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Structural Equation Modelling Using AMOS: Foundation Course

Welcome to the IBM AMOS Foundation Course, a comprehensive course designed to help you master Structural Equation Modeling (SEM) using IBM AMOS. Whether you are a student, research...

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Foundation
Published 2026 · Last updated Jul 2026

Structural Equation Modelling Using AMOS: Foundation Course

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Course overview

Course Overview

Welcome to the IBM AMOS Foundation Course, a comprehensive course designed to help you master Structural Equation Modeling (SEM) using IBM AMOS. Whether you are a student, researcher, academic, or professional, this course will provide you with the knowledge and practical skills needed to build, validate, and test complex theoretical models with confidence.


Structural Equation Modeling has become one of the most widely used statistical techniques in academic and industry research. This course begins with the fundamentals of SEM and gradually progresses to advanced concepts, including measurement models, structural models, Confirmatory Factor Analysis (CFA), Exploratory Factor Analysis (EFA), model estimation, model fit assessment, hypothesis testing, and model interpretation. Through step-by-step demonstrations and practical examples using IBM AMOS, you will learn how to develop, evaluate, and report SEM models suitable for high-quality research and publication.


By the end of this course, you will have the confidence to perform Structural Equation Modeling using IBM AMOS and apply these techniques in research, dissertations, theses, and professional projects.

Learning Outcomes

Perform Structural Equation Modeling (SEM) using IBM AMOS.

Conduct Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Build, estimate, and validate measurement and structural models.

Assess model fit and interpret SEM results accurately.

Apply SEM techniques for academic research and high-impact publications.

What you walk away with

Career Map & Skills You Gain

Every course adds to three macro skills. Log in to swap platform averages for your own numbers.

Financial skills

62th

percentile

Your salary vs. learners in these roles

After this course+14 pts → 76th

Data Analysis and Research specialist

72% course score

+7

Project contributor

66% course score

+7

Independent practitioner

61% course score

+6

Employability skills

71th

percentile

Quiz scores from the courses you finish

After this course+18 pts → 89th

Perform Structural Equation Modeling (SEM) using IBM AMOS

64% course score

+6 pts

Conduct Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA)

70% course score

+7 pts

Build, estimate, and validate measurement and structural models

76% course score

+8 pts

Assess model fit and interpret SEM results accurately

82% course score

+8 pts

Life skills

48th

percentile

Life-skill test scores after each course

After this course+11 pts → 59th

Critical thinking

76% course score

+8 pts

Decision-making

71% course score

+7 pts

Confidence

68% course score

+7 pts

Curriculum

10 modules · 109 lessons · 0 min

Each module pairs the hand calculation with the same analysis in Excel, SPSS, R and Python.

