This document discusses structural equation modeling (SEM) and partial least squares SEM (PLS-SEM). It provides an overview of the key differences between covariance-based SEM and PLS-SEM, including their objectives, assumptions, strengths, and evaluation. It also discusses important considerations for using SEM such as data characteristics, model specification, and the systematic process of applying PLS-SEM. Guidelines are provided for determining whether PLS-SEM or CB-SEM is best suited for a given research question and study.

1. Joe F. Hair, Jr.Joe F. Hair, Jr.
Founder & Senior Scholar, DBAFounder & Senior Scholar, DBA
ProgramProgram
Joe F. Hair, Jr.Joe F. Hair, Jr.
Founder & Senior Scholar, DBAFounder & Senior Scholar, DBA
ProgramProgram
PLS-SEMPLS-SEM

2. Sewall Wright, Correlation and Causation,Sewall Wright, Correlation and Causation, JournalJournal
of Agricultural Researchof Agricultural Research, Vol. XX, No. 7, 1921., Vol. XX, No. 7, 1921.
SEM Model:SEM Model:
Predicting the Birth WeightPredicting the Birth Weight
of Guinea Pigsof Guinea Pigs
X & Y = different outcomesX & Y = different outcomes
B, C & D = common causesB, C & D = common causes
A & E = independent causesA & E = independent causes

3. The greatest interest in any factor solution centers on the correlations between the original
variables and the factors. The matrix of such test-factor correlations is called the factor structure,
and it is the primary interpretative device in principal components analysis. In the factor
structure the element rjk gives the correlation of the jth test with the kth factor. Assuming that the
content of the observation variables is well known, the correlations in the kth column of the
structure help in interpreting, and perhaps naming, the kth factor. Also, the coefficients in the jth
row give the best view of the factor composition of the jth test.
Another set of coefficients of interest in factor analysis is the weights that compound predicted
observations z from factor scores f. These regression coefficients for the multiple regression of
each element of the observation vector z on the factor f are called factor loadings and the matrix
A that contains them as its rows is . . . . .
Source: Cooley, William W., and Paul R. Lohnes, Multivariate Data Analysis, John Wiley & Sons,
Inc., New York, 1971, page 106.
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4. Structural Equations ModelingStructural Equations Modeling
What comes to mind?What comes to mind?
CB-SEMCB-SEM
LISRELLISREL
AMOS ?AMOS ?
PLS-SEM
PLS-SEM

5. CB-SEM (Covariance-based SEM)CB-SEM (Covariance-based SEM) ––
objective is to reproduce the theoreticalobjective is to reproduce the theoretical
covariance matrix, without focusing oncovariance matrix, without focusing on
explained variance.explained variance.
PLS-SEM (Partial Least Squares SEM)PLS-SEM (Partial Least Squares SEM)
– objective is to maximize the explained– objective is to maximize the explained
variance of the endogenous latentvariance of the endogenous latent
constructs (dependent variables).constructs (dependent variables).

6. CB-SEM ModelCB-SEM Model
HBAT,HBAT, MDAMDA databasedatabase

7. Covariance Matrix = HBAT 3-Construct modelCovariance Matrix = HBAT 3-Construct model

8. CB-SEMCB-SEM – evaluation focuses on goodness of– evaluation focuses on goodness of
fit = minimization of the difference betweenfit = minimization of the difference between
the observed covariance matrix and thethe observed covariance matrix and the
estimated covariance matrix.estimated covariance matrix.
Research objective: testing and confirmation whereResearch objective: testing and confirmation where
prior theory is strong.prior theory is strong.
• Assumes normality of data distribution,Assumes normality of data distribution,
homoscedasticity, large sample size, etc.homoscedasticity, large sample size, etc.
• Only reliable and valid variance is useful for testingOnly reliable and valid variance is useful for testing
causal relationships.causal relationships.
• A “full information approach” which means smallA “full information approach” which means small
changes in model specification can result inchanges in model specification can result in
substantial changes in model fit.substantial changes in model fit.

