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| Anonymous posted on Friday, March 11, 2005 - 10:43 am
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Dear Professor Muthen,
I have some questions about the path analysis.
(1) I tried your example 3.11 first. The original code is
model: y1 y2 on x1 x2 x3;
y3 on y1 y2 x2;
Question (1) is: are these x1 x2 x3 assumed correlated in the first regression by default?
(2) Then I tried with
model: y1 y2 on x1 x2 ;
y3 on y1 y2 x2 x3;
x1 with x2;
Question (2) is: In the output, I got x1 with x3, x2 with x3 also. But those are not what I specify in the model. What is it going on here?
I met the similar situation with other data, when I specify one variable with another in the model, I got a lot of other "with" in the oupput.
Question (3) is: How can I specify x1 is correlated with x2, x1 is independent of x3?
(3) Finally, I tried with,
model: y1 y2 on x1 x2 ;
y3 on y1 y2 x2 x3;
x1 with x2;
output: modindices;
So, in the output I got some "with" modifications.
Question (4) is: How does this option work? Add one path per time? How can I specify the suggested "with" modification in the model?
Thanks for your time! |
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1. As in regular regression, the model is estimated conditioned on the x's.
2. When you mention x1 WITH x2, they are no longer treated as exogenous variables. Therefore, you no longer estimate the model conditioned on x1 and x2 but there are part of the model. You should not mention x variables in the MODEL command except on the right hand side of ON.
3. You will obtain modification indices for all parameters that are fixed or constrained to be equal to other parameters. See the SEM literature for how to use modification indices. See the Mplus User's Guide for a description of them. |
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| Anonymous posted on Tuesday, March 15, 2005 - 10:09 am
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| Thanks for your quick answers. Another question is can I use Mplus to fit the path analysis model with "feedback loop", i.e. X and Y are reciprocally causing each other? Thanks. |
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| Yes. |
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| Anonymous posted on Sunday, April 03, 2005 - 7:33 pm
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| Could you show you how to write the code for the path analysis with "feedback loop"? Thanks. |
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y1 ON y2;
y2 ON y1; |
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| Reetu Kumra posted on Thursday, April 13, 2006 - 1:16 pm
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Hi,
I have three imputed datasets from NORM that I am working currently working with. I ran the exact same model for all three datasets. It seems as though MPlus added on a few 'with' statement that aren't specified by me. These statements
aren't the same in the three outputs I am looking at. Why exactly does
this happen?
Thanks,
Reetu |
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| I would not be able to tell you that without more information. If the inputs are identical and only the data set name changes, I would be surprised to see different defaults in effect. If you want me to look at this, send the input, data sets, outputs, and license number to support@statmodel.com. |
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I realized where my error was. There was one variable in the 'use variable' statement that was located in a different spot than in the other use variable statements. Which raises my next questions:
1. Why would that make a difference in the with statements that are produced?
2. Why are the additional with statements that aren't pre-specified on the output?
Thanks for your help!
Reetu |
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| I cannot answer your question without the information that I asked for above. |
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Sorry if I've posted this message under the wrong topic. I wasn't sure where to post it.
I really like being able to run multiple regression models using MPLUS with FIML since it avoids listwise deletion. 1. Is there a way to get a plot of the residuals (estimated value of dependent observed variable minus actual value of dependent observed variable vs the predicted (estimated) values? This is very useful to check whether the model should be linear or quadratic. 2. Is there a way to see the Variance Inflation Factor values to check for problems with multicollinearity? |
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1. Individual residuals are not automatically available in Mplus. You can use the DEFINE command to create them.
2. No. |
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Thanks for your answers, Linda.
