Using PLS-Graph in Structural Equation Modeling Data Analysis

1. Using PLS-Graph in Structural Equation Modeling Data Analysis Kwabena G. Boakye (KGB)

2. Structural Equation Modeling (SEM) SEM is a methodological technique that validates instruments and tests linkages between constructs. It involves two (2) techniques: • Covariance-based using LISREL (CB-SEM) • Variance-based using Partial Least Squares (PLS-SEM) PLS-SEM: a structured equation modeling estimation technique which generates estimation of item loadings and path coefficients SIMULTANEOUSLY LISREL has experienced substantial progress in its methodological capabilities than PLS, hence its popularity since the early 70’s

3. Recently, PLS methodology has gained prominent recognition in research settings such as management information systems (e.g., Dibbern, Goles, Hirschheim, & Jayatilaka,2004), e-business(e.g., Pavlou & Chai, 2002), organizational behavior (e.g., Higgins, Duxbury, & Irving, 1992), marketing (e.g., Reinartz, Krafft, & Hoyer, 2004) In fact, several alternative softwares are available for researchers to choose from: • PLS-GUI, • VisualPLS, • PLS-Graph, ( main focus today) • SmartPLS, • SPAD-PLS • WarpPLS

4. PLS-SEM and CB-SEM are not competitive in nature but rather complement each other

5. Note: • Use CB-SEM for causal modeling when prior theory is strong and further testing and development is the goal • Use PLS-SEM for causal modeling when prediction and/or theory building is the goal

6. Criteria for evaluating the PLS Model Results • Involves a two-step process • Outer model assessment • Inner model assessment

7. Outer Model Assessment • Reliability: • Composite reliability above 0.7 • eliminate an indicator only when it substantially increases the composite reliability of that latent variable • Remember a latent variable should explain at least 50% of each indicator’s variance • Construct Validity : • Convergent validity – AVE above 0.5 or significant t-statistics • Discriminant validity • Fornell-Larcker criterion (AVE of each latent variable should be higher than the squared correlations with all other latent variables) • Cross-loadings criterion (Loading of each indicator should be greater than all of its cross-loadings (Chin, 1998) N.B: These are good for reflective measurement models only

8. Inner Model Assessment

9. Using PLS-Graph • Open data in SPSS • Save data as an ASCII file using Tab delimited

10. 3. Rename the saved ASCII file to PLS-Graph friendly format (*.raw)

11. 4. Open PLS-Graph Programs Accessories PLS-Graph

12. 5. Save your new project (for convenience, save it in the same folder as your RAW data file).

13. 6. Set up links to your input and output files (even though some may have no data yet)

14. Do it for all files except the Log file

15. 7. Create your model • Select “Construct” from PLS Graph Tools (the middle icon) • Click on the workspace to create a construct • Repeat for each construct that you will include in the model • Name your constructs -> Right-click on a construct and select “Label…” F2 does not work! • Click OK, repeat for each construct in your model

16. 8. Assign indicators to constructs • Right-click on a construct and select “Indicators” • Select your indicators and then click “Assign”

17. Click on the “Links” button on the PLS Graph Tools menu • Click on IV and then on DV to create a link

18. Click the “Generate Extract Option” icon from the PLS Function toolbar or Click Execute Generate, Run, Extract from the menu bar

19. Assessing the Outer Model Item loading Item weight

20. To assess construct validity and composite reliability *.out Results

21. Outer Model estimates Convergent validity: T-values should be significant at 5% Discriminant validity: 1. Measurement item should load high on their theoretical construct with a value of at least 0.7 2. Square root of AVE > inter-construct correlation Composite Reliability: Better estimate than Cronbach’s alpha because it assumes that indicators are not equally reliable and takes into account that indicators have different loadings and hence, prioritizes indicators according to their reliabilities. C.R. > 0.7

22. Inner Model Estimates

23. Inner or Structural Model

24. Further Reading A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example (Gefen and Straub 2005, Vol. 16) The use of partial least squares path modeling in International Marketing (Hensler, Ringle and Sinkovics 2009, Vol. 20)