9. PLS-SEMPLS-SEM – objective is to maximize the– objective is to maximize the
explained variance of the endogenousexplained variance of the endogenous
latent constructs (dependent variables).latent constructs (dependent variables).
Research objective: theory development andResearch objective: theory development and
prediction.prediction.
• Normality of data distribution not assumed.Normality of data distribution not assumed.
• Can be used with fewer indicator variables (1 or 2)Can be used with fewer indicator variables (1 or 2)
per construct.per construct.
• Models can include a larger number of indicatorModels can include a larger number of indicator
variables (CB-SEM difficult with 50+ items).variables (CB-SEM difficult with 50+ items).
• Preferred alternative with formative constructs.Preferred alternative with formative constructs.
• Assumes all measured variance (including error) isAssumes all measured variance (including error) is
useful for explanation/prediction of causaluseful for explanation/prediction of causal
relationships.relationships.

10. PLS Path ModelPLS Path Model
X1
X2
X3
X4
X5
X6
X7
Y2
Y1
Y3
W1
W2
W3
W4
W5
W6
W7
P1
P2
IndicatorVariable
LatentVariableLatent ConstructLatent Construct

11. Multivariate MethodsMultivariate Methods

12. Should SEM Be Used?Should SEM Be Used?
Considerations:Considerations:
1.1.The VariateThe Variate
2.2.Multivariate MeasurementMultivariate Measurement
3.3.Measurement ScalesMeasurement Scales
4.4.CodingCoding
5.5.Data DistributionData Distribution

13. Variate =Variate = a linear combination of several variables,a linear combination of several variables,
often referred to as the fundamental building blockoften referred to as the fundamental building block
of multivariate analysis.of multivariate analysis.
Variate value = xVariate value = x11ww11 + x+ x22ww22 + . . . + x+ . . . + xkkwwkk
Data MatrixData Matrix

14. Multiple Regression ModelMultiple Regression Model
Variate = xVariate = x11 + x+ x22 + x+ xkk + e+ e

15. Multivariate MeasurementMultivariate Measurement
Measurement = the process of assigning numbers to aMeasurement = the process of assigning numbers to a
variable/construct based on a set of rules that are used to assignvariable/construct based on a set of rules that are used to assign
the numbers to the variable in a way that accurately represents thethe numbers to the variable in a way that accurately represents the
variable.variable.
When variables are difficult to measure, one approach is toWhen variables are difficult to measure, one approach is to
measure them indirectly with proxy variables. If the concept ismeasure them indirectly with proxy variables. If the concept is
restaurant satisfaction, for example, then the several proxyrestaurant satisfaction, for example, then the several proxy
variables that could be used to measure this might be:variables that could be used to measure this might be:
1.1.The taste of the food was excellent.The taste of the food was excellent.
2.2.The speed of service met my expectations.The speed of service met my expectations.
3.3.The wait staff was very knowledgeable about the menu items.The wait staff was very knowledgeable about the menu items.
4.4.The background music in the restaurant was pleasant.The background music in the restaurant was pleasant.
5.5.The meal was a good value compared to the price.The meal was a good value compared to the price.
Multivariate measurement involves using several variables toMultivariate measurement involves using several variables to
indirectly measure a concept, as in the restaurant satisfactionindirectly measure a concept, as in the restaurant satisfaction
example above. It also enables researchers to account for the errorexample above. It also enables researchers to account for the error
in data.in data.

16. Data Characteristics – PLS-SEMData Characteristics – PLS-SEM
Sample Size • No identification issues with small sample sizes (35-50).
• Generally achieves high levels of statistical power with small
sample sizes (35-50).
• Larger sample sizes (250+) increase the precision
(i.e., consistency) of PLS-SEM estimations.
Data
Distribution
• No distributional assumptions (PLS-SEM is a non-parametric
method; works well with extremely non-normal data).
Missing
Values
• Highly robust as long as missing values are below
reasonable level (e.g., up to 15% randomly missing data
points).
• Use mean replacement (sub-groups) and nearest neighbor.
Measurement
Scales
• Works with metric, quasi-metric (ordinal) scaled data, and
binary coded variables (~only exogenous variables).
• Limitations when using categorical data to measure
endogenous latent variables.
• Suggest using binary variables for multi-group comparisons.

