I added control variables to my model. I got the following message after the addition. I wanted to use the full information. Therefore, I defined the all of the variables with missing data. It seems that the program deleted cases using listwise deletiion. With the following warning, I could not see the model estimation.
Data set contains cases with missing on x-variables. These cases were not included in the analysis. Number of cases with missing on x-variables: 94 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
What can I do to see the results of the model estimation?
The only way to avoid the listwise deletion of covariates is to bring them into the model as dependent variables. You can do this by mentioning their variances in the MODEL command. You then make distributional assumptions about them.
nina chien posted on Friday, September 04, 2009 - 1:58 pm
To avoid listwise deletion of covariates, I added the covariates into the model by mentioning their variances. This resulted in a series of WARNING statements that some of my covariates are categorical (which they are): “WARNING: VARIABLE GENDERR MAY BE DICHOTOMOUS BUT DECLARED AS CONTINUOUS.”
So, I tried stating covariates as categorical, but this resulted in error statements that the covariates are not DV’s: “ERROR in VARIABLE command. CATEGORICAL option is used for dependent variables only. GENDERR is not a dependent variable.”
I'm very confused because the WARNING and ERROR messages are contradictory. Can I ignore the warning statements?
Also, the model where covariates were declared as continuous did not converge. Is this related to the WARNING?
You should not put covariates on the CATEGORICAL list. The message comes about because the mean and variance of a dichotomous variable are not independent of each other. I would need to see the full output and your license number at firstname.lastname@example.org to say if this is related to convergence.
I have been asked by a reviewer to provide a reference for exactly what Mplus is doing when bringing covariates with missing data into the model as dependent variables by steps such as mentioning their variances in the MODEL command (and thus making distributional assumptions about them). If you have any recommendations for this, I would greatly appreciate it.
I don't know of a reference to this exactly. When you bring the x's into the model, multivariate normality is assumed for the x's and all continuous y's. This assumption is discussed in any structural equation modeling book.
Missing data theory does not apply to observed exogenous variables. The model is estimated conditioned on them. You can bring all of the covariates into the model by mentioning their variances in the MODEL command. Then they will be treated as dependent variables and distributional assumptions will be made about them.
Thanks Linda. I have run the analysis, and realise that if I bring the exogenous variable with missing data into the model, df becomes higher. I am wondering if this would pose any issue with explaining to readers how the model was specified. How do people usually justify the use of this method?
I am running a mediational model with two covariates (relationship status and greek membership). I have missing values on these. I specified type=missing with MLR. Then I entered the covariates in the model command and Mplus uses the whole sample. I understand that missing data theory does not apply to exogenous observed variables and when I do not include them in the model, there was no substantive changes except in one of my hypothesized effect. I want to keep all sample but I am wondering what happened here that brought the change? Can I trust the output with the observed variables included in the model
When you mention the means, variances, or covariances of the exogenous variables in the MODEL command, they are treated as dependent variables and distributional assumptions are made about them. This can change the results if not all variables are continuous. See the Version 6.1 version history on the website for a description.
When I am running a one-factor CFA model with continuous data, I get the following error. "Data set contains cases with missing on all variables. These cases were not included in the analysis. Number of cases with missing on all variables: 2"
Previously, I have run the same model without getting the error message.
I have checked whether the error is not due to misreading the input file, but still cannot find its source.
I have several questions regarding logistic regression with missing values. My data set contains 1000 cases, 300 of them with missing values. y and x1 are both binary and have missing values; x2-x4 are continuous and completely observed. I want Mplus to use all cases, including those with missing values. In order to do that I mentioned the variance of x1 and ran the following model:
ANALYSIS: estimator = ml; integration = montecarlo; MODEL: y on x1 x2 x3 x4; x1;
My questions are as follows:
1. When I mention the variance of x1 Mplus uses 950 of 1000 cases. If I specify one further independent variable (no matter which one), all cases are included. What I don’t understand is that the coefficients of the model vary depending on which variance I choose to mention. This observation confuses me and I am not sure which variance I should mention.
2. A more general question concerns predicting probabilites. Is it right that it is not possible to predict probabilities when there are missing values on an independent variable using ML estimation?
3. Is the given R square the McKelvey & Zavoina’s R square – and can I trust the value although I have missing values on the independent variable or does this bias the R square-computation (as I would guess)?
1. You should include the variances of all of the covariates or none of the covariates. When you include, for example, y1 and y2, they are correlated and y3 and y4 are correlated because the model is estimated conditional on them. But there are no correlations between y1, y2 and y3, y4. The zero correlations vary depending on which variances you inlcude in the model.