This document discusses partial least squares structural equation modeling (PLS-SEM). It explains that PLS-SEM is suitable for predictive applications and complex models with many indicators or small sample sizes. It also provides guidelines for specifying the structural and measurement models in PLS-SEM, including collecting and preparing the data, before analyzing models using SmartPLS software.

1. My name is FIRDA
I am here because
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2. SESSION 1
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3. 1. What is PLS-SEM
2. When to use PLS-SEM
3. Minimum Sample Requirements
4. PLS-SEM Path Modelling
5. Model Creation using SmartPLS
a) Specifying the Structural Model
b) Specifying the Measurement Model
c) Collecting and Preparing Data
d) Have SmartPls software ready
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4.  2nd Generation technique of multivariate method to
test the hypothesis of existing theories and concept
(confirmatory) or to develop theory (exploratory)
 Focus on the predictive ability of the model
(explaining the variance in the dependent variables)
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5. Primarily Exploratory Primarily Confirmatory
First Generation techniques • Cluster Analysis
• Exploratory Factor
Analysis
• Multidimensional Scaling
• Analysis of Variance
• Logistic regression
• Multiple regression
• Confirmatory factor
analysis
Second Generation
Techniques
Partial Least Squares
structural equation modelling
(PLS-SEM)
• Covariance-based
structural equation
modelling (CB-SEM)
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6.  The goal is predicting target constructs
 The structural model is complex
 The sample size is small.
 The data are non normally distributed
 Measurement scale is nominal,ordinal and interval
 Formatively measured constructs
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7.  Rules of thumb provided by Cohen(1992)
 Alternatively use program such as G*Power to carry out
power analyses specific to model set up.
http://www.gpower.hhu.de/
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8. 8
Sample Size Recommendation in PLS-SEM for a Statistical Power of 80%
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Indicators /
Items
Indicators /
Items
Exogenous latent
variable
Endogenous latent
variable

11. REFLECTIVE
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12. FORMATIVE
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14. 1. Specifying the Structural Model
 Prepare a diagram that connect construct based on theory
to visually display the hypotheses that will be tested
 Established Relationship between them by drawing arrows
The construct on the left predict the construct on the right side.
Reputation predict the Customer Loyalty 14
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Reputation
Customer
Loyalty

15. Reputation
Satisfaction
Customer
Loyalty
direct effect
c
a b
indirect effect = a * b
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Reputation Satisfaction
Customer
Loyalty
Reputation
Customer
Loyalty

16. Customer
Satisfaction
Income
Customer
Loyalty
.
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2. Specifying the Measurement Model
 Decide the measurement types (reflective vs
formative)
 Select the indicators to measure a particular construct
 Established Relationship between items and construct
by drawing arrows

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2. Specifying the Measurement Model
CSOR
COMP
ATTR
Csor_1
Csor_2
Csor_4
Csor_3
Csor_5
Attr-1
Attr_2
Attr_3
Comp_1
Comp_2
Comp_3

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2. Specifying the Measurement Model
Comp_1
Comp_2
Comp_3
Like-1
Like_2
Like_3
Comp_1
Comp_2
Comp_3
COMP
LIKE
CUSA CUSLcusa

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3. Collecting and Preparing Data
a) Collecting data using questionnaire

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3. Collecting and Preparing Data
b) Prepare data matrix for the indicators

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3. Collecting and Preparing Data
c) Examine missing data, outliers and data distribution
• Missing data
If less than 5% Tabachnick and Fidell (2007) suggested to use mean
substitution.When the amount > 15% observation is removed from
the data file (Hair, 2017)
• Outliers
Identify outlier, if it is cause by error in data entry  deleted. If there
is an explanation  retained (Hair, 2017)
• Data distribution
Absolutes Skewness & Kurtosis value > 1 indicate of non normal data.

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4. Have SmartPLS Software ready
The software is available free of charge 30 trial at
http://www.smartpls.com

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Contact Me
Firdha_beth@sbm-itb.ac.id

25. Variance Based (PLS-SEM) Covariance Based (CB-SEM)
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