17. Model Characteristics – PLS-SEMModel Characteristics – PLS-SEM
Number of Items
in each Construct
Measurement
Model
• Handles constructs measured with single and
multi-item measures.
• Easily handles 50+ items (CB-SEM does not).
• Single item scales OK.
Relationships
between Latent
Constructs and
their Indicators
• Easily incorporates reflective and formative
measurement models.
Model
Complexity
• Handles complex models with many structural
model relationships.
• Larger numbers of indicators are helpful in
reducing “consistency at large”.
Model Set-up • Causal loops not allowed in the structural model
(only recursive models).

18. Algorithm Properties – PLS-SEMAlgorithm Properties – PLS-SEM
Objective • Minimizes the amount of unexplained variance (i.e.,
maximizes the R² values).
Efficiency • Converges after a few iterations (even in situations with
complex models and/or large sets of data) to the global
optimum solution; efficient algorithm.
Latent
Construct
Scores
• Estimated as linear combinations of their indicators.
• Used for predictive purposes.
• Can be used as input for subsequent analyses.
• Not affected by data inadequacies.
Parameter
Estimates
• Structural model relationships underestimated (PLS-
SEM bias).
• Measurement model relationships overestimated (PLS-
SEM bias).
• Consistency at large (minimal impact with N = 250+).
• High levels of statistical power with smaller sample
sizes (35-50).

19. Model Evaluation Issues – PLS-SEMModel Evaluation Issues – PLS-SEM
Evaluation of
Overall Model
• No global goodness-of-fit criterion.
Evaluation of
Measurement
Models
• Reflective measurement models: reliability and
validity assessments by multiple criteria.
• Formative measurement models: validity
assessment, significance of path coefficients,
multicollinearity.
Evaluation of
Structural
Model
• Significance of path coefficients, coefficient of
determination (R²), pseudo F-test (f² effect size),
predictive relevance (Q² and q² effect size).
Additional
Analyses
• Mediating effects
• Impact-performance matrix analysis
• Higher-order constructs
• Multi-group analysis
• Measurement mode invariance
• Moderating effects
• Uncovering unobserved heterogeneity: FIMIX-PLS

20. Rules of Thumb: PLS-SEM or CB-SEM?Rules of Thumb: PLS-SEM or CB-SEM?
Use PLS-SEM when:Use PLS-SEM when:
•The goal is predicting key target constructs or identifyingThe goal is predicting key target constructs or identifying
key “driver” constructs.key “driver” constructs.
•Formative constructs are easy to use in the structuralFormative constructs are easy to use in the structural
model. Note that formative measures can also be used withmodel. Note that formative measures can also be used with
CB-SEM, but doing so requires construct specificationCB-SEM, but doing so requires construct specification
modifications (e.g., the construct must include bothmodifications (e.g., the construct must include both
formative and reflective indicators to meet identificationformative and reflective indicators to meet identification
requirements).requirements).
•The structural model is complex (many constructs andThe structural model is complex (many constructs and
many indicators).many indicators).
•The sample size is small and/or the data is not-normallyThe sample size is small and/or the data is not-normally
distributed, or exhibits heteroskedasticity.distributed, or exhibits heteroskedasticity.
•The plan is to use latent variable scores in subsequentThe plan is to use latent variable scores in subsequent
analyses.analyses.

21. Use CB-SEM when:Use CB-SEM when:
•The goal is theory testing, theoryThe goal is theory testing, theory
confirmation, or the comparison of alternativeconfirmation, or the comparison of alternative
theories.theories.
•Error terms require additional specification,Error terms require additional specification,
such as the covariation.such as the covariation.
•Structural model has non-recursiveStructural model has non-recursive
relationships.relationships.
•Research requires a global goodness of fitResearch requires a global goodness of fit
criterion.criterion.
Rules of Thumb: PLS-SEM or CB-SEMRules of Thumb: PLS-SEM or CB-SEM

