1. Component based SEM Comparison between various methods
Michel Tenenhaus

2. A Component-based SEM tree
ALL BLOCK REFLECTIVE
SEM
Component-based SEM
(Score computation)
Covariance-based SEM (CSA)
(Model estimation)
Chatelin-Esposito Vinzi
Fahmy-Jäger-Tenenhaus
XLSTAT-PLSPM (2007)
W. Chin
PLS-Graph
Herman Wold
NIPALS (1966)
PLS approach (1975)
J.-B. Lohmöller
LVPLS 1.8 (1984)
H. Hwang
Y. Takane
GSCA (2004)
VisualGSCA 1.0
(2007)
(AMOS 6.0, 2007)
Score computed
using block MV
loadings
Path analysis on the structural model
defined on the scores
For good blocks
(High Cronbach ):
- Score = 1st PC
- Score =  MV’s
Path-PCA
ULS-SEM
GSCA
Path-Scale
PLS
When the blocks are heterogeneous,
GSCA is too close to PCA. PLS and SEM
give almost the same results.
M. Tenenhaus : Component-based SEM
Total Quality Management, 2008
When all blocks are good, all the methods
give almost the same results.

3. The ECSI model

4. The ECSI model
Fairly good blocks

5. Outer weights (Fornell normalization)

6. the LVs coming from the 5 methods
Comparison between
the LVs coming from the 5 methods
PCA
ULS-SEM
SCALE
PLS
GSCA
When all blocks are good, all the methods
give almost the same results.

7. ECSI model with noise Noise variables are highly correlated (> .99)
and uncorrelated with Customer Satisfaction MVs.
For this new block:
- Noise = 1st PC
- Customer Satisfaction = 2nd PC

8. Fornell weights when the augmented Customer Satisfaction block is heterogeneous and reflective
GSCA is
trapped !!!!

9. Why GSCA is trapped The GSCA criterion PCA MSEV, Glang (1988)
MSEV = Maximum Sum of Explained Variance

10. For reflective blocks, GSCA seems to be too close to PCA
Fornell weights
for original
ECSI model

11. Fornell weights when the augmented Customer Satisfaction block is heterogeneous and formative
GSCA is still
trapped !!!!

12. = A Component-based SEM tree ALL BLOCK FORMATIVE Component-based SEM
Herman Wold
PLS approach (1975)
Mathes (1994)
Component-based SEM
(Score computation)
H. Hwang
VisualGSCA 1.0
(2007)
M. Glang
MSEV (1988) =
Glang and Hwang criteria are equivalent.
Computational practice: PLS  Maximum
PLS  Critical points

13. B + Centroid

14. B + Factorial

15. GSCA
R2=.491
R2=.263
R2=.380
R2=.691
R2=.301
R2=.313

16. Comparison between PLS, GSCA and CCA

17. Comparison between methods* * * *
Criterion optimized by the method
Practice supports “theory”

18. the LVs coming from the 3 methods
Comparison between
the LVs coming from the 3 methods
B + Centroid
B + Factorial
GSCA
When all blocks are good, all the methods
give almost the same results.

19. Economic inequality and political instability (Russet)
Agricultural inequality (X1)
GINI
INST + + +
ECKS +
FARM 1 + + +
DEAT
RENT - 3
D-STB +
GNPR + +
D-INS - 2
LABO -
DICT
Industrial
development (X2)
Political
instability (X3)

20. Use of XLSTAT-PLSPM Mode B + Centroid scheme
Y1 = X1w1
Y3 = X3w3
Y2 = X2w2

21. Use of XLSTAT-PLSPM Mode B + Factorial scheme
Y1 = X1w1
Y3 = X3w3
Y2 = X2w2

22. Use of GSCA (All formative)
When there is only one structural equation and
when all blocks are formative,GSCA is equivalent
to a canonical correlation analysis.

23. Use of XLSTAT-PLSPM for two blocks Mode B  Canonical Correlation Analysis

24. Comparison between methods* * * * *
Criterion optimized by the method
Practice supports “theory”

25. Conclusion
When the blocks are good (or moderately good) all methods seems to give almost the same LV scores.
When some blocks are heterogeneous, PLS and ULS-SEM seems to give better results than GSCA.
For all formative blocks : GSCA criterion is a more natural criterion than the PLS ones.
For all formative blocks : PLS give good results for multiblock data analysis.

26. Final conclusion William Camden (1623)
« All the proofs of a pudding are in the eating, not in the cooking ».
William Camden (1623)