Research and Data Analysis

Structural Equation Modeling (SEM) with AMOS/R/Python Training Course

Introduction

Structural Equation Modeling (SEM) is a powerful statistical technique widely used in research and analytics to model complex relationships among observed and latent variables. Structural Equation Modeling (SEM) with AMOS/R/Python Training Course is designed to equip participants with in-depth knowledge and practical skills in SEM using AMOS, R (lavaan, semPlot), and Python (semopy, statsmodels). Learners will gain expertise in advanced statistical modeling, multivariate analysis, confirmatory factor analysis, model fit evaluation, and more—making them industry-ready professionals in the field of data science and quantitative research.

With the rise of data-driven decision-making, the demand for skilled professionals in SEM has surged across industries including academia, healthcare, social sciences, marketing, and finance. This course merges theoretical foundations with real-world application through software-based modeling, syntax-based scripting, and case studies. Learners will master model specification, identification, estimation, evaluation, and modification while gaining the ability to translate raw data into actionable insights.

Programme Curriculum

Structural Equation Modeling (SEM) with AMOS/R/Python Training Course

Introduction

Course Objectives

Understand the fundamentals of Structural Equation Modeling (SEM)
Master Confirmatory Factor Analysis (CFA) and path modeling
Apply SEM techniques using AMOS, R, and Python
Interpret model fit indices: CFI, RMSEA, SRMR, Chi-square
Analyze latent variables and measurement models
Conduct model identification and estimation
Modify and respecify models for improved model fit
Visualize SEM using semPlot and AMOS path diagrams
Use Python’s semopy for scripting SEM models
Evaluate mediating and moderating effects in SEM
Handle missing data and perform data imputation
Generate publication-ready results and visualizations
Apply SEM in real-world research and business cases

Target Audience

Researchers in social sciences, psychology, or education
Data scientists and quantitative analysts
Academicians and Ph.D. candidates
Business analysts working with behavioral data
Healthcare and epidemiology professionals
Statistical consultants and research officers
Graduate students in statistics, data science, or econometrics
Machine learning practitioners using complex models

Course Duration: 5 days

Course Modules

Module 1: Introduction to SEM

Overview of SEM concepts
Understanding measurement and structural models
Key assumptions and requirements
Differences between SEM and regression
Benefits of SEM in research
Case Study: SEM in customer satisfaction analysis

Module 2: Measurement Model & Confirmatory Factor Analysis (CFA)

Latent variables and observed indicators
Model identification and CFA syntax
Goodness-of-fit statistics in CFA
Construct reliability and validity
Software comparison: AMOS vs R
Case Study: CFA for educational testing scales

Module 3: Structural Model Development

Specifying structural relationships
Mediation and moderation in SEM
Direct and indirect effects
Hypothesis testing within SEM
Sample size considerations
Case Study: SEM on job satisfaction and performance

Module 4: SEM with AMOS

Navigating AMOS interface
Drawing path diagrams
Model estimation in AMOS
Generating output and interpretation
Exporting visualizations
Case Study: AMOS-based model for healthcare service delivery

Module 5: SEM using R (lavaan & semPlot)

Installing and using lavaan package
Syntax-driven model building
semPlot for graphical representation
Fit measures in R
Reporting results in APA style
Case Study: SEM for consumer behavior analysis

Module 6: SEM using Python (semopy)

Introduction to semopy and pandas integration
Model definition using Python scripts
Model diagnostics and error handling
Visualization with networkx and semopy
Saving and exporting models
Case Study: Python SEM for HR retention modeling

Module 7: Model Fit, Diagnostics, and Modification

Absolute and incremental fit indices
Understanding residuals and modification indices
Handling model misspecification
Respecifying and re-estimating models
Evaluating nested models
Case Study: Model fit evaluation in medical research

Module 8: Advanced Applications and Reporting

Multi-group SEM and invariance testing
Longitudinal SEM techniques
Handling missing data
Writing and publishing SEM studies
Ethical considerations in SEM
Case Study: Multi-group SEM for cross-cultural studies

Training Methodology

Interactive instructor-led sessions via Zoom/Teams
Live software demonstrations (AMOS, R, Python)
Hands-on modeling exercises with real datasets
Downloadable SEM templates and scripts
Group discussions and Q&A after each module
Evaluation through mini-projects and case study analysis

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

SEM AMOS/R/Python path analysis

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000