22. Specifying the Structural Model
Specifying the Measurement Models
Data Collection and Examination
PLS-SEM Model Estimation
Assessing PLS-SEM Results for Reflective
Measurement Models
Assessing PLS-SEM Results for Formative
Measurement Models
Assessing PLS-SEM Results for the Structural
Model
Interpretation of Results and Drawing Conclusions
Stage 1
Stage 2
Stage 3
Stage 4
Stage 5a
Stage 5b
Stage 6
Stage 7
Systematic Process for applying PLS-SEMSystematic Process for applying PLS-SEM

23. Should You Use SEM?Should You Use SEM?
Journal reviewers rate SEM papers more favorablyJournal reviewers rate SEM papers more favorably
on key manuscript attributes . .on key manuscript attributes . . ..
Mean ScoreMean Score
AttributesAttributes SEMSEM No SEMNo SEM p-valuep-value
Topic RelevanceTopic Relevance 4.24.2 3.83.8 .182.182
Research MethodsResearch Methods 3.53.5 2.72.7 .006.006
Data AnalysisData Analysis 3.53.5 2.82.8 .025.025
ConceptualizationConceptualization 3.13.1 2.52.5 .018.018
Writing QualityWriting Quality 3.93.9 3.03.0 .006.006
ContributionContribution 3.13.1 2.82.8 .328.328
Note: scores based on 5-point scale, with 5 = more favorableNote: scores based on 5-point scale, with 5 = more favorable
Source: Babin, Hair & Boles, Publishing Research in Marketing JournalsSource: Babin, Hair & Boles, Publishing Research in Marketing Journals
Using Structural Equation Modeling,Using Structural Equation Modeling, Journal of Marketing Theory andJournal of Marketing Theory and
PracticePractice, Vol. 16, No. 4, 2008, pp. 281-288., Vol. 16, No. 4, 2008, pp. 281-288.

24. PLS-SEM Stages 1, 2 & 3: Design IssuesPLS-SEM Stages 1, 2 & 3: Design Issues
1.1. Scale MeasuresScale Measures
• Scale selection/designScale selection/design
• Reflective vs. FormativeReflective vs. Formative
2.2. Common Methods VarianceCommon Methods Variance
• Harmon Single Factor TestHarmon Single Factor Test
• Common Latent FactorCommon Latent Factor
• Marker ConstructMarker Construct
3.3. Missing Data, outliers, etc.Missing Data, outliers, etc.

25. Scale DesignScale Design
1.1. Revise/UpdateRevise/Update
• Established scales – how old?Established scales – how old?
• Double barreled; negatively wordedDouble barreled; negatively worded
2.2. Number of Scale PointsNumber of Scale Points
• More scale points = greater variabilityMore scale points = greater variability
3.3. Single Item ScalesSingle Item Scales

26. Single Item Scales ?Single Item Scales ?
Single-item measures Multi-item measures
Theoretical
Aspects
Reliability
• no adjustment of
random error
• assessing reliability
is problematic
• allows for random error
adjustment
• determination of reliability by
means of internal
consistency
Validity
• lower construct
validity – does not
account for all facets
of a construct
• decreased criterion
validity
• assessing validity is
more problematic
• higher construct validity –
different facets of a
construct can be captured
• increased criterion validity
• validity measures based on
item-to-item correlations
Partition-
ing
• Partitioning solely
based on the single
variable
• more precise partition
• possible
Missing
Values
• very difficult to
resolve
• imputation methods based
on correlations between
indicators of the same
construct
Use in
Academic
Research
• very uncommon
(publication
problematic)
• generally accepted

27. Single Item Scales ?Single Item Scales ?
Single-item measures Multi-item measures
Practical
Aspects
Costs
• lower costs associated
with scale development,
questioning, and data
analysis
• higher costs associated with
scale development,
questioning, and data
analysis
Non-
response
• increased survey
response rate
• lower item nonresponse
• lower survey response rate
• higher item nonresponse
Burden
of
Question
-ing
• little burden: simple,
fast, and
comprehensible
• increased burden: longer,
likely more boring and tiring

