IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced

Name: IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced
Rating: 4.2 (1140 reviews)

Learn Structural Equation Modelling, Path Analysis and Confirmatory Analysis using IBM SPSS AMOS from Scratch

Created byScholarsight Learning

Last updated 10/2024

English

What you'll 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

Course content

12 sections • 114 lectures • 5h 40m total length

Introduction2:03
Downloading and Installing AMOS 24 (Free 14 Day Trial Version)4:07
How to get answer to your queries fast?0:44

Opening AMOS1:48
Developing Familiarity with Top Menus4:55
Getting Familiar with AMOS Graphics Tools0:58
Understanding Input and Output Values on Path Diagram1:24
Understanding "Group Number" Box1:04
Understanding "Default Model" Box0:59
Understanding Unstandardized and Standardized Estimates2:51
Understanding "Computation Summary" and "Files in Current Directory" Boxes0:39
Understanding "Path Model Canvas" and "Output" Tab0:37
Understanding Bottom Tabs: "Path Diagram" & "Tables"0:44
Explore the bottom tabs to view a path diagram and tables, showing variables, their estimates, and standard errors via the view text option.

Drawing and Naming Observed Variables3:24
Drawing Observed Variables and Error Terms3:34
Using Drag and Touch-up Tools1:07
Understanding Constrained Values on Error Terms0:22
In AMOS, every error term or residual has a built-in constraint: the path value is automatically set to one. This ensures the model remains defined and calculations are possible.
Using "Draw Paths" Tool2:09
Draw paths with single headed arrows to model relationships in AMOS, treating socioeconomic status as a mediating variable affecting performance, and add error terms for model identification.
"Draw a Latent Variable" Tool2:57
Using "Rotate" Tool1:22
Using "Erase Object" Tool0:39
Using Three Types of "Select Object" Tool1:22

What is meaning of Good Model Fit?3:48
Meaning of Indicator & Factor Variances and Co-variances1:17
When to Use Maximum Likelihood (ML) Method?0:05
Explore when to apply the maximum likelihood method in sem; the lecture shows that when data are normal, maximum likelihood is the better technique.
When to Use Asymptotic Distribution Free (ADF) Method?0:07
Learn when to apply the asymptotic distribution free method, highlighting its use only with very large samples, typically in the thousands.
What is Maximum Likelihood Method?0:44
Assumptions of Maximum Likelihood Method?3:21
Other Model Discrepancy Calculation Methods: GLS, ULS, SLS & ADF3:33
"Estimate Means and Intercepts": Dealing with Missing Data0:51
Understanding "Emulisrel 6" Option0:38
Understanding "Chicorrect" and Leaarning to Constrain Values3:52
Understanding "Fit Saturated and Independence Models" Option2:13

What is Exploratory Factor Analysis (EFA)?2:12
Explain what exploratory factor analysis is and how it reduces many data items into a few meaningful factors, using personality traits as an example.
Understanding Latent Variables and Indicators in FA1:03
Sample Researches Using FA in Social Science & Engineering5:54
Historical Origin of FA & Its Application in Test Construction4:36
Trace the historical origin of factor analysis from Spearman to Cattell and apply exploratory factor analysis to compress large item pools into meaningful scales and test factors.
Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)5:17
Setting Data for Factor Analysis2:33
Understanding "Selection Variable"2:50
Univariate Descriptives & Initial Solutions: Descriptive1:18
Explore descriptive statistics in SPSS AMOS using univariate descriptives to view the mean and standard deviation for each observed variable, and note the initial solution for commonalities.
Correlation Matrix: Coefficients, Significance, Determinant, KMO & Bartlett's4:31
Explore building and interpreting a correlation matrix for exploratory factor analysis, including coefficients, significance, and determinant checks, with KMO and Bartlett's tests of sphericity to assess adequacy.
Understanding Inverse, Reproduced, Anti-Image3:58
Extraction Method: Principle Component Analysis2:54
Extraction Method: Principle Axis Factoring1:40
Extraction Method: Maximum Likelihood Estimation0:48
Learn how maximum likelihood estimation overcomes the sample-population gap in SEM extraction, offering probabilistic estimation and greater generalizability compared to PCA and other methods.
Choosing Correlation vs. Covariance Matrix for Factor Analysis5:53
Interpreting Correlation Matrix & Unrotated Factor Solution7:29
Determining number of factors: Scree Plot vs. Kaiser's eigen value criteria8:14
Factor Rotation: What it is and why its done?6:28
Rotation Methods: Varimax, Quartimax, Equamax, Direct Oblimin, Promax8:04
Calculating Factor Scores: Regression, Bartlett, Anderson-Rubin3:47
Factor Score Coefficient Matrix1:40
Missing Value Analysis: Listwise, Pairwise, Replace with Mean2:49
Sort by Size & Suppressing Smaller Coefficients6:16
Project in Factor Analysis Part 1: Identifying Dimensions of Personality14:21
Identify latent personality factors by performing a from-scratch factor analysis in SPSS with principal component analysis, scree plot, and varimax rotation.
Project in Factor Analysis Part 2: Identifying Dimensions of Personality15:32
Project in Factor Analysis Part 3: Identifying Dimensions of Personality5:31
Project in Factor Analysis Part 4: Factor Naming13:29
Project in Factor Analysis Part 5: Reliability Analysis of Factors22:31
Project in Factor Analysis Part 6: Presenting Results in APA Style8:53

