bengkel smartpls 2011
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Bengkel smartpTRANSCRIPT
Introduction to SmartPLSIntroduction to SmartPLS
By: Azwadi Ali Department of Accounting and Finance,
Faculty of Management and Economics, Universiti Malaysia Terengganu.
FPE, UMT. 23 December 2010.
Introduction to SmartPLS SmartPLS is one of the leading software applications for PLS path
modeling analyses. increasing popularity as an easy, yet powerful, estimation
technique for structural equation models. have been successfully used in the fields of strategic
management, information technology management, media management etc.
e.g. the American customer satisfaction index (ACSI) and the European customer satisfaction index (ECSI).
relatively unrestricted applications especially in SEM situations with hard assumptions of more traditional multivariate statistics.
Structural Equation Modeling (SEM) Structural Equation Modeling (SEM) is used to evaluate both the
structural and measurement model. One well-known framework (popularized by Karl Jöreskog,
University of Uppsala) is depicted by three matrix equations:
Structural Equation Modeling (SEM) In applied work, structural equation models are most often
represented graphically:
Latent Constructs (variables) “latent constructs“ = abstract psychological concepts such as
"intelligence“, "attitude“ and “satisfaction”. can observe the behavior of latent variables only indirectly, and
imperfectly, through their effects on manifest variables (items/dimensions).
two types of latent constructs--exogenous and endogenous. Exogenous constructs are independent variables in all equations
in which they appear. exogenous constructs are indicated by the Greek character "ksi"
Latent Constructs (variables) endogenous constructs are dependent variables in at least one
equation--although they may be independent variables in other equations in the system.
endogenous constructs are indicated by the Greek character "eta“.
Tips to remember: endogenous – dependent exogenous - independent
Structural Model includes the relationships among the latent constructs. Normally linear relationships. one-headed arrows represent regression relationships, while
two-headed arrows represent correlational relations. relations between latent constructs are typically labeled with
"gamma" for the regression of an endogenous construct on an exogenous construct.
or with "beta" for the regression of one endogenous construct on another endogenous construct.
Structural Model Typically in SEM, exogenous constructs are allowed to covary
freely. Parameters labeled with "phi" represent these covariances.
comes from common predictors of the exogenous constructs which lie outside the model under consideration.
Typically also includes a structural error term, labeled with "zeta“.
these error terms are assumed to be uncorrelated with the model's exogenous constructs.
Measurement Model Each latent construct is usually associated with multiple
measures (manifest variables/items/dimensions). Manifest variables associated with exogenous constructs are
labeled X, while those associated with endogenous constructs are labeled Y.
link the latent constructs to their measures through a factor analytic measurement model resulting measures having own loadings.
these "loadings" linking constructs to measures are labeled with "lambda“.
Measurement Model The most common measurement model is the congeneric
measurement model, where each measure is associated with only one latent construct.
All covariation between measures is a consequence of the relations between measures and constructs – hence measures are a ‘reflection’ of latent constructs.
however, it makes more sense to model a latent construct as the result or consequence of its measures – hence called ‘formative’ measures (causal indicators model).
This alternative measurement model is also central to Partial Least Squares (PLS).
Reflective vs Formative
See accompanying slides.
Reflective vs FormativeExample: Computer Self-EfficacyReflective – I am capable at performing tasks on my computer.
I feel confident in my ability to perform computer-related tasks. Formative – I am confident at my ability to perform tasks in MS
Word. I am skillful at using Excel.
Example: System QualityReflective – Overall, I would rate the system quality of the system
highly. The quality of the system is appropriate for my needs.
Formative – Reliability, Ease of Use, Complexity, Accessibility, Responsiveness
Partial Least Squares Partial least squares (PLS) was invented by Herman Wold
(mentor to Karl Jöreskog) as an analytical alternative for situations where theory is weak
and where the available manifest variables or measures would likely not conform to a rigorously specified measurement model (soft modeling).
PLS method is designed to maximize prediction rather than fit. to maximize the proportion of variance of the dependent
"construct" that is explained by the predictor "constructs.“ Some researchers argue that the "latent constructs" in PLS are
not really "latent" at all, since they are strict linear composites of observed variables.
Morning Break(rilek dulu)
Using SmartPLS Please refer to the provided documents/files in the PLS folder. Note that sufficient tutorials and manual is given on the smartpls
website.