28. Reflective (Scale) Versus FormativeReflective (Scale) Versus Formative
(Index) Operationalization of Constructs(Index) Operationalization of Constructs
A central research question in social science research, particularly marketingA central research question in social science research, particularly marketing
and MIS, focuses on the operationalization of complex constructs:and MIS, focuses on the operationalization of complex constructs:
Are indicators causing or being caused byAre indicators causing or being caused by
the latent variable/construct measured by them?the latent variable/construct measured by them?
Construct
Indicator 1 Indicator 2 Indicator 3
Construct
Indicator 1 Indicator 2 Indicator 3
?
Changes in the latent variableChanges in the latent variable
directly cause changes in thedirectly cause changes in the
assigned indicatorsassigned indicators
Changes in one or more of theChanges in one or more of the
indicators causes changes inindicators causes changes in
the latent variablethe latent variable

29. Example: Reflective vs. Formative WorldExample: Reflective vs. Formative World
ViewView
DrunkennessDrunkenness
Can’t walk a straightCan’t walk a straight
lineline
Smells of alcoholSmells of alcohol
Slurred speechSlurred speech

30. Example: Reflective vs. Formative World ViewExample: Reflective vs. Formative World View
DrunkennessDrunkenness
Consumption of beerConsumption of beer
Consumption of wineConsumption of wine
Consumption of hardConsumption of hard
liquorliquor

31. Basic Difference Between Reflective andBasic Difference Between Reflective and
Formative Measurement ApproachesFormative Measurement Approaches
““Whereas reflective indicators are essentially interchangeable (andWhereas reflective indicators are essentially interchangeable (and
therefore the removal of an item does not change the essentialtherefore the removal of an item does not change the essential
nature of the underlying construct), with formative indicatorsnature of the underlying construct), with formative indicators
‘omitting an indicator is omitting a part of the construct’.”‘omitting an indicator is omitting a part of the construct’.”
(DIAMANTOPOULOS/WINKLHOFER, 2001, p. 271)(DIAMANTOPOULOS/WINKLHOFER, 2001, p. 271)
TheThe reflective measurementreflective measurement approachapproach
focuses onfocuses on maximizingmaximizing thethe overlapoverlap
between interchangeable indicatorsbetween interchangeable indicators
TheThe formative measurementformative measurement approachapproach
generallygenerally minimizesminimizes thethe overlapoverlap
between complementary indicatorsbetween complementary indicators
ConstructConstruct
domaindomain
ConstructConstruct
domaindomain

32. Exercise: Satisfaction in Hotels as FormativeExercise: Satisfaction in Hotels as Formative
and Reflective Operationalized Constructand Reflective Operationalized Construct
I am comfortable withI am comfortable with
this hotelthis hotel
I appreciate this hotelI appreciate this hotel
I am looking forward toI am looking forward to
staying overnight instaying overnight in
this hotelthis hotel
The rooms‘ furnishingsThe rooms‘ furnishings
are goodare good
The rooms are quietThe rooms are quiet
The hotel‘s personnelThe hotel‘s personnel
are friendlyare friendly
The hotel’s service isThe hotel’s service is
goodgood
The hotel’s cuisine isThe hotel’s cuisine is
goodgood
The hotel’s recreationThe hotel’s recreation
offerings are goodofferings are good
The rooms are cleanThe rooms are clean
Taking everything intoTaking everything into
account, I am satisfiedaccount, I am satisfied
with this hotelwith this hotel
The hotel is low-pricedThe hotel is low-priced
SatisfactionSatisfaction
with Hotelswith Hotels

33. Formative Constructs – Two TypesFormative Constructs – Two Types
1.1. Composite (formative) constructsComposite (formative) constructs –– indicators completelyindicators completely
determine the “latent” construct. They share similarities becausedetermine the “latent” construct. They share similarities because
they define a composite variable but may or may not havethey define a composite variable but may or may not have
conceptual unity. In assessing validity, indicators are notconceptual unity. In assessing validity, indicators are not
interchangeable and should not be eliminated, because removinginterchangeable and should not be eliminated, because removing
an indicator will likely change the nature of the latent construct.an indicator will likely change the nature of the latent construct.
2.2. Causal constructsCausal constructs –– indicators have conceptual unity in thatindicators have conceptual unity in that
all variables should correspond to the definition of the concept. Inall variables should correspond to the definition of the concept. In
assessing validity some of the indicators may beassessing validity some of the indicators may be
interchangeable, and also can be eliminated.interchangeable, and also can be eliminated.
Bollen, K.A. (2011), Evaluating Effect, Composite, and Causal Indicators inBollen, K.A. (2011), Evaluating Effect, Composite, and Causal Indicators in
Structural Equations Models,Structural Equations Models, MIS QuarterlyMIS Quarterly, Vol. 35, No. 2, pp. 359-372., Vol. 35, No. 2, pp. 359-372.

