I am analyzing 2-1-1 path model with binary mediator (intent2) and outcome (condom) by using MLR. In the results, odds ratios were only given for intent2->condom and no odds ratios were given for independent variables -> intent2. May I ask how I obtain the odds ratios of independent variables? and in this case, is it ok to use MLR instead of other estimators? I copied my syntax below: VARIABLE: NAMES ARE caseid weight cdattit hivsus norm intent primary condom intent2; USEVARIABLES ARE caseid weight cdattit hivsus norm intent primary condom intent2 inter; CATEGORICAL IS condom intent2; BETWEEN IS cdattit hivsus norm; WITHIN IS primary inter; CLUSTER IS caseid; WEIGHT IS weight; DEFINE:inter = primary*intent2; ANALYSIS: TYPE IS TWOLEVEL; MODEL: %WITHIN% condom ON intent2(b) primary inter; %BETWEEN% cdattit hivsus norm intent2 condom; intent2 ON cdattit(a); intent2 ON norm(c); intent2 ON hivsus(d); condom ON cdattit; condom ON norm; condom ON hivsus; MODEL CONSTRAINT: NEW(indb indb1 indb2); indb=a*b; indb1=c*b; indb2=d*b;
Hello I am interested in a 2 (cdep) -2 (ccoll)-1 (fiver) path, with moderation between the level 2 variables by another variable (cbot). Covariates age, male and ethnic are all categorical at L1, weight (continuous) at level 2. My drinking outcome is binary, but my level 2 mediator and predictor are both continuous. My code is: categorical = fiver; usevariables are ngh fiver male ses ethnic ccoll cbot cdep wgtbot INT2; missing are all (-9999); cluster is ngh; between is ccoll cbot cdep wgtbot INT2; !centered except wgtbot within = male ses ethnic ; define:CENTER wgtbot(GRANDMEAN) ; define: int2 =cbot*cdep; ANALYSIS: TYPE = twolevel; estimator=wlsmv; MODEL: %WITHIN% fiver on male ses ethnic ; %BETWEEN% fiver ON ccoll(b); fiver on cdep (cp1);
ccoll on cdep (a1); ccoll on cbot ; ccoll on wgtbot; ccoll ON INT2 (bb);
MODEL CONSTRAINT: new (dp_col_int wmodval dep_coll depdir); dp_col_int = (a1+bb*wmodval)*b; wmodval = 0; dep_coll = a1*b; depDIR = cp1;
Is this the correct estimator? I have assumed I use the raw data, and perhaps it is this assumption I have incorrect. Thank you so much
I am getting very different results between MLR and WLSMV. I have read through many of the discussion pieces on estimators and read the choice of estimators pdf.
My binary outcome has a 90/10 split (only 10% have the outcome) and my mediators and independent variables (both at level 2) are very non-normal. Is it likely that it is these characteristics which are producing different results? Some discussion pages say WSLMV is more robust to non-normality, other pages say less. My sample size is 4267.
Thank you so much for your incredibly prompt response. It is wonderful, especially being in a small country with not many MPlus users.
Sorry, I meant to say that my comparisons were relating to the patterns of significance. My results with my non-normal independent variables are all non-significant in WLSMV and all significant in ML and MLR using integration. I am unsure which to trust. I do have one very normally distributed independent variable which produces identical results using all 3 estimators, but my non-normal indicators produce very contrasting findings.
Any further advice would be greatly appreciated. I am so appreciative.
I have been reading everyone's posts who are interested in 2-1-1 models and I see many differences across them in their approach.
If x is the contextual predictor at L2 and m is the mediator at L1 and y is the outcome, I see the following codes:
%WITHIN% y ON m (b) ; %BETWEEN% m on x (a); y ON x(cp); MODEL CONSTRAINT: NEW(indb); indb=a*b;
Or I see %WITHIN% y ON m; %BETWEEN% y ON m (b); m on x (a); y ON x (cp); MODEL CONSTRAINT: NEW(indb); indb=a*b;
Or the Preacher Appendix for MLM is essentially the same as the first: %WITHIN% y ON m (b) ; %BETWEEN% m ON x (a) y on m (b) constrained to be equal to b; y ON x(cp); MODEL CONSTRAINT: NEW(indb); indb=a*b;
Help! Thank you and wishing you a very merry Xmas.
I don't care for the first one because why would you not have y ON m on Between? If you leave it out you get the the y ON x slope distorted.
I like the second one because here it is clear that the effect of the between-level x is expressed in a good mediation model on Between. But I don't know if that's what the originators of the 2-1-1 language had in mind.
Number 3 is ok if one can make that equality assumption.