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.

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. Contact student:
Raphaella de M. Cezar
Trainee - Jr. Eng.Eng. de Produção Mecânica
UNESP Guaratinguetá
Cel.: (12) 8172 6064
e-mail: raphaella@globo.com
Francesco Andreoli
ETH Zuerich, Switzerland
e-mail: francesco.andreoli@hotmail.com
http://www.linkedin.com/in/francescoandreoli

3. Goal
Showing the student the main properties of SmartPLS
software used for basic statistical treatments.
1 part
Overview of the situation and presentation of the software.
2 part
Video showing how SmartPLS works

4. 1 part

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. Refresh
• Correlation
– linear relationship between two variables
– range from -1 to +1
• Covariance
– unstandardised form of correlation
– positive number  positive relationship

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. Software on market
•Lavaan
•Sem package in R programmation
•EQS
•Mplus
•SPSS Amos
•SmartPLS
•Partial Least Squares
•theory and measures simultaneously examination

9. Graphical Vocabulary
Observed Variable
Latent variable
Error
Predictive relationship
(Cov)
correlation

10. SmartPlS
Quick Tutorial
Source: http://www.smartpls.de/

11. Create new project

14. Model creation

21. Import data

26. If the variable become color blu the data input and
the model is correct

27. And then simulate:

28. Report

29. Resume simulation Variable
insertion
Model design
Data input
Box description

30. Our Final case examinated in
SmartPLS

31. Questionnaire development
Answer range from 1 to 5

32. Excel data
Question nr. Supp. Infos
Case studied Quest. res

33. Data input in smartPLS
This Matrix is only a
preview!! The all data is in

35. Construct Model in SmartPLS
Benefit
Difficulties
Performance

36. 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

37. Trust field
•Reliability: This is demonstrated by Composite
Reliability greater than 0.700.
•Convergent Validity: This is demonstrated by loadings
greater than 0.700, AVE greater than 0.500, and
Communalities greater than 0.500
•Discriminant validity: This is demonstrated by the square
root of the AVE being greater than any of the inter-
construct correlations.

38. 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 0,0182
cont 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

39. 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 0
cont 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 0
cont 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

40. Sources:
http://web.psych.unimelb.edu.au/jkanglim/Introduction
toSEM.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

41. 2 part
Tutorial video