Before we start, let’s take a look at a simple case/research selected for this workshop.
Sample questionnaire (representing measures) is given in the PLS folder.
The research model selected for this workshop is as follows:
Research Model
Overall AttitudePerceptual Antecedents (cognitive evaluative)
Behavioural Outcome
Information Usefulness
Attitude towards IR Websites
Intention to Re-use
Usability
Attractiveness
Research hypotheses
H1: ‘Information usefulness’ is positively related to ‘attitude towards IR Websites’
H2: ‘Usability’ is positively related to ‘attitude towards IR Websites’
H3: ‘Attractiveness’ is positively related to ‘attitude towards IR Websites’
H4: ‘Attitude towards IR Websites’ is positively related to ‘intention to re-use IR Website’
Research Model with Indicators
IU
USB
ATR
AT_ST INT
IQ
CRD
IQ1
IQ3
IQ4
IQ7
IQ8
IQ9
CRD2
CRD4
CRD5
CRD6
USB1
USB2
USB3
USB5
USB6
USB7
ATR1
ATR2
ATR3
ATR5
ATR6
COG AFT
COG1
COG2
COG3 COG4
COG5
COG6
AFT1
AFT2
AFT3
INT1
INT2
INT3
INT4
Sample Results
Information Usefulness
Attitude towards IR Websites
(σ2 = .784)
Intention to Re-use
(σ2 = .409)
γ = 0.341t = 2.865
γ = 0.297t = 2.425
γ= 0.311
t = 5.030
β = 0.640t = 8.873Usability
Attractiveness
Model FitConstruct Structural Model
Model Quality
(H2) (Q2)
INT 0.408993a (0.828074)b 0.336686c 0.828182d (0.332465)e
AT_ST 0.784225 (0.685600) 0.271908 0.685665 (0.532704)
IU 0.995844 (0.594612) 0.456906 0.602816 (0.603847)
COG 0.938884 (0.790545) 0.742018 0.790584 (0.737104)
AFT 0.678615 (0.847454) 0.570006 0.847526 (0.565652)
IQ - (0.687052) - 0.705153 (-)
CRD - (0.558615) - 0.564710 (-)
USB - (0.789696) - 0.789634 (-)
ATR - (0.812278) - 0.812301 (-)
Average 0.761312 0.714539f 0.475505 0.736286 (0.554354)
GoFg 0.737555
A simple application of SmartPLS
First – open the smartPLS – you’ll be prompted to re-activate the software if your key has expired after three months, otherwise you will see the window for the program.
Close the ‘welcome’ sub-window. Choose ‘file’>’new’>’create new project’, then follow the
facilitator’s guide.
Lunch Break
Testing the model
To test the model, we normally follow the two-step method (Anderson & Gerbing, 1988) – evaluate the results of measurement model, followed by the structural model.
Measurement model - convergent For constructs with reflective measures (i.e. latent constructs),
one examines the loadings, which can be interpreted in the same manner as the loadings in a principal component analysis.
For constructs using formative measures (i.e. emergent constructs), the weights provide information as to what the makeup and relative importance are for each indicator in the creation/formation of the component.
Individual reflective item reliability is considered adequate when an item has a factor loading that is greater than 0.707 on its respective construct.
The internal consistency for a given block of indicators is assessed using the composite reliability.
Nunnally (1978) suggests 0.7 as a benchmark for a modest reliability applicable.
Measurement model - discriminant A model is also said to converge when Average variance
extracted (AVE) (Fornell and Larcker, 1981) is greater than 0.50 meaning that 50 per cent or more variance of the indicators should be accounted for.
AVE assesses the amount of variance that a construct captures from its indicators relative to the amount due to measurement error.
Discriminant validity indicates the extent to which a given construct is different from other latent variables.
AVE should be greater than the variance shared between the latent construct and other latent constructs in the model (i.e. the squared correlation between two constructs) (Barclay et al., 1995).
Structural model Structural model of a model is assessed by Path coefficients (γ &
β), t-values, and the variance explained (R 2) in the dependent constructs
Support for each general hypothesis on both samples can be determined by examining the sign and statistical significance of the t-values.
Goodness of Fit GoF is given by √ [(average communality) x (average R2)]. fit statistics for both outer model (H2) and inner model (Q2) kindly see the provided examples.
End of Workshop