Introduction to SEM Using AMOS
2:04
Downloading and Installing AMOS
4:12
Download Dataset
0:10
Guide for Downloading Well-being Data
1:38
References
2:37
Opening AMOS
1:56
Developing Familiarity with Top Menus
5:01
Getting Familiar with AMOS Graphics Tool
1:04
Understanding Input and Output Values on Path Diagram
1:30
Understanding "Group Number" Box
1:11
Understanding "Default Model" Box
1:05
Meaning of Unstandardized and Standardized Estimates
2:58
Understanding "Computation Summary" and "Files in Current Directory" Boxes
0:44
Understanding "Path Model Canvas" and "Output" Tab
0:47
Understanding Bottom Tabs: "Path Diagram" & "Tables"
0:50
Terminology Used for variables in Model
1:31
What is Structural Equation Modelling (SEM)?
1:00
What is an Exogenous Variable?
2:22
What are Observed and Unobserved Variables?
1:38
What are Residual Variables?
1:36
An Example: A structural Model of Managerial Innovation Process
0:41
What is Meaning of "Factor Loading"?
1:14
Drawing and Naming Observed Variables
3:29
Drawing Observed Variables and Error Terms
3:41
Using Drag and Touch-up Tools
1:13
Understanding Constrained Values on Error Terms
0:27
Using "Draw Paths" Tool
2:15
"Draw a Latent Variable" Tool
3:02
Using "Rotate" Tool
1:26
Using "Erase Object" Tool
0:45
Using Three Types of "Select Object" Tool
1:28
What is Meaning of Good Model Fit?
3:58
Meaning of Indicator & Factor Variances and Co-variances
1:28
When to Use Maximum Likelihood (ML) Method?
0:16
When to Use Asymptotic Distribution Free (ADF) Method?
0:18
What is Maximum Likelihood Method?
0:55
Assumptions of Maximum Likelihood Method
3:31
Other Model Discrepancy Calculation Methods: GLS, ULS, SLS & ADF
3:43
"Estimate Mean and Intercept:" Dealing with Missing Data
1:02
Understanding "Emulisrel 6" Option
0:48
Understanding "Chicorrect" and Learning to Constrain Values
4:03
Understanding "Fit Saturated and Independence Models" Option
2:23
How Large Should be Sample Size in SEM?
1:06
Can I Use AMOS if My Sample Distribution is Non-Normal?
0:49
Can I use AMOS if My Variables are Non-continuous?
0:42
Regression Vs. SEM & Adding More Variables to Model
1:01
What is Exploratory Factor Analysis (EFA)?
2:23
Understanding Latent Variables and Indicators in FA
1:12
Sample Researches Using FA in Social Science & Engineering
6:04
Historical Origin of FA & Its Application in Test Construction
4:44
Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)
5:27
Setting Data for Factor Analysis
2:42
Understanding "Selection Variable"
2:58
Univariate Descriptives & Initial Solutions: Descriptive
1:28
Understanding Inverse, Reproduced, Anti-Image
4:08
Extraction Method: Principle Component Analysis
3:04
Extraction Method: Principle Axis Factoring
1:44
Extraction Method: Maximum Likelihood Estimation
0:54
Choosing Correlation vs. Covariance Matrix for Factor Analysis
6:02
Interpreting Correlation Matrix & Unrotated Factor Solution
7:38
Determining Number of Factors: Scree Plot vs. Kaiser's Eigen Value Criteria
8:23
Factor Rotation: What It Is & Why Its Done?
6:36
Rotation Methods: Varimax, Quartimax, Equamax, Direct Oblimin, Promax
6:36
Calculating Factor Scores: Regression, Bartlett, Anderson-Rubin
3:54
Factor Score Coefficient Matrix
1:47
Missing Value Analysis: Listwise, Pairwise, Replace with Mean
2:55
Sort by Size & Suppressing Smaller Coefficients
6:22
Part 1: Identifying Dimensions of Personality
14:29
Part 2: Identifying Dimensions of Personality
15:39
Part 3: Identifying Dimensions of Personality
5:39
Part 4: Factor Naming
13:36
Part 5: Reliability Analysis of Factors
22:38
Part 6: Presenting Results in APA Style
8:59
Importing EFA model in AMOS
3:58
Reliability and Validity: Two Sides of Model Quality
0:27
Understanding Reliability and Validity
2:36
What is Validity?
2:18
Type of Construct Validity: Convergent Validity
2:13
Statistical Criteria for Convergent Validity in AMOS
1:16
What is Average Variance Extracted (AVE) & Why AVE More than .5 is Required?
4:41
Understanding Formula for AVE Calculation
3:26
Manual Calculation of AVE using Excel
5:47
What is Maximum Shared squared Variance (MSV)?
2:14
Why MSV Should be Less Than AVE for Discriminant Validity?
1:31
Manual Calculation of MSV?
3:13
What is Average Shared squared Variance (ASV)?
1:22
Why ASV should be less than AVE for Discriminant Validity?
1:17
Manual Calculation of ASV?
7:44
What are Indices of Model-Fit?
3:32
Type of Fit Indices: Incremental and Absolute Fit Indices
5:15
What are Incremental Fit Indices?
2:01
What are Absolute Fit Indices?
0:57
Which Indices Should I Report in Output or My Article?
3:45
How to Calculate Indices of Model Fit in AMOS?
1:40
Explaining CMIN (with Detailed Explanation of Variance-Covariance Matrix)
7:04
Symbolic Expression of Null Hypothesis of Goodness of Fit Test
1:50
Problem with Chi-Square Test & Why We Need Relative Chi-Square?
2:38
What is Relative Chi-Square?
2:24
Goodness of Fit Index (GFI) & Adjusted Goodnes of Fit Index (AGFI)
2:00
Parsimony based Goodness of Fit Index (PGFI)
1:29
SRMR: Conceptual Explanation
1:40
SRMR: Calculation
1:41
RMSEA: Conceptual Explanation
1:49
RMSEA: Calculation
1:11
What are Plugins?
1:12
Location of Plugins in AMOS 23 and Lower Versions
1:38
Location of Plugins in AMOS 24
3:04
Downloading AMOS Plugins from Statswiki Website (Prof. James Gaskin)
2:33
Installing Four Plugins by Prof. Gaskin in AMOS 23
4:01
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Who is this course for?

This course is designed for learners who want a practical, guided path through Data Analysis and Research.

What will I learn?

Perform Structural Equation Modeling (SEM) using IBM AMOS. Conduct Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Build, estimate, and validate measurement and structural models.

How long do I have access?

Your course access remains available for the lifetime of your account, including future updates to the lessons.

Is there a refund?

Yes. Eligible purchases include the platform money-back guarantee described at checkout.

We are committed to your progress

Lifetime access, mentor Q&A on every lesson, and a certificate you can share the moment you finish. If it is not right for you, the first 30 days are on us.

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