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Anonymous posted on Wednesday, October 12, 2005 - 5:37 pm
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Is it possible to have binary (0 and 1) variables at level-2 in multi-level sem? |
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Yes. This is possible. |
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I am using a dichotomous outcome (yes/no DSM-IV diagnosis) as 2 dependent variables in a 2 level model. When I include the command "categorical are XX XX", I get an error message "RECIPROCAL INTERACTION PROBLEM". Without the "categorical" statement, the model runs fine. Is it necessary to label dichotomous variables as categorical in multilevel modeling? |
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Are you turning one yes/no variable into two variables? It sounds that way but maybe I misunderstand. If so, this is not necessary. Use only one variable. This variable should be placed on the CATEGORICAL list. |
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Thank you for your response. I will list the 2 variables (they are two separate variables, each with a possibility of 0 for does not meet criteria and 1 for meets diagnostic criteria) as categorical. Now, is there anyway to amend the problem of "RECIPROCAL INTERACTION" with my data - or and I just not able to run this model? Thank you. |
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Given that you have two variables, you need to send the input, data, output and your license number to support@statmodel.com so I can see what the reciprocal interaction message is coming from. |
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Is there a solution to this problem? I have a model with a dichotomous independent variable, one dichotomous mediating variable, three continuous mediating variables and one dichotomous dependent variable. I have listed the dichotomous mediating and dependent variables in the CATEGORICAL list. Is there something that I should do differently so that I don't get the 'RECIPROCAL INTERACTION" message? |
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Which version of Mplus are you using? |
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It appears to be version 4. I am a doctoral student using the school's program. Would using the newer version eliminate my problem? |
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Your problem should fit in the Demo. Try it there. I think a newer version would solve the problem. |
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Okay I will thank you so much! |
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Dr. Muthen, I downloaded the demo and tried it and I am still getting the "RECIPROCAL INTERACTION PROBLEM" message, is there something else that could cause me to get the message? |
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Then you would need to send the input, data, and your license number to support@statmodel.com. |
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Tyler Burch posted on Friday, December 04, 2015 - 12:17 pm
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First, thank you so much for what you do to help the discipline. I am trying to run a mediation analysis using a level 1-2-2 configuration. I run the following syntax as suggested by Preacher, although I change the Estimator to WLSMV because my outcome is dichotomous. This does not work as the Estimator defaults to ML. I am wondering if there is a way to run a mediation analysis using a 1-2-2 configuration with a dichotomous outcome. Thanks for your help! ANALYSIS: TYPE IS TWOLEVEL RANDOM; Estimator = WLSMV; MODEL: ! model specification follows %WITHIN% ! Model for Within effects follows aWFCIND; ! estimate Level-1 (residual) variance for x %BETWEEN% ! Model for Between effects follows bFAMSAT SEPCDmx; ! estimate Level-2 (residual) variances for m and y bFAMSAT ON aWFCIND(a); ! regress m on x, call the slope "a" SEPCDmx ON bFAMSAT(b); ! regress y on m, call the slope "b" SEPCDmx ON aWFCIND; ! regress y on x MODEL CONSTRAINT: ! section for computing indirect effect NEW(indb); ! name the indirect effect indb=a*b; ! compute the Between indirect effect OUTPUT: TECH1 TECH8 CINTERVAL; ! request parameter specifications, starting values, ! optimization history, and confidence intervals for all effects |
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ML can handle a binary outcome. The indirect effect is still for the continuous latent response variable behind the binary outcome. You don't need Type = Random. You can also use Bayes estimation. |
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Hello, when I model a level-1 outcome (lifetime drinker, coded 0 or 1, by wave) in a 2-level MSEM (time points within persons), Mplus states that there is no variance for the dependent variable at level 1; it cannot be modeled. Why is this, even though this yes/no variable is time-specific? Thank you. |
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A binary DV generally does not have a free variance parameter on Level-1. |
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OK, thank you. I understand that the mean of a DV is a level-2 parameter, and given the complete dependency of the mean and variance of a dichotomous variable, it would make sense that there is only variance for said DV on level 2. Is the level-2 variance parameter then interpreted as a "blend" of level-1 and level-2 variance? Or is there only level-2 variance in a stricter sense? I am running conditional models and want to make sense of the variance explained statistics. I am seeing Mplus only give the variance estimate on level 2, with some interesting changes as I add level-1 and level-2 predictors. Thank you. |
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No, that's not how this should be understood. On level 1 there is not a free variance parameter in line with probit/logit regression with a binary DV. Or, it can be seen as fixed (at 1 for probit). On level 2 you consider the random intercept for this binary DV and the random intercept is a continuous variable connected with several free parameters: mean, variance, and regression parameters (Mplus reports the threshold instead of the mean.) So, no, the level-2 variance is not a blend. Our short course handouts on this give several good background readings for really understanding this. |
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Thanks, Dr. Muthen. Could you point me to one of the short course handouts in particular, and I will search for the background readings it provides? I saw several short course documents on the statmodel.com website pertaining to multilevel modeling, and I wonder which one I might view to see the references. Can you help? |
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Eva K posted on Thursday, October 26, 2017 - 3:57 am
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Dear Mplus team, I performed a multilevel analysis using Mplus7. On level 2 I included a number of continuous predictors and one dichotomous variable (group, coded with 0 and 1). Does it make sense to interpret the standardized coefficients for the dichotomous variable or would I rather report and interpret unstandardized results for this particular variable? I would also like to know: Do I have to include "categorical = group" in the input? Thank you very much for your time! |
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Our UG gives advice on standardizations - don't standardize regression slopes for binary x's. Categorical = is only for DVs. not x's. |
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