A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)SAGE Publications, 29 Feb 2016 - 384 halaman A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) by Joseph F. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt is a practical guide that provides concise instructions on how to use partial least squares structural equation modeling (PLS-SEM), an evolving statistical technique, to conduct research and obtain solutions. Featuring the latest research, new examples using the SmartPLS software, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways. |
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CHAPTER 1 AN INTRODUCTION TO STRUCTURAL EQUATION MODELING | 1 |
CHAPTER 2 SPECIFYING THE PATH MODEL AND EXAMINING DATA | 36 |
CHAPTER 3 PATH MODEL ESTIMATION | 81 |
EVALUATION OF REFLECTIVE MEASUREMENT MODELS | 104 |
EVALUATION OF THE FORMATIVE MEASUREMENT MODELS | 137 |
EVALUATION OF THE STRUCTURAL MODEL | 190 |
CHAPTER 7 MEDIATOR AND MODERATOR ANALYSIS | 227 |
CHAPTER 8 OUTLOOK ON ADVANCED METHODS | 275 |
GLOSSARY | 312 |
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apply assessment ATTR bootstrap bootstrap confidence intervals bootstrap samples CB-SEM Chapter collinearity COMP composite reliability Computational concept confidence interval consider convergent validity corporate reputation correlations criteria criterion Cronbach's alpha CSOR CTA-PLS CUSA CUSL data set discriminant validity endogenous construct endogenous latent variable evaluation example exogenous formative indicators formative measurement models Henseler HTMT indicator variables indirect effect interaction term latent variable scores LOCs measured constructs measurement invariance mediator variable method missing values model estimation model relationships model-implied Modeling window moderating effect moderator variable multigroup analysis observations outer loadings outer weights parameter partial least squares path coefficients PLS path model PLS-SEM algorithm PLS-SEM results PLSc R2 values reflective measurement model regression researchers Ringle Sarstedt scale shown in Exhibit significance level Sobel test specific standard error statistical power structs structural equation modeling structural model subsamples target construct tion total effect two-stage approach variance Y1 and Y2
