IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced
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IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced

Learn Structural Equation Modelling, Path Analysis and Confirmatory Analysis using IBM SPSS AMOS from Scratch
4.4 (16 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
69 students enrolled
Created by Dr. Sanjay Singh
Last updated 8/2017
English
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Current price: $10 Original price: $200 Discount: 95% off
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Includes:
  • 5.5 hours on-demand video
  • 5 Articles
  • 3 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • do confirmatory analysis using AMOS
  • establish reliability and validity of a scale using AMOS
  • do Structural Equation Modelling using AMOS
  • analyse complex path models and derive insight from multivariate data
View Curriculum
Requirements
  • There is no prerequisite. You just need computer with AMOS installed. Some basic understanding of Statistics and SPSS would be helpful.
Description

If you are looking to test a complex structural model then you already know the importance of AMOS. Its a powerful and one of the most popular tool for doing Structural Equation Modelling.

If you are a researcher then your knowledge of research will not be complete unless you mastered the SEM as vast majority of researches are increasingly using SEM. You can refer to my research papers that I have published using SEM:

  •  Sanjay Singh & Yogita Aggarwal (2017). Happiness at Work Scale: Construction and psychometric validation of a measure using mixed method approach. Journal of Happiness Studies. doi:10.1007/s10902-017-9882-x. Springer 
  •  Sanjay Singh & Yogita Aggarwal (2017). Antecedents and consequences of work significance in Indian organizations. Journal Management, Spirituality and Religion. doi: 10.1080/14766086.2017.1320580. Taylor & Francis 

In this course you will learn how to do SEM from scratch using AMOS. AMOS is a powerful tool for confirmatory validation and often used by researchers and psychometricians for research and high impact publishing. It enables you to specify, estimate, assess and present models to show hypothesized relationships among variables. The  AMOS software lets you build and test complex models more accurately and efficiently than standard multivariate statistics techniques. 

I am sure you will absolutely love this course. If not you can take your full refund within 30 days!! No questions asked!! 


I am very responsive to questions and in case you need any clarification I am just a message away. 


Some reviews from my SPSS Foundation course:

  • "Really Excellent in Explaining the topics each and every point step by step and I like his way of teaching approach.. I feel , it's very easy to understand the SPSS Tool in this way.. Thank You so much Dr. Sanjay Singh "
  • "Very well organized and easy to understand"
  • "its a must have course on SPSS. Excellent job by instructors! Trainer is very helpful n units are very well organized. Looking for more and more stuff from the trainer."

Sign up and Start learning AMOS the right way!! 



Who is the target audience?
  • Researchers and PhD students
  • Anyone looking to master SEM using AMOS
  • Data Analysts
  • Psychometricians
  • Professors
  • Research Methodologists
  • Social Scientists
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Curriculum For This Course
111 Lectures
05:44:57
+
Introduction & Installation of Software
2 Lectures 06:15

Downloading and Installing AMOS 24 (Free 14 Day Trial Version)
04:12
+
Practice Datasets, References and Resources
5 Lectures 02:40
Well-being Dataset
00:02

Guide for downloading well-being data
01:37

This dataset has been used for demonstrating exploratory factor analysis (EFA) in Section 8

Personality Data set
00:04

Link to Dropbox Folder Containing All Practice Datasets and Resources
00:03

+
Getting Familiar with AMOS Interface
10 Lectures 16:58
Opening AMOS
01:55

Developing Familiarity with Top Menus
05:00


Understanding Input and Output Values on Path Diagram
01:29

Understanding "Group Number" Box
01:10

Understanding "Default Model" Box
01:05


Understanding "Computation Summary" and "Files in Current Directory" Boxes
00:43

Understanding "Path Model Canvas" and "Output" Tab
00:47

Understanding Bottom Tabs: "Path Diagram" & "Tables"
00:49
+
Meanings & Definitions: Getting Familiar with Terminology of SEM
7 Lectures 09:21

What is Structural Equation Modelling (SEM)?
00:59


What are Observed and Unobserved Variables?
01:37

What are Residual Variables?
01:35

An Example: A structural Model of Managerial Innovation Process
00:40

+
Using AMOS Graphic Tools to Build a Structural Model
9 Lectures 17:40

Drawing Observed Variables and Error Terms
03:40

Using Drag and Touch-up Tools
01:12

Understanding Constrained Values on Error Terms
00:27

Using "Draw Paths" Tool
02:14

"Draw a Latent Variable" Tool
03:02


Using "Erase Object" Tool
00:44

Using Three Types of "Select Object" Tool
01:27
+
Understanding "Analyse Properties" Tab in AMOS
11 Lectures 22:15
What is meaning of Good Model Fit?
03:57

Meaning of Indicator & Factor Variances and Co-variances
01:27

When to Use Maximum Likelihood (ML) Method?
00:15

When to Use Asymptotic Distribution Free (ADF) Method?
00:17

What is Maximum Likelihood Method?
00:54

Assumptions of Maximum Likelihood Method?
03:30

Other Model Discrepancy Calculation Methods: GLS, ULS, SLS & ADF
03:42


Understanding "Emulisrel 6" Option
00:47

Understanding "Chicorrect" and Leaarning to Constrain Values
04:02

Understanding "Fit Saturated and Independence Models" Option
02:23
+
Issues in Structural Equation Modelling (SEM) Using AMOS
4 Lectures 03:35
How Large Should be Sample Size in SEM?
01:05

