Description
Course Duration: 2 Weeks
Prerequisites:
- Basic understanding of statistics and research methods
- Familiarity with concepts of regression analysis and correlation
Course Outline:
Session 1: Introduction to Structural Equation Modeling (SEM) and SmartPLS
- Understanding the need for SEM in complex data analysis
- Overview of SmartPLS and its features
- Installation and setup of SmartPLS software
- Loading and organizing data in SmartPLS
Session 2: Theoretical Foundations of SEM & Measurement Model in SmartPLS
- Review of path analysis, factor analysis, and latent variables
- Causal vs. non-causal relationships
- Formulating research questions and hypotheses for SEM
- Defining reflective and formative constructs
- Construct validity and reliability
- Indicator selection and measurement scaling
- Assessing measurement model using SmartPLS
Session 3: Model Assessment & Structural Model in SmartPLS
- Concept of latent variables and their relationships
- Understanding model fit and its importance
- Model modification and improvement
- Model specification and development
- Path specification and hypothesis testing
- Running the structural model in SmartPLS
Session 4: Advanced Topics in SmartPLS
- Moderation and mediation analysis using SmartPLS
- Second-order and higher-order constructs
- Multigroup analysis for comparing groups
- Handling missing data in SmartPLS
Session 5: Reporting and Interpreting Results
- Interpreting path coefficients and their significance
- Presenting results graphically
- Writing the results section of a research paper
- Addressing limitations and discussing implications
Bonus: Best Practices and Ethical Considerations
- Ensuring model identification and identification problem
- Assessing multicollinearity and singularity
- Dealing with endogeneity and causality challenges
- Ethical considerations in SEM research
- Troubleshooting common issues in SmartPLS
Premium Packages: SmartPLS version 4 or 3 Professional License, SmartPLS report documents, Session Videos, After Training Services, Before Training Services



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