I have a 1-1-1 mediation (see below an MSEM code). My problem is that I have a level 2 moderator as well. As far as I could understand, level 2 measures can influence level 2 constructs (between), but not level 1 (within). Am I right so far? If so can someone help with the implementation of that into the code below?
TITLE: 1-1-1 mediation (MSEM) DATA: FILE IS mydata.dat; VARIABLE: NAMES ARE id x m y; USEVARIABLES ARE id x m y; CLUSTER IS id; ANALYSIS: TYPE IS TWOLEVEL RANDOM; MODEL: %WITHIN% m ON x(aw); ! regress m on x, call the slope "aw" y ON m(bw); ! regress y on m, call the slope "bw" y ON x; ! regress y on x %BETWEEN% x m y; ! estimate Level-2 (residual) variances for x, m, and y m ON x(ab); ! regress m on x, call the slope "ab" y ON m(bb); ! regress y on m, call the slope "bb" y ON x; ! regress y on x MODEL CONSTRAINT: ! section for computing indirect effects NEW(indb indw); ! name the indirect effects indw=aw*bw; ! compute the Within indirect effect indb=ab*bb; ! compute the Between indirect effect OUTPUT: TECH1 TECH8 CINTERVAL; ! request parameter specifications, starting values, ! optimation history, and confidence intervals for all effects
I am not into the numbering approach of characterizing two-level mediation modeling, but looking at Bauer et al (2006) in Psych Methods Figure 2, bottom, it looks to me that 1-1-1 mediation has a mediation model on the Within level and unrestricted random effects on the Between level. You pose a model for the random effects on Between. It happens to be a saturated model so it becomes the same as unrestricted.
Then you say "level 2 measures can influence level 2 constructs (between), but not level 1 (within)." Let's scrutinize the second part of that statement. It sounds like you want "2-2-1" as in top of Figure 2. Using an education example, you have a teacher background variable influencing a teacher dependent variable (2-2) which you want to influence a student dependent variable (2-2-1). This is done by letting the teacher dependent variable influence the between-level part of the student variable on Between, that is the random intercept (varying over teachers/classrooms) of the student variable. The model you have written in your Mplus input does this with both M and Y having between-level random intercepts.
I don't see any moderator in your Mplus input. A moderator is an interaction.
You may also want to take a look at the Topic 7 handout on our web site. Slides 44-45 talk about random intercepts and random slopes (cross-level interaction).
Sean Lane posted on Wednesday, August 24, 2011 - 9:47 am
I'm trying to fit a model similar to that listed above (using the Bauer et al syntax), but I get an error message saying that Monte Carlo integration is needed. When I specify Monte Carlo integration I get another error message saying that within-person variables cannot be on the right hand side of an ON statement. However, since I didn't specify any variables as BETWEEN I should be able to use them as WITHIN or BETWEEN, right?
VARIABLE: NAMES ARE myid pol anger attackm; MISSING ARE ALL (-99); USEVARIABLES ARE myid pol anger attackm; CLUSTER IS myid; ANALYSIS: TYPE IS TWOLEVEL RANDOM; !ALGORITHM = INTEGRATION; !INTEGRATION = MONTECARLO; MODEL: %WITHIN% sa | anger ON pol; sb | attackm ON anger; sc | attackm ON pol; %BETWEEN% sa sb sc pol anger attackm; sa WITH sc pol anger attackm; sa WITH sb(cab); sb WITH sc pol anger attackm; sc WITH pol anger attackm; anger ON pol(ab); attackm ON anger(bb); attackm ON pol; [sa](aw); [sb](bw); MODEL CONSTRAINT: NEW(a b indb indw); a=aw+ab; ! compute Between a path b=bw+bb; ! compute Between b path indw=aw*bw+cab; ! compute the Within indirect effect indb=a*b; ! compute the Between indirect effect OUTPUT: TECH1 TECH8 CINTERVAL;