Modeling and Analysis Using SEM Software
( 25 Modules )

Module #1
Introduction to Structural Equation Modeling
Overview of SEM, its application, and importance

Module #2
Software Overview:Getting Familiar with SEM Software
Introduction to popular SEM software, interface, and basic functionality

Module #3
Research Questions and Hypotheses in SEM
Formulating research questions and hypotheses in the context of SEM

Module #4
Data Preparation for SEM
Preparing data for SEM analysis, including data cleaning and preprocessing

Module #5
Measurement Models in SEM
Introduction to measurement models, including factor analysis and item response theory

Module #6
Structural Models in SEM
Introduction to structural models, including path analysis and regression models

Module #7
Model Specification:Defining Latent Variables and Relationships
Specifying SEM models, including defining latent variables and relationships

Module #8
ModelIdentification and Evaluation
Evaluating model identification, including model fit indices and modification indices

Module #9
Estimation and Model Fit in SEM
Estimation methods in SEM, including maximum likelihood and Bayesian estimation

Module #10
Model Fit Indices and Model Selection
Evaluating model fit using fit indices, including chi-square, RMSEA, and CFI

Module #11
Model Modification and Respecification
Modifying and respecifying SEM models based on fit indices and modification indices

Module #12
MEDiation Analysis in SEM
Conducting mediation analysis using SEM, including direct and indirect effects

Module #13
Moderation Analysis in SEM
Conducting moderation analysis using SEM, including interaction effects

Module #14
Multi-Group Analysis in SEM
Conducting multi-group analysis using SEM, including invariance testing

Module #15
SEM with Non-Normal Data
Dealing with non-normal data in SEM, including robust estimation methods

Module #16
Power Analysis and Sample Size Determination in SEM
Conducting power analysis and determining sample size for SEM studies

Module #17
Reporting and Interpreting SEM Results
Reporting and interpreting SEM results, including visualizing models and results

Module #18
Common Pitfalls and Errors in SEM
Avoiding common pitfalls and errors in SEM, including model misspecification and overfitting

Module #19
Advances in SEM:Latent Class Analysis and Mixture Modeling
Introduction to latent class analysis and mixture modeling using SEM

Module #20
Advances in SEM:Bayesian SEM and Machine Learning
Introduction to Bayesian SEM and machine learning approaches using SEM

Module #21
SEM in Various Fields:Applications and Case Studies
Applications and case studies of SEM in various fields, including psychology, education, and business

Module #22
SEM Software Tutorial 1:Using [Software Name] for Basic SEM Analysis
Hands-on tutorial using [Software Name] for basic SEM analysis

Module #23
SEM Software Tutorial 2:Using [Software Name] for Advanced SEM Analysis
Hands-on tutorial using [Software Name] for advanced SEM analysis

Module #24
Best Practices in SEM:Writing a SEM Research Paper
Best practices in writing a SEM research paper, including reporting guidelines

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Modeling and Analysis Using SEM Software career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Modeling and Analysis Using SEM Software ( 25 Modules )

Modeling and Analysis Using SEM Software
( 25 Modules )