Importing EFA model in AMOS3:52
Import the EFA model's rotated component matrix into Amos with Pattern Matrix Builder. Specify the data file and perform missing value analysis with EM to validate a six-factor personality scale.
Reliability and Validity: Two Sides of Model Quality0:22
Understanding Reliability and Validity2:30
What is Validity?2:12
Type of Construct Validity: Convergent Validity2:08
Statistical Criteria for Convergent Validity in AMOS1:10
What is Average Variance Extracted (AVE) & Why AVE More than .5 is Required?4:36
Understanding Formula for AVE Calculation3:20
Manual Calculation of AVE using Excel5:42
What is Maximum Shared squared Variance (MSV)?2:09
Why MSV Should be Less Than AVE for Discriminant Validity?1:25
Manual Calculation of MSV?3:07
What is Average Shared squared Variance (ASV)?1:17
Why ASV should be less than AVE for Discriminant Validity?1:11
Manual Calculation of ASV?7:38

What are Indices of Model-Fit?3:22
Type of Fit Indices: Incremental and Absolute Fit Indices5:05
What are Incremental Fit Indices?1:55
What are Absolute Fit Indices?0:52
Which Indices Should I Report in Output or My Article?3:40
Report a judicious combination of incremental and absolute fit indices to assess model fit. Emphasize CFI as the incremental index, and include GFI, SRMR, RMK, and chi-square by df ratio.
How to Calculate Indices of Model Fit in AMOS?1:34
Explaining CMIN (with Detailed Explanation of Variance-Covariance Matrix)6:58
Symbolic Expression of Null Hypothesis of Goodness of Fit Test1:44
Describe the chi-square null hypothesis as the comparison of population and model implied covariance matrices, and show good fit when sample, model implied, and population covariances align via maximum likelihood.
Problem with Chi-Square Test & Why We Need Relative Chi-Square?2:33
Relative Chi-Square?2:18
Goodness of Fit Index (GFI) & Adjusted Goodnes of Fit Index (AGFI)1:55
Parsimony based Goodness of Fit Index (PGFI)1:23
SRMR: Conceptual Explanation1:34
SRMR: Calculation1:36
Calculate srmr with the amos plugin by opening the dialog and clicking calculate estimates. Determine that the default model's srmr is 0.0774, near 0.08 cutoff, so we reject the model.
RMSEA: Conceptual Explanation1:44
Explore rmk, a measure of lack of model fit that adjusts for model complexity and provides a 90% confidence interval; near-zero lower limit, upper limit under 0.08, indicate good model.
RMSEA: Calculation1:05

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."

Who this course is for:

Researchers and PhD students
Anyone looking to master SEM using AMOS
Data Analysts
Psychometricians
Professors
Research Methodologists
Social Scientists

IBM SPSS AMOS Foundation Course: SEM Scratch to Advanced

What you'll learn

Explore related topics

Course content

Introduction & Installation of Software3 lectures • 7min

Practice Datasets, References and Resources5 lectures • 3min

Getting Familiar with AMOS Interface10 lectures • 16min

Meanings & Definitions: Getting Familiar with Terminology of SEM7 lectures • 9min

Using AMOS Graphic Tools to Build a Structural Model9 lectures • 17min

Understanding "Analyse Properties" Tab in AMOS11 lectures • 20min

Issues in Structural Equation Modelling (SEM) Using AMOS4 lectures • 3min

Exploratory Factor Analysis (EFA): A Precursor to CFA using AMOS28 lectures • 2hr 51min

Scale Validation in AMOS15 lectures • 43min

Indices of Model Fit16 lectures • 39min

Requirements

Description

Who this course is for: