PLS-SEM

Partial Least Squares-SEM

ลักษณะของ PLS

1. Partial Least Squares is just like Principal Component (PC) Regression except in how the  component scores are comput

 2. PC regression = weights are calculated from the covariance matrix of the predictors- PLS = weights reflect the covariance structure between predictors and response

3. While conceptually not too much of a stretch, it requires a more complicated iterative algorithm

           4. Like in regression, the goal is to maximize the correlation between the response(s) and component scores

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กล่าวโดยสรุปการประเมินผล PLS-SEM

Reflective Measurement Models	Formative Measurement Models
1.     Internal consistency (composite reliability) ร่วมกับ Cronbachs Alpha)  2.     Indicator reliability 3.     Convergent validity (AVE > 0.5) 4.     Discriminant validity (Fornell-Lacker Analysis)	1.  Convergent validity (R2 แต่ละตัว)           2. Collinearity among indicators (VIF)         3.   Significance and Relavance od outer weights
1.     Coefficients of determination (R2 = จำนวนตัวแปรตาม) 2.     Predictive relevance (Q2) 3.     Size and significance of path coefficients 4.     f2 effect sizes 5.     q2 effect sizes (if necessary)

Source: Hair, J. F. Jr., Hult, G. T. M., Ringle, C.M.,Sarstedt, M. (2013) A Premier Partial Least Squares Structural Equation Modeling (PLS-SEM). London: Sagepublications.com