Tutorial PLS SEM menggunakan smartPLS lengkap dengan interpretasi output #pemula

Summary

This tutorial introduces SmartPLS for Partial Least Squares Structural Equation Modeling, focusing on analyzing the relationships among motivation to learn, job opportunities, parental support, and interest. It guides users through preparing data, creating a structural model, and evaluating its validity and reliability. Key concepts such as loading factors, Cronbach's Alpha, and R-squared values are explained to assess the model's significance and fit. The tutorial emphasizes practical steps in using SmartPLS, making it accessible for beginners interested in structural equation modeling.

Takeaways

• 📊 SmartPLS is used for Partial Least Squares Structural Equation Modeling (PLS-SEM) with latent variables.
• 📈 The tutorial covers the relationship between four latent variables: learning motivation, job opportunities, parental support, and interest.
• 📝 Data should be prepared in a text format or CSV before importing into SmartPLS.
• 🔄 After importing data, the tutorial demonstrates how to create a Venn diagram representing the relationships among the variables.
• 🔍 The outer model consists of loading factors, with values above 0.7 indicating good convergent validity.
• 📉 Reliability is assessed using Cronbach's Alpha and composite reliability, with values above 0.7 considered acceptable.
• 💡 Average Variance Extracted (AVE) values greater than 0.5 indicate good convergent validity.
• 📊 Discriminant validity is assessed by comparing the square root of AVE with the correlation coefficients among latent variables.
• 🔗 Statistical significance of relationships is determined using t-statistics and p-values, with thresholds of 1.96 and 0.05 respectively.
• 💻 Model fit is evaluated through R-squared, with values above 0.26 indicating a good model fit.

Q & A

What are the four latent variables discussed in the tutorial?

The four latent variables are learning motivation, job opportunities, parental support, and interest.

What software is being used for the analysis?

The analysis is conducted using SmartPLS.

How should the data be prepared before importing it into SmartPLS?

The data should be prepared in Notepad or CSV format before importing it into SmartPLS.

What does a loading factor greater than 0.7 indicate?

A loading factor greater than 0.7 indicates that the indicator has good convergent validity.

What is the significance of Cronbach's alpha and composite reliability?

Both metrics assess the reliability of the latent variables; a value greater than 0.7 indicates good reliability.

What criteria must t-statistics meet to conclude significance between variables?

The t-statistics must be greater than 1.96 to conclude that the relationship between the latent variables is significant.

How is discriminant validity assessed in the tutorial?

Discriminant validity is assessed by comparing the square root of the average variance extracted (AV) against the correlations between latent variables.

What is the R-squared value mentioned, and why is it important?

The R-squared value is 0.493, indicating that the model explains 49.3% of the variance in the dependent variable, which is considered a good fit.

What action should be taken if a loading factor is below 0.7?

If a loading factor is below 0.7, it may indicate a weaker indicator, and further evaluation is necessary to determine its impact.

What invitation does the presenter extend to viewers at the end of the tutorial?

The presenter invites viewers to comment or ask questions about the tutorial content.