I am trying to test a simple mediation model where a level-2 predictor (w) is an exogenous variable and a level-1 variable (y1) is the primary endogenous variable. Variables x1 and x2 (both level-1 variables) are hypothesized to be mediators of this relationship.
When I run the code below I get the following WARNING:
** WARNING in MODEL command In the MODEL command, the following variable is a y-variable on the BETWEEN level and an x-variable on the WITHIN level. This variable will be treated as a y-variable on both levels: X1 *** WARNING in MODEL command In the MODEL command, the following variable is a y-variable on the BETWEEN level and an x-variable on the WITHIN level. This variable will be treated as a y-variable on both levels: X2
However, despite this WARNING I do get an estimate of both the y1-x1 and y1-x2 relationship at the WITHIN level according to the output.
Should I be worried, or is there a better way to specify this mediation model?
It is just saying the x1 and x2 are being treated at dependent variables on both levels. This means distributional assumptions are made about them. If this is the model you want, this is how it is done.
Hello, I am running a similar multilevel mediational path model [per Preacher et al (2011), a 2-1-1 model], yet with a 4-level categorical (ordinal) outcome (i.e. y1 above is ordinal). Mplus does not seem to allow this, with the message:
*** ERROR in MODEL command Observed variable on the right-hand side of a between-level ON statement must be a BETWEEN variable. Problem with: x1 Same message repeated for any mediating variables with both within and between level components.
Running with the outcome as continuous does work. Is there a different setup required, or is the multilevel ordinal logistic model with mediation impossible to test with Mplus? I realize the numerical integration makes it computationally problematic, but Mplus won't try to run it. Thanks, Phil
Thanks Dr. Muthen. The workaround of creating the perfect factors separating within/between components did work.
As a followup question, I wonder if Mplus is able to model a weighted TWOLEVEL model with an ordinal outcome and mediators using MLR, to obtain an ordinal logistic model.
The model runs using WLSMV but not with MLR, and I receive this error:
THE ESTIMATED WITHIN COVARIANCE MATRIX COULD NOT BE INVERTED. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 1. CHANGE YOUR MODEL AND/OR STARTING VALUES.
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE COMPUTATION. CHANGE YOUR MODEL AND/OR STARTING VALUES.
The chapter 16 tables of estimators seems to indicate the MLR should work, but perhaps I've set this up incorrectly. I'd appreciate any suggestions you might have. The following post will show a simplified model that yields the error.
Thanks Dr. Muthen, adding back that little bit of variance allowed the model to work under MLR. I appreciate your prompt suggestions. Very best regards, Phil
Andy Luse posted on Wednesday, August 01, 2012 - 11:08 am
I have a 1-2-1 mediation model where the two within IVs (rotate, healthy) are dicotomous, the 2nd level mediator is latent-continuous (Imp), and the 1st level DV (choice) is also dichotomous. I am trying to test for indirect effects for each of the two IVs through the mediator. I tried using your suggestions above, but I get the same error when setting @0 and when I set it @0.01 it just keeps running.
Andy Luse posted on Wednesday, August 01, 2012 - 11:11 am
Here is the syntax...
USEVARIABLES ARE id choice rotate healthy Imp_1 Imp_2 Imp_3 Imp_4 Imp_5 Imp_7 Imp_9;
CLUSTER IS id;
BETWEEN ARE Imp_1 Imp_2 Imp_3 Imp_4 Imp_5 Imp_7 Imp_9;
Andy Luse posted on Friday, August 03, 2012 - 10:06 am
Ok, I have broken it down to where I am just trying to estimate the direct effect at each level with a single dichotomous IV (rotate) and a single dicotomous DV (choice), but I get the error "THE ESTIMATED WITHIN COVARIANCE MATRIX COULD NOT BE INVERTED. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 1. CHANGE YOUR MODEL AND/OR STARTING VALUES." I'm trying to use the method above.
You should change within and between in the same way.
Andy Luse posted on Friday, August 03, 2012 - 2:26 pm
When I try that, I get the following error and it doesn't run "Unrestricted x-variables in TWOLEVEL analysis with ALGORITHM=INTEGRATION must be specified as either a WITHIN or BETWEEN variable. The following variable cannot exist on both levels: ROTATE"
With numerical integration, the latent variable decomposition of a individual variable is not allowed. You need to create a between-level variable for rotate. You can do this using the CLUSTER_MEAN option of the DEFINE command.
Andy Luse posted on Saturday, August 04, 2012 - 12:21 pm
Ok, I made a new cluster-level variable for rotate using the CLUSTER_MEAN command and then specified rotate as a within variable and the new variable (rotate_b) as a between variable. Now I am getting the following error...
*** ERROR One or more variables have a variance of zero. Check your data and format statement.
Continuous Number of Variable Observations Variance
I'm not sure why choice is listed as a continuous variable as I have specified it as categorical in the variable portion. It makes sense that rotate_b would have a variance of zero as it is the mean of the within subjects dichotomous experimental condition rotate.
