This involves the use of an existing formative latent variable as an exogenous latent variable to predict an endogenous latent variable operationalized through one or more reflectively measured indicators (see Figure 25).ДЇigure 25: Redundancy Analysis for Assessing Convergent Validity To establish convergent validity, a вЂњredundancy analysisвЂќ can be carried out for each latent variable separately. Only remove the indicator if both of its outer weights and outer loadings are not significant. If a particular indicatorвЂ™s outer weight is shown as not significant (i.e., <1.96), check the significance of its outer loading. After running the procedure, check the T-Statistics value as shown in the вЂњOuter WeightsвЂќ window (Bootstrapping Bootstrapping -J Outer Weights ). In SmartPLS, bootstrapping can also be used to test the significance of formative indicatorsвЂ™ outer weight. Marketers should pay attention to those indicators with high outer weights as they are the important area or aspect of the business that should be focused on. Predictive Relevance: The Stone-GeisserвЂ™s (Q 2) ValuesДЇor models with formative measurement scale, the outer weights can be found using the path (PLS -> Calculation Results Outer Weight) after the PLS algorithm is run.Checking Structural Path Significance in Bootstrapping.Inner model path coefficient sizes and significance.Explanation of target endogenous variable variance.

To recap, the followings should be reported if you have a formative measurement model: In terms of the inner вЂњstructuralвЂќ model, we should check and report the same items as shown in previous chapter. Instead, we analyze the modelвЂ™s outer weight, convergent validity, and collinearity of indicators. When working with model that utilizes a formative measurement scale, we do not analyze indicator reliability, internal consistency reliability, or discriminant validity because the formative indicators are not highly correlated together. It means that even initially my path of X->Y ( with No mediation) is insignificant, I can go ahead to model with mediation (X->M->Y).As described earlier in this book, a model does not necessarily have reflective measurements. When Path c is reduced to zero, we have strong evidence for a single, dominant mediator. In regard to the last condition we may envisage a continuum. (c) when Paths a and b are controlled, a previously significant relation between the independent and dependent variables is no longer significant, with the strongest demonstration of mediation occurring when Path c is zero. (a) variations in levels of the independent variable significantly account for variations in the presumed mediator (i.e., Path (b) variations in the mediator significantly account for variations in the dependent variable (i.e., Path b), In the quotes mentioned, I am confused for the third(c) criterion.

I am really thankful to you for clearing my so many doubts. The Moderator-Mediator Variable Distinction in Social Psychological Research: conceptual, strategic, and statistical considerations. вЂњA variable functions as a mediator when it meets the following conditions: (a) variations in levels of the independent variable significantly account for variations in the presumed mediator (i.e., Path a), (b) variations in the mediator significantly account for variations in the dependent variable (i.e., Path b), and (c) when Paths a and b are controlled, a previously significant relation between the independent and dependent variables is no longer significant, with the strongest demonstration of mediation occurring when Path c is zero. In fact the path from X -> Y could be significant,Д«ut when we add the mediator, that coefficients will decrease.

What is bootstrapping in smartpls