34. PLS-SEM ExamplePLS-SEM Example
CUSLCUSA
LIKE
COMP

35. Reflective Measurement
Model
Reflective Measurement
Model
Single-Item Construct
Reflective Measurement
Model
Types of Measurement ModelsTypes of Measurement Models
PLS-SEM ExamplePLS-SEM Example

36. Indicators for SEM Model ConstructsIndicators for SEM Model Constructs
Competence (COMP)
comp_1 [company] is a top competitor in its market.
comp_2 As far as I know, [company] is recognized world-wide.
comp_3 I believe that [company] performs at a premium level.
Likeability (LIKE)
like_1 [company] is a company that I can better identify with than other companies.
like_2 [company] is a company that I would regret more not having if it no longer
existed than I would other companies.
like_3 I regard [company] as a likeable company.
Customer Loyalty (CUSL)
cusl_1 I would recommend [company] to friends and relatives.
cusl_2 If I had to choose again, I would chose [company] as my mobile phone services
provider.
cusl_3 I will remain a customer of [company] in the future.
Satisfaction (CUSA)
cusa If you consider your experiences with [company] how satisfied are you with
[company]?

37. Data Matrix for Indicator VariablesData Matrix for Indicator Variables
Column Number and Variable Name
Case
Number
1 2 3 4 5 6 7 8 9 10
comp_1 comp_2 comp_3 like_1 like_2 like_3 cusl_1 cusl_2 cusl_3 cusa
1 4 5 5 3 1 2 5 3 3 5
2 6 7 6 6 6 6 7 7 7 7
. . .
344 6 5 6 6 7 5 7 7 7 7

38. Getting Started with the SmartPLS SoftwareGetting Started with the SmartPLS Software
The next slide shows the graphical interface for the SmartPLS
software, with the simple model already drawn. We describe in
the following slides how to set up this model using the SmartPLS
software program. Before you draw your model, you need to
have data that serves as the basis for running the model. The
data we will use to run our example PLS model can be
downloaded either as comma separated values (.csv) or text (.txt)
data files at the following URL: http://www.smartpls.de/cr/. When
you get to the website scroll down to the Corporate Reputation
Example where it says Click on the following links to downloadClick on the following links to download
filesfiles..
SmartPLS can use both data file formats (i.e., .csv or .txt).
Follow the onscreen instructions to save one of these two files
on your hard drive. Click on Save Target As… to save the data to
a folder on your hard drive, and then Close. Now go to the folder
where you previously downloaded and saved the SmartPLS
software on your computer. Click on the file that runs SmartPLS
( ) and then on the Run tab to start the software. You are
now ready to create a new SmartPLS project.

39. SmartPLS Graphical InterfaceSmartPLS Graphical Interface

40. Example with Names and Data AssignedExample with Names and Data Assigned

41. Brief Instructions: Using SmartPLSBrief Instructions: Using SmartPLS
1.1. Load SmartPLS software – click onLoad SmartPLS software – click on
2.2. Create your new project – assign name and data.Create your new project – assign name and data.
3.3. Double-click to get Menu Bar.Double-click to get Menu Bar.
4.4. Draw model – see options below:Draw model – see options below:
• Insertion mode =Insertion mode =
• Selection mode =Selection mode =
• Connection mode =Connection mode =
5.5. Save model.Save model.
6.6. Click on calculate icon and select PLS algorithm onClick on calculate icon and select PLS algorithm on
the Pull-Down menu. Now accept the default options bythe Pull-Down menu. Now accept the default options by
clicking Finish.clicking Finish.

