2-1-1 path analysis with binary media... PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
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 Ahyoung Song posted on Wednesday, April 16, 2014 - 5:54 am
Hi,

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;

Many thanks in advance!
 Bengt O. Muthen posted on Wednesday, April 16, 2014 - 7:38 pm
You can create odds ratios by Model constraint, e.g.

or = exp(b);

MLR should be fine here.
 Ahyoung Song posted on Thursday, April 17, 2014 - 3:02 am
Thank you so much!
 Nicki Keating posted on Tuesday, August 04, 2015 - 8:34 am
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
 Bengt O. Muthen posted on Wednesday, August 05, 2015 - 12:43 am
Looks right. Raw data are needed.
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