A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)SAGE, 2014 - 307 ˹éÒ A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), by Hair, Hult, Ringle, and Sarstedt, provides a concise yet very practical guide to understanding and using PLS structural equation modeling (PLS-SEM). PLS-SEM is evolving as a statistical modeling technique and its use has increased exponentially in recent years within a variety of disciplines, due to the recognition that PLS-SEM's distinctive methodological features make it a viable alternative to the more popular covariance-based SEM approach. This text—the only comprehensive book available to explain the fundamental aspects of the method—includes extensive examples on SmartPLS software, and is accompanied by multiple data sets that are available for download from the accompanying website (www.pls-sem.com). |
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application assessment ATTR blindfolding bootstrapping CB-SEM Chapter collinearity COMP composite reliability concepts convergent validity corporate reputation correlations CSOR CUSA CUSL data points data set default report dependent variable Diamantopoulos discriminant validity effect size endogenous constructs endogenous latent variable Esposito Vinzi example exogenous explained formative indicators formative measurement models Henseler important indicator variables indirect effect interaction term IPMA latent variable scores LOCs Marketing mediator variable methods missing values model estimation model setup modeling window moderating effect moderator variable multivariate outer loadings outer weights parameter partial least squares path coefficients path relationships PLS path model PLS-MGA PLS-SEM algorithm PLS-SEM results R² values reflective measurement models regression researchers Ringle sample Sarstedt satisfaction scale shown in Exhibit sign change option single-item measures SmartPLS project SmartPLS software specific standard errors statistical statistical power structs structural equation modeling subsamples target construct tion total effects variance