Hello, Suppose 2-2-1 multilevel mediation model for about 400000 patients in 300 hospitals in 9 countries. Random intercepts for the hospital, no random slopes. Countries are not chosen at random and n is only 9. One possibility is multiple group (i.e. country) multilevel (i.e. hospital) mediation model, which for these data is too computationally demanding. I have also not seen examples of this in the literature. An often suggested proxy is to put country in as a fixed effect. In a mediation model, would country then have to be included in both the a as well as the b path? CLUSTER = hosp; BETWEEN = ctrptn ctreduc pes beds0 bedsmiss tech0 techmiss teac0 teacmiss clinical country; WITHIN = age sex er drg ermiss charlson; CATEGORICAL = d30; analysis: TYPE= twolevel; ESTIMATOR = BAYES; BITERATIONS = (1000); PROCESS=2; model: %BETWEEN% d30 on ctrptn ctreduc beds0 bedsmiss tech0 techmiss teac0 teacmiss country; d30 on clinical (b); clinical on ctrptn (a); clinical on pes country; %WITHIN% d30 on age sex er ermiss drg charlson; MODEL CONSTRAINT: NEW(ind); ind=a*b;
Does my code & calculation of the indirect effects look correct for specifying a sequential mediation model with all paths fixed? Thanks USEVARIABLES ARE ID X M2 M1 Y; MISSING ARE ALL (-999); CLUSTER IS ID; !GROUPING VARIABLE IS ID WITHIN = X M2 M1;!LEVEL 1 VARIABLES BETWEEN = ; DEFINE: CENTER X M2 M1 (GROUPMEAN);!GROUP MEAN CENTER Analysis: TYPE = TWOLEVEL RANDOM; MODEL: %WITHIN% Y ON M1 (b1); !path b1 Y ON M2 (b2); !path b2 Y ON X (cdash); !Direct effect X-Y M1 ON X (a1); !path a1 M2 ON X (a2); ! path a2 M2 ON M1 (d1); ! path d1 %BETWEEN% Y; ! no predictors of intercept MODEL CONSTRAINT: NEW(a1b1 a2b2 a1d1b2); a1b1 = a1*b1; !Specific indirect effect of X on Y via M1 a2b2 = a2*b2; !Specific indirect effect of X on Y via M2 a1d1b2 = a1*d1*b2; !Specific indirect effect of X on Y via M1 and M2 OUTPUT: TECH1 TECH8 CINTERVAL;
Looks ok. But I don't know why you group-mean center the mediator.
Ted Fong posted on Monday, July 16, 2018 - 1:40 am
I understand that ALGORITHM=INTEGRATION is needed for ML multilevel modeling on a binary outcome. After reading the new technical report on latent variable centering, I have the following three quick queries that I hope you could confirm.
1) Is latent variable decomposition of predictors X still not feasible for ML estimation in Mplus 8.1 when numerical integration is involved?
2) If I stay with MLR estimation, must I create a between-level component for X using CLUSTER_MEAN, which would then deviate from the latent variable centering technique?
3) Is Bayes the only feasible estimation for using latent variable centering in multilevel mediation analysis with both categorical outcome and random slope?
MODEL: %WITHIN% y ON x; Y ON m1 (a); Y ON m2 (b); m1 ON x (c); m2 ON x (d);
%BETWEEN% y ON x_mean; y ON Z (e); Z ON x-mean (f);
MODEL CONSTRAINT: NEW(ind1 ind2 ind3); ind1=a*c; ind2=b*d; ind3=e*f;
Some questions regarding this model:
1) Is this syntax correct? 2) Does the model provide logit parameters for the categorical endogenous variables m1 and y and linear regression parameters for the effect of X on m2 and X_mean on Z are? 3) I also tried the model using a BAYES estimator. If I choose this estimator, are my estimates for m1 and Y still logits? 4) Which estimator is to be preferred? Bayes are MLR? 5) How do I interpret the indirect effect ind2, which is the multiplication of a linear regression (d) and a logit (b) parameter? Is it correct to multiply these coefficients?
Because your mediation model has categorical variables, you should read these 2 articles as a first step:
Muthén, B. & Asparouhov T. (2015). Causal effects in mediation modeling: An introduction with applications to latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 12-23. DOI:10.1080/10705511.2014.935843 Click here to download the paper.
Nguyen, T.Q., Webb-Vargas, Y., Koning, I.K. & Stuart, E.A. (2016). Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention. Structural Equation Modeling: A Multidisciplinary Journal, 23:3, 368-383 DOI: 10.1080/10705511.2015.1062730
See also our book:
Muthén, B., Muthén, L. & Asparouhov, T. (2016). Regression And Mediation Analysis Using Mplus. Los Angeles: Muthén & Muthén.
More specifically, your ind1 = a*b expression which involves the categorical M1 is in question. With WLSMV and Bayes, the prediction of the outcome from this mediator is from M1*, the cont's latent response variable behind the observed M1. And the outcome is considered as Y*, not Y. This makes a*b correct. With ML, M1 itself is predicting Y (or Y*) and a*b is not correct. If you are interested in M1 or M predicting Y, you should use counterfactually-defined causal effects as in the references above.
Thank you very much for this helpful response. I have read the material. The Nguyen et al. paper, however, states that the counterfactually-defined causal effects method is not yet tested for a multilevel setting. Would it be applicable for this model?
But if I understand correctly, if I use a WLSMV or Bayes estimator, the above syntax for my model is correct? The indirect effects are then interpreted as the effect of X on the latent response variable Y, via M1.
refers to work by Ngyuen et al for more than one mediator but not multilevel settings.
Sandra Ohly posted on Wednesday, March 20, 2019 - 2:17 am
Thank you for the quick response. I had a look at th Ngyuen paper and wonder if it is relevant because it deals with binary outcomes, but not binary mediators. Furthermore I wonder if this approach can be implemented in a multilevel setting at all?
So far, I tried to combine Chris Stride's approach of adjusting the between level effects of the mediators before calculating indirect effects with the MSEM approach by Preacher. However, this resulted in a number of problems. 1. When stating that the mediators are categorical, the model won't run because variances for categorical outcomes are not allowed on the within level. 2. When ignoring that the mediators are categorial, the model will run, but the standard errors are not trustworthy.
Any suggestions on how to implement a 2-1-1 mediation with binary mediators? Thank you in advance.
Yes, I understand that the indirect effect is calculated using the level2 effects. But following Preachers 2-1-1 approach I need to model the effects on the lower level as well, I believe. Is this correct?