Steps in Data Analysis - SmartPLS4 Series
Learn the steps in Data Analysis when using Structural Equation Modelling.

Steps in Data Anlayiss
The focus of the session is to help scholars learn the basic steps in Data Analysis when using SmartPLS4
Steps in Data Analysis and Results
- Clean your data
- Measurement Model Assessment
- Factor Loading
- Reliability (Alpha and Composite Reliability)
- Validity
- Convergent Validity (AVE)
- Discriminant Validity (Fornell & Larcker Criterion, HTMT (Preferred), or Cross Loadings)
- Report Measurement Model
- Structural Model Assessment
- Check for Collinearity
- Assess and Report Significance of Relationships (through Bootstrapping)
- Check for Bootstrapped Path Coefficients, T Statistics, P Values
- A T value over 1.96 (two tailed) and p value < .05 mean significant results and the (alternate) hypothesis is substantiated.
- Assess the Explanatory (R-Square) and Predictive Power (PLS-Predict)
Video for Each Step
Step by Step Approach to Data Analysis using Structural Equation Modelling (See Description). The short session guides on the steps for data analysis when using Structural Equation Modelling.
What is SEM?
https://youtu.be/H0d5LZ9Bs64
https://youtu.be/15hxvI2bSJgData Cleaning
https://youtu.be/dZdIiEsgHWE
Video
Additional Literature Review Resources
- A Practical Example of Writing and Formatting the Literature Review
- From Literature to Research Problem, Objectives and Questions
- How to Avoid Plagiarism?
- How to Develop relationship between variables using a theory?
- How to Use Google Scholar for Literature Review
- How to Use QDA Miner Lite for Searching Literature
- How to Use Scholarcy and Google Tall to Books to Extract Literature
- How to Write the Literature Review
- Theoretical vs Conceptual Framework – Different, Similar, or Complimentary
- Using Mendeley for Literature Search