42. To create a new project, click on → File → New → Create New Project.To create a new project, click on → File → New → Create New Project.
The screen below will appear. Type a name in the window. ClickThe screen below will appear. Type a name in the window. Click
Next.Next.

43. You now need to assign a data file to the project, in our case, data.csv (orYou now need to assign a data file to the project, in our case, data.csv (or
whatever name you gave to the data you downloaded). To do so, click onwhatever name you gave to the data you downloaded). To do so, click on
the dots tab (…) at the right side of the window, find and highlight your datathe dots tab (…) at the right side of the window, find and highlight your data
folder, and click Open to select your data. Once you have specified the datafolder, and click Open to select your data. Once you have specified the data
file, click on Finish.file, click on Finish.

44. SmartPLS Software OptionsSmartPLS Software Options
Find your new project in window, expand list of projects to get projectFind your new project in window, expand list of projects to get project
details (see below), click on the .splsm file for your projectdetails (see below), click on the .splsm file for your project

45. Double click on your new model to get the menuDouble click on your new model to get the menu
bar to appear at the top of the screen.bar to appear at the top of the screen.
Selection modeSelection mode
Draw constructsDraw constructs
Draw structural pathsDraw structural paths

46. Initial Structural Model – No Indicator VariablesInitial Structural Model – No Indicator Variables

47. Structural Model with Names and PathsStructural Model with Names and Paths

48. Name Constructs, Align Indicators, Etc. . . .Name Constructs, Align Indicators, Etc. . . .
 Start calculation
Change reflective to formative
Show measurement model
Rename Construct
Hide used indicators

49. How to Run SmartPLS SoftwareHow to Run SmartPLS Software

50. Default Settings for Example – Click Finish to runDefault Settings for Example – Click Finish to run
Trade-off in missing valueTrade-off in missing value
treatment:treatment:
Case wise replacement canCase wise replacement can
greatly reduce the number ofgreatly reduce the number of
cases but sample meancases but sample mean
imputation reduces variables’imputation reduces variables’
variance.variance.
 Preferred approach to dealPreferred approach to deal
with missing data is combinationwith missing data is combination
of sub-group and nearestof sub-group and nearest
neighbor, or use EM imputationneighbor, or use EM imputation
using SPSS.using SPSS.
Always use path weighting schemeAlways use path weighting scheme

51. PLS Results for ExamplePLS Results for Example

52. SmartPLS Calculation Reports – OverviewSmartPLS Calculation Reports – Overview

53. Quality Criteria Report – SmartPLSQuality Criteria Report – SmartPLS
The composite reliability isThe composite reliability is
excellent – almost .90 for allexcellent – almost .90 for all
three constructs.three constructs.
The AVEs for all three constructs areThe AVEs for all three constructs are
well above .50.well above .50.

54. Summary of PLS-SEM FindingsSummary of PLS-SEM Findings
1.1.The direct path from COMP to CUSA is 0.162 and the direct pathThe direct path from COMP to CUSA is 0.162 and the direct path
from COMP to CUSL is 0.009.from COMP to CUSL is 0.009.
2.2.The direct path from LIKE to CUSA is 0.424 and the direct pathThe direct path from LIKE to CUSA is 0.424 and the direct path
from LIKE to CUSL is 0.342.from LIKE to CUSL is 0.342.
3.3.The direct path from CUSA to CUSL is 0.504.The direct path from CUSA to CUSL is 0.504.
4.4.Overall, the model predicts 29.5% of the variance in CUSA, andOverall, the model predicts 29.5% of the variance in CUSA, and
56.2% of the variance in CUSL.56.2% of the variance in CUSL.
5.5.Reliability of constructs is excellent.Reliability of constructs is excellent.
6.6.Constructs achieve convergent validity (AVE > 0.50)Constructs achieve convergent validity (AVE > 0.50)
To determine significance levels, you must run BootstrappingTo determine significance levels, you must run Bootstrapping
option. Look for under the calculate option.option. Look for under the calculate option.