1) The document discusses evaluating PLS-SEM models, including assessing the measurement model (reliability, convergent validity, discriminant validity) and structural model (R2, f2, Q2). 2) Measurement model evaluation includes examining Cronbach's alpha, composite reliability, outer loadings, and average variance extracted. Discriminant validity is assessed using Fornell-Larcker criterion and cross loadings. 3) The example output shows the measurement model meeting reliability, convergent validity, and discriminant validity criteria for the COMP, LIKE, and CUSL constructs.

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https://www.youtube.com/watch?v=BdWxexRzX2I

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1. Model estimation delivers empirical measures of the relationships
between the indicators and the constructs, as well as between the
construct.We can determine how well the theory fits the data
2. PLS-SEM results are reviewed and evaluated using a systematic
process. The goal of PLS-SEM is maximizing the explained variance
(the R2 value) of the endogenous latent variables.
3. For this reason, evaluation of the model (measurement and
structural) focus on model predictive capability.
4. The most important measurement models are reliability,
convergent validity, and discriminant validity.
5. For the structural model are R2 (explained variance), f2 (effect size)
and Q2 (predictive relevance)

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The systematic evaluation follows a two step process :
1) measurement models and 2) structural model

6. • To evaluate internal consistency reliability researcher using
Cronbach’s alpha, and composite reliability

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All partial regression models are estimated by the PLS-SEM
algorithm’s iterative procedures. Select a value of at least 300
for the maximum number of iterations.

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Construct Cronbach
Alpha
Criteria Result Composite
Reliability (CR)
Criteria Result
COMP 0.776 0-1 Reliable 0.870 0.7 to 0.9 Reliable
CUSA 1.000 0-1 Reliable 1.000 0.7 to 0.9 Reliable
CUSL 0.421 0-1 Reliable 0.692 0.7 to 0.9 Reliable
LIKE 0.831 0-1 Reliable 0.897 0.7 to 0.9 Reliable

9. • Convergent Validity is the extent to which a measure correlates
positively with alternative measure of the same construct.
• To evaluate convergent validity consider the outer loadings of the
indicators and the AVE

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CONSTR
UCT
Outer
loading
Criteria Result AVE Criteria Result
COMP 0.692 >0.5 fulfilled
comp_1 0.766 >0.708 fulfilled
comp_2 0.858 fulfilled
comp_3 0.868 fulfilled
LIKE 0.744 fulfilled
like_1 0.903 fulfilled
like_2 0.859 fulfilled
like_3 0.824 fulfilled
CUSA 1.000 fulfilled 1.000 fulfilled
CUSL 0.530 fulfilled
cusl_1 -0.002
cusl_2 0.874 fulfilled
cusl_3 0.909 fulfilled

11. • Discriminant Validity is the extent to which a construct is truly distinct
from other constructs. It implies that a construct is unique.
• To evaluate discriminant validity consider : cross loading & fornell &
Larcker criterion

12. Test Criteria
Fornell & Larcker compares the square root of the AVE values with the latent variable
correlations. As shown in Table the square root of each construct's AVE
should be greater than its highest correlation with any other construct
COMP CUSA CUSL LIKE AVE SQRT AVE
COMP 1.000 0.692 0.832
CUSA 0.119 1.000 1.000 1.000
CUSL 0.126 0.614 1.000 0.530 0.728
LIKE 0.622 0.045 0.081 1.000 0.744 0.863

13. Test Criteria
Cross Loading, Cross loading report shown that all indicator's outer loading on
the associated construct are greater than all of its loadings on
other constructs, therefore cross loading is fulfilled.Cross Loadings
COMP CUSA CUSL LIKE
comp_1 0.766 0.082 0.103 0.612
comp_2 0.858 0.113 0.105 0.462
comp_3 0.868 0.100 0.108 0.495
cusa 0.119 1.000 0.614 0.045
cusl_1 0.008 -0.002 -0.002 0.053
cusl_2 0.082 0.504 0.874 0.076
cusl_3 0.139 0.586 0.909 0.070
like_1 0.574 0.056 0.082 0.903
like_2 0.498 0.038 0.060 0.859
like_3 0.534 0.014 0.066 0.824

14. An estimate of the correlation between the construct. , based on the average
of heterotrait-heteromethod correlation as suggested by (Henseler, Ringle,
& Sarstedt, 2015).
The ratio of HTMT is expected lower than 0.90 at 95% confident interval. To
examining the HTMT ratio, we tested whether the HTMT values are
significantly different from 1.
The value of HTMT higher than 0.9 indicate there is a lack of discriminant
validity.

15. Latent
Variables
Indicators
Internal Consistency
Reliability
Convergent Validity Discriminant
Validity
Composite
Reliability
Cronbach
Alpha
Loadings AVE
0.6 – 0.9 0.6 - 0.9 >0.7 >0.5
HTMT
confidence
interval
doesn’t
include 1
COMP Comp_1 0.870 0.776 0.766 0.693 Yes
Comp_2 0.858
Comp_3 0.868
LIKE Like_1 0.897 0.831 0.903 0.744 Yes
Like_2 0.859
Like_3 0.824
CUSL Cusl_1 0.692 0.421 -0.002 0.530 Yes
Cusl_2 0.874
Cusl_3 0.909

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Firdha_beth@sbm-itb.ac.id