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)
- 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.