?

Lecture 13: What is Average Shared squared Variance (ASV)?

Lecture 14: Why ASV should be less than AVE for Discriminant Validity?

Lecture 15: Manual Calculation of ASV?