Another reason I was interested in the plot of individual residuals is that it reveals whether heteroscedasticity is a problem. I have one book on multiple regression that says when the homoscedasticity assumption is violated "conventionally computed confidence intervals and conventional t-tests of OLS estimators can no longer be justified." I don't know whether this warning is applicable when the multiple regression coefficients are estimated in Mplus using FIML. 1. Should I be concerned about the potential for heteroscedasticity when using FIML with the ML estimator? 2. If I use FIML with estimator = MLR so that robust standard errors are generated? 3. If the negative consequences of heteroscedsasticity are as likely/severe using FIML as in conventional OLS multiple regression, how would you recommend I check for heteroscedasticity using Mplus? Your guidance is greatly appreciated! |
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| The sames issues related to heteroscedasticity apply to both OLS and ML. See Example 3.9 in the user's guide for a suggestion with how to deal with this. |
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Thanks for the very quick reply, but I could use a little more information regarding my second and third questions:
Do the robust standard errors from the MLR estimator provide any protection against the negative consequences associated with heteroscedasticity?
How would you recommend I check for heteroscedasticity in a multiple regression using Mplus?
Again, your assistance is much appreciated. |
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| Toan Huu Ha posted on Wednesday, April 01, 2009 - 5:24 am
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Dear Dr. Muthen,
I run the path analysis in Mplus and got the result for chi square like this
Chi-Square Test of Model Fit for the Baseline Model
Value 920.454
Degrees of Freedom 58
Value 0.0000
Can you kindly let me know why the chi square test is so high. My sample size is 335 cases.
Thank you for your kind response. I really appreciate that |
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| You have a lot of degrees of freedom. I would look at modification indices to see where the model misfit is. Use the MODINDICES option of the OUTPUT command. |
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Calvin Croy:
MLR protects against heteroscedasticity. See:
White (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica,41, 733-750.
One way to test for heteroscedasticiy is to compare the ML and MLR standard errors. You can also do the procedure suggested in Example 3.9. |
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| Toan Huu Ha posted on Wednesday, April 01, 2009 - 10:47 am
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| Thank you so much. I got the model fixed. |
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Dear Prof. Muthén,
I have conducted a path analysis with two independent and four dependent variables (using means and sum scores). Since I have hypotheses about the direction of the influence from the independent on the dependent variables it would be appropriate to report the one-tailed p-value. However, Mplus only computes the two-tailed p-values. Is there a possibility to obtain the one-tailed p-value using a specific output-command? Or is it sufficient to divide the two-tailed p-value by two?
Is it appropriate to restrict some of the intercorrelations between the dependent variables using the WITH-statement due to content aspects? (one dependent variable is measured via video analysis and therefore no correlations are expected with the other 3 dependent variables).
Thanks for your help. Tony |
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To obtain the one-tailed p-value, look up the z value in a z table.
A path model should reflect the presence and absence of relationships based on theory. If theory suggests a relationship is zero, it should be fixed at zero. |
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Hello,
I want to accompany a correlation matrix with my longitudinal path model. Mplus output gives correlation coefficinets among variables used in the models, but how do I get significance levels of these correlations? In SPSS, the correlations are different because the program uses listwise delition (which I don't want).
Thank you,
Kristine |
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| It would be complicated to do this in Mplus given that the covariance matrix is analyzed for path models not the correlation matrix. You would have to use WITH statements to define all covariances and then use MODEL CONSTRAINT to turn them into correlations. |
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| Qilong Yuan posted on Monday, April 19, 2010 - 12:34 pm
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Hi,
My model specification is this:
y2 ON y1;
y3 ON y2;
x2 ON x1;
x3 ON x2;
y2 ON x1;
y3 ON x2;
x2 ON y1;
x3 ON y2;
y1 WITH x1;
But in addition to all of these paths, I also get an estimate of “x3 WITH y3”. When I remove “y1 WITH x1” the correlation between x3 and y3 is still estimated.
I am surprised to get a correlation I did not specify. This is a correlation between disturbance on x3 and y3, correct? Is it essential to the model, or would it be reasonable to fix it to zero (and gain a degree of freedom)? |
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Mplus estimates certain parameters as the default. If you don't want them, fix them to zero, for example,
x3 WITH y3@0; |
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