A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)SAGE, 2014 - 307 หน้า 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). |
เนื้อหา
01Hair47120 | 1 |
02Hair47120 | 32 |
03Hair47120A | 73 |
04Hair47120 | 95 |
05Hair47120 | 118 |
06Hair47120 | 167 |
07Hair47120 | 205 |
08Hair47120 | 243 |
282 | |
10Author IndexHair47120 | 290 |
293 | |
คำและวลีที่พบบ่อย
86 Sarstedt application assessment ATTR blindfolding bootstrapping CB-SEM Chapter collinearity COMP composite reliability 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 evaluation example exogenous explained f2 effect formative indicators formative measurement models Henseler important indicator variables indicator’s indirect effect interaction term IPMA latent variable scores LOCs mediator variable method missing values model estimation modeling window moderating effect moderator variable multivariate ofthe outer loadings outer weights partial least squares path coefficients path relationships PLS path model PLS-SEM algorithm PLS-SEM results R2 values reflective reflective measurement models regression researchers Ringle sample Sarstedt satisfaction scale shown in Exhibit sign change option SmartPLS project SmartPLS software specific standard errors statistical statistical power structs structural equation modeling target construct total effects variance Y1 and Y2