Can I Use AMOS if My Data is Non-Normal?
00:48

Can I use AMOS if My Variables are Non-continuous?
00:41

Regression Vs. SEM & Adding More Variables to Model
01:01
+
Exploratory Factor Analysis (EFA): A Precursor to CFA using AMOS
27 Lectures 02:49:16
What is Exploratory Factor Analysis (EFA)?
02:23

Understanding Latent Variables and Indicators in FA
01:11

Sample Researches Using FA in Social Science & Engineering
06:03

Historical Origin of FA & Its Application in Test Construction
04:43

Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)
05:27

Setting Data for Factor Analysis
02:41

Understanding "Selection Variable"
02:57

Univariate Descriptives & Initial Solutions: Descriptive
01:28

Understanding Inverse, Reproduced, Anti-Image
04:07

Extraction Method: Principle Component Analysis
03:03

Extraction Method: Principle Axis Factoring
01:43

Extraction Method: Maximum Likelihood Estimation
00:53

Choosing Correlation vs. Covariance Matrix for Factor Analysis
06:02

Interpreting Correlation Matrix & Unrotated Factor Solution
07:37

Determining number of factors: Scree Plot vs. Kaiser's eigen value criteria
08:22

Factor Rotation: What it is and why its done?
06:35

Rotation Methods: Varimax, Quartimax, Equamax, Direct Oblimin, Promax
08:11

Calculating Factor Scores: Regression, Bartlett, Anderson-Rubin
03:53

Factor Score Coefficient Matrix
01:46

Missing Value Analysis: Listwise, Pairwise, Replace with Mean
02:54

Sort by Size & Suppressing Smaller Coefficients
06:21

Project in Factor Analysis Part 1: Identifying Dimensions of Personality
14:29

Project in Factor Analysis Part 2: Identifying Dimensions of Personality
15:38

Project in Factor Analysis Part 3: Identifying Dimensions of Personality
05:39

Project in Factor Analysis Part 4: Factor Naming
13:35

Project in Factor Analysis Part 5: Reliability Analysis of Factors
22:37

Project in Factor Analysis Part 6: Presenting Results in APA Style
08:58
+
Scale Validation in AMOS
15 Lectures 43:53
Importing EFA model in AMOS
03:57

Reliability and Validity: Two Sides of Model Quality
00:27

Understanding Reliability and Validity
02:35

What is Validity?
02:17

Type of Construct Validity: Convergent Validity
02:13

Statistical Criteria for Convergent Validity in AMOS
01:15

What is Average Variance Extracted (AVE) & Why AVE More than .5 is Required?
04:41

Understanding Formula for AVE Calculation
03:25

Manual Calculation of AVE using Excel
05:46

What is Maximum Shared squared Variance (MSV)?
02:14

Why MSV Should be Less Than AVE for Discriminant Validity?
01:30

Manual Calculation of MSV?
03:12

What is Average Shared squared Variance (ASV)?
01:22

Why ASV should be less than AVE for Discriminant Validity?
01:16

Manual Calculation of ASV?
07:43
+
Indices of Model Fit
16 Lectures 40:46
What are Indices of Model-Fit?
03:32

Type of Fit Indices: Incremental and Absolute Fit Indices
05:15

What are Incremental Fit Indices?
02:00

What are Absolute Fit Indices?
00:57

Which Indices Should I Report in Output or My Article?
03:44

How to Calculate Indices of Model Fit in AMOS?
01:39

Explaining CMIN (with Detailed Explanation of Variance-Covariance Matrix)
07:03

Symbolic Expression of Null Hypothesis of Goodness of Fit Test
01:49

Problem with Chi-Square Test & Why We Need Relative Chi-Square?
02:38

Relative Chi-Square?
02:23

Goodness of Fit Index (GFI) & Adjusted Goodnes of Fit Index (AGFI)
02:00

Parsimony based Goodness of Fit Index (PGFI)
01:28

SRMR: Conceptual Explanation
01:39

SRMR: Calculation
01:41

RMSEA: Conceptual Explanation
01:48

RMSEA: Calculation
01:10
1 More Section
About the Instructor
Dr. Sanjay Singh
4.3 Average rating
108 Reviews
1,296 Students
3 Courses
Assistant Professor, Erasmus Mundus WILLPower Fellow

Dr. Sanjay Singh has over 7 years of teaching and research experience and has done his Masters and Doctoral Degree from University of Delhi, India. He have been recipient of Erasmus Mundus-WILLPower fellowship awarded by European Union. During the tenure of his fellowship he studied and worked at the University of Padova, Venice, Italy at the Centre for Risk and Decision-making (CeRD).

Publications:

1. Sanjay Singh & Yogita Aggarwal (2017). Happiness at Work Scale: Construction and psychometric validation of a measure using mixed method approach. Journal of Happiness Studies. doi:10.1007/s10902-017-9882-x. Springer 

2. Yogita Aggarwal & Sanjay Singh (2017). Antecedents and consequences of work significance in Indian organizations. Journal Management, Spirituality and Religion. doi: 10.1080/14766086.2017.1320580. Taylor & Francis