SmartPLS presentation

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SmartPLS is a software application for (graphical) path modeling with latent variables (LVP). The partial least squares (PLS)-method is used for the LVP-analysis in this software.

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SmartPLS presentation

  1. 1. Structural Equation Modelling & Path Analysis Resources Performed by SmartPLS Prof. Livre Docente Otávio J. Oliveira Bolsista Produtividade DT/CNPq e-mail: otavio@feb.unesp.br  Currículo: http://lattes.cnpq.br/8045074316518664 FEG/UNESP
  2. 2. Contact student:Raphaella de M. CezarTrainee - Jr. Eng.Eng. de Produção MecânicaUNESP GuaratinguetáCel.: (12) 8172 6064e-mail: raphaella@globo.comFrancesco AndreoliETH Zuerich, Switzerlande-mail: francesco.andreoli@hotmail.com http://www.linkedin.com/in/francescoandreoli
  3. 3. Goal Showing the student the main properties of SmartPLS software used for basic statistical treatments. 1 partOverview of the situation and presentation of the software. 2 part Video showing how SmartPLS works
  4. 4. 1 part
  5. 5. Overview of the presentation• Intro•Path diagram•Software•Worked Example •Data collection •Model design •Hypotesis •Simulation and parameter estimates •Overview of the results
  6. 6. Refresh• Correlation – linear relationship between two variables – range from -1 to +1• Covariance – unstandardised form of correlation – positive number  positive relationship
  7. 7. • Latent variable – not measured directly in a study – assumed to bring about the observed responses• Observed variables – directly measured in a study• Exogenous variables – assumed to be external to the model – only have double headed arrows (i.e., correlation)• endogenous variables – predicted by other variables in the model – directed arrow entering into them
  8. 8. Software on market•Lavaan •Sem package in R programmation•EQS•Mplus•SPSS Amos•SmartPLS •Partial Least Squares •theory and measures simultaneously examination
  9. 9. Graphical Vocabulary Observed Variable Latent variable Error Predictive relationship (Cov) correlation
  10. 10. SmartPlSQuick Tutorial Source: http://www.smartpls.de/
  11. 11. Create new project
  12. 12. Model creation
  13. 13. Import data
  14. 14. If the variable become color blu the data input and the model is correct
  15. 15. And then simulate:
  16. 16. Report
  17. 17. Resume simulation Variable insertionModel design Data input Box description
  18. 18. Our Final case examinated in SmartPLS
  19. 19. Questionnaire development Answer range from 1 to 5
  20. 20. Excel dataQuestion nr. Supp. Infos Case studied Quest. res
  21. 21. Data input in smartPLS This Matrix is only a preview!! The all data is in
  22. 22. Construct Model in SmartPLS Benefit Difficulties Performance
  23. 23. Coeff definition review•AVE • average value•Reability •equal factor loadings misured • variance portion rapresentation•Composite reability •overall reliability of a collection of heterogeneous•R square •coefficient of determination, measuring the amount of variation accounted for in the endogenous constructs by the exogenous constructs•Cronbach’s α •lower-bound estimate for the composite score reliability
  24. 24. Trust field•Reliability: This is demonstrated by CompositeReliability greater than 0.700.•Convergent Validity: This is demonstrated by loadingsgreater than 0.700, AVE greater than 0.500, andCommunalities greater than 0.500•Discriminant validity: This is demonstrated by the squareroot of the AVE being greater than any of the inter-construct correlations.
  25. 25. Result Quality criteria overview AVE Composite Reliability R Square Cronbachs Alpha Communality Redundancy HR 0,6629 0,854 0,3498 0,742 0,6629 0,2307 benefit 0,6167 0,8642 0,56 0,7941 0,6167 0,0182cont improvement 0,7364 0,8932 0,6124 0,8198 0,7364 0,246 costumers 0,735 0,8925 0 0,8202 0,735 0 difficulties 0,3922 0,1331 0,2363 0,2717 0,3922 0,0419 performance 0,4084 0,807 0,4731 0,728 0,4084 0,0275 standardization 0,7621 0,9057 0,4953 0,8437 0,7621 -0,1583 suppliers 0,8242 0,9336 0,3851 0,893 0,8242 0,0657
  26. 26. Path coefficient HR benefit cont improvement costumers difficulties performance standardization suppliers HR 0 0,0342 0,3447 0 -0,2366 0,0804 -0,2318 0,2123 benefit 0 0 0 0 -0,0355 0 0 0cont improvement 0 -0,0033 0 0 0,0137 -0,0167 0,6638 -0,0516 costumers 0,5915 0,5414 0,5277 0 -0,0302 0,5837 0,2213 -0,1639 difficulties 0 0 0 0 0 -0,0719 0 0 performance 0 0 0 0 0 0 0 0 standardization 0 0,2212 0 0 -0,2935 -0,144 0 0,6544 suppliers 0 0,0831 0 0 -0,0292 0,2635 0 0 Path coefficient total Effect HR benefit cont improvement costumers difficulties performance standardization suppliers HR 0 0,0484 0,3447 0 -0,2383 0,1429 -0,0031 0,1925 benefit 0 0 0 0 -0,0355 0,0026 0 0cont improvement 0 0,1753 0 0 -0,1985 0,0029 0,6638 0,3828 costumers 0,5915 0,7099 0,7316 0 -0,3612 0,6411 0,5697 0,2967 difficulties 0 0 0 0 0 -0,0719 0 0 performance 0 0 0 0 0 0 0 0 standardization 0 0,2755 0 0 -0,3224 0,0516 0 0,6544 suppliers 0 0,0831 0 0 -0,0321 0,2658 0 0
  27. 27. Sources:http://web.psych.unimelb.edu.au/jkanglim/IntroductiontoSEM.pdfhttp://www.smartpls.de/forum/index.phphttp://statwiki.kolobkreations.com/wiki/PLS PLS Reliability and validity http://statwiki.kolobkreations.com/wiki/PLShttp://zencaroline.blogspot.com.br/2007/06/composite-reliability.html
  28. 28. 2 partTutorial video

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