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Hi, I am analyzing 211 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; Many thanks in advance! 


You can create odds ratios by Model constraint, e.g. or = exp(b); MLR should be fine here. 


Thank you so much! 


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 


Looks right. Raw data are needed. 


Hello again, Thank you so much for your prompt response. 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 nonnormal. Is it likely that it is these characteristics which are producing different results? Some discussion pages say WSLMV is more robust to nonnormality, other pages say less. My sample size is 4267. I hope you can help! Many thanks Nicki 


MLR is logistic regression as the default. WLSMV is probit regression. The comparison should be of the patterns of significance. 


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 nonnormal independent variables are all nonsignificant 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 nonnormal indicators produce very contrasting findings. Any further advice would be greatly appreciated. I am so appreciative. Nicki 


Please send both outputs and your license number to support@statmodel.com. 


Festive greetings, I have been reading everyone's posts who are interested in 211 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. Nicki 


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 betweenlevel x is expressed in a good mediation model on Between. But I don't know if that's what the originators of the 211 language had in mind. Number 3 is ok if one can make that equality assumption. 

empisoz posted on Sunday, October 20, 2019  4:03 am



Hello, I'd like to estimate a "211" twolevel model where the mediator has been measured at the withinlevel and is dichtomous, while the dependent variable is continuous. Given that the exposure/independent variable is situated at the betweenlevel and hence the betweenvariance of the categorical mediator is taken into account, would it be correct to consider the indirect betweenlevel effect as a product of linear regression slopes (exposure to mediator, mediator to dependent variable)? Thank you very much fpr your reply. 


Yes. 

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