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.
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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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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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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
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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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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