Bengkel smartPLS 2011

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  • 1. Introduction to SmartPLS By: Azwadi Ali Department of Accounting and Finance, Faculty of Management and Economics, Universiti Malaysia Terengganu. FPE, UMT. 23 December 2010.
  • 2. 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.
  • 3. 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:
  • 4. Structural Equation Modeling (SEM)
    • In applied work, structural equation models are most often represented graphically:
  • 5. 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"
  • 6. 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:
    • en d ogenous – d ependent
    • exogenous - independent
  • 7. 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.
  • 8. 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.
  • 9. 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“.
  • 10. 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).
  • 11. Reflective vs Formative
    • See accompanying slides.
  • 12. Reflective vs Formative Example: Computer Self-Efficacy Reflective – 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 Quality Reflective – 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
  • 13. 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.
  • 14. Morning Break (rilek dulu)
  • 15. 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:
  • 16. Research Model
  • 17. 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’
  • 18. Research Model with Indicators
  • 19. Sample Results
  • 20. Model Fit Construct Structural Model Model Quality (H 2 ) (Q 2 ) INT 0.408993 a (0.828074) b 0.336686 c 0.828182 d (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 0.714539 f 0.475505 0.736286 (0.554354) GoF g 0.737555
  • 21. 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.
  • 22. Lunch Break
  • 23. 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.
  • 24. 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.
  • 25. 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).
  • 26. 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 R 2 )].
    • fit statistics for both outer model (H 2 ) and inner model (Q 2 )
    • kindly see the provided examples.
  • 27. End of Workshop