Dear PLS experts
Hope my message find you all well...really in need for advice
I know there has been some post on this issue but could not gain a good idea on what should be done.
I am interested in testing mediating effect of one variable, and
since Baron and Kenny approach has been attacked I wanted to test it using Preacher and Hayes approach. therefore I run there macro on spss using 5000 bootstrap.
the problem is that:
1) their direct and indirect reporting is different from smartpls results (within 5%) so can I ask you why I am getting different results? and
2) in in the first case is it ok to use both smartpls and macro together, knowing that the macros work using OLS??
3) finally if i decided to use smartpls results only what is the approach to use in deciding if these indirect relationships are partial or complete?
thank you for any help i can get.
mediating effect problem

 PLS Junior User
 Posts: 6
 Joined: Thu Aug 16, 2012 9:47 pm
 Real name and title:
 Hengkov
 PLS SuperExpert
 Posts: 1619
 Joined: Sun Apr 24, 2011 10:13 am
 Real name and title: Hengky Latan
 Location: AMQ, Indonesia
 Contact:
Hi,
Used four step approach.
Baron and Kenny (1986), Judd and Kenny (1981), and James and Brett (1984) discussed four steps in establishing mediation:
Step 1: Show that the causal variable is correlated with the outcome. Use Y as the criterion variable in a regression equation and X as a predictor (estimate and test path c in the above figure). This step establishes that there is an effect that may be mediated.
Step 2: Show that the causal variable is correlated with the mediator. Use M as the criterion variable in the regression equation and X as a predictor (estimate and test path a). This step essentially involves treating the mediator as if it were an outcome variable.
Step 3: Show that the mediator affects the outcome variable. Use Y as the criterion variable in a regression equation and X and M as predictors (estimate and test path b). It is not sufficient just to correlate the mediator with the outcome; the mediator and the outcome may be correlated because they are both caused by the causal variable X. Thus, the causal variable must be controlled in establishing the effect of the mediator on the outcome.
Step 4: To establish that M completely mediates the XY relationship, the effect of X on Y controlling for M (path c') should be zero (see discussion below on significance testing). The effects in both Steps 3 and 4 are estimated in the same equation.
Best Regards,
Used four step approach.
Baron and Kenny (1986), Judd and Kenny (1981), and James and Brett (1984) discussed four steps in establishing mediation:
Step 1: Show that the causal variable is correlated with the outcome. Use Y as the criterion variable in a regression equation and X as a predictor (estimate and test path c in the above figure). This step establishes that there is an effect that may be mediated.
Step 2: Show that the causal variable is correlated with the mediator. Use M as the criterion variable in the regression equation and X as a predictor (estimate and test path a). This step essentially involves treating the mediator as if it were an outcome variable.
Step 3: Show that the mediator affects the outcome variable. Use Y as the criterion variable in a regression equation and X and M as predictors (estimate and test path b). It is not sufficient just to correlate the mediator with the outcome; the mediator and the outcome may be correlated because they are both caused by the causal variable X. Thus, the causal variable must be controlled in establishing the effect of the mediator on the outcome.
Step 4: To establish that M completely mediates the XY relationship, the effect of X on Y controlling for M (path c') should be zero (see discussion below on significance testing). The effects in both Steps 3 and 4 are estimated in the same equation.
Best Regards,