Message/Author 

Anonymous posted on Sunday, May 01, 2005  6:52 am



Is it possible to graph the random slopes in an SEM? For example, my model is similar to example 5.13 in the user's guide. I was wondering of PLOT3 will do this or if there is something special that I had to do? Thank you in advance 

bmuthen posted on Sunday, May 01, 2005  4:45 pm



You can request factor score estimates and use PLOT3 to show their distribution univ and bivariate with another variable. 

Anonymous posted on Tuesday, May 03, 2005  4:21 am



Thank you so much! 


Question: Is there any way to avoid listwise deletion when using random slopes analysis? My situation: I have 2 correlated outcomes of interest DV1 & DV2 (about 25% of DV2 are missing) I have 2 uncorrelated predictors IV1 & IV2 (about 25% of IV2 are missing). My model is: s1  DV1 on IV1 ; s2  DV2 on IV2 ; The problem: Any instances where the either DV2 or IV2 are missing, those cases get deleted. thanks for you help! 


I have no idea what version of Mplus you are using but in the current version, TYPE=MISSING is the default so cases with missing on DV2 will not be eliminated. However, cases with missing on one or more independent variables will be deleted because missing data theory does not apply to independent variables. 


Thanks! 

Mireille H. posted on Tuesday, December 20, 2011  4:28 am



Dear Mr./Mrs. Munthén The Mplus output (version 6) indicated that a few of my 3way interaction terms were significant, and I was wondering how I can test wich slopes of each term significantly differ from each other? I tried to use the online calculator of Mr. Preacher for this, but this gave me unplausible results. I hope you can help me with this. Kind regards 


You can use the Wald test or difference testing. See MODEL TEST for the Wald test. This assumes the coefficients being compared are on the same scale. 


Basic question. I'm having a difficult time understanding how Mplus estimates equations where the slope is a dependent variable and variables are independent variables. I see that the slope is treated as a latent variable, but beyond that I am lost. I appreciate any help. Best, 


I think you are talking about UG ex 3.9 which says on page 30 that the random slope handles heteroscedasticity in the y residual variance as a function of predictors. The degree of variance of s and its covariance with the y residual corresponds to the degree of heteroscedasticity. We give references on that page too. 


Yes, I am wondering if there is a simple way of explaining this. Forgive me that I do not yet understand the details of the citations. I do not understand how S "handles heteroscedasticity in Y residuals variance as a function of predictors." Is it the case, using the example you cite, that Mplus regresses Y on X1 for values of X2, to get vector of S data? And then regress S on X2? Is it the case that Mplus regresses Y on X1 and then you take unexplained residual and regress that on X2 to see how X2 changes residuals around average slope? Can you explain to me this connection between slope and residuals? Because now it seems like S is not the dependent variable but the Y residuals after regressing Y on X1, no? I also do not understand what you mean by variance of S and its covariance with the Y residual. Where does the data on S come from? Now it sounds as if we're regressing residuals on S or vice versa. I'd appreciate some explanations. I am fairly competent at statistics, but I am a bit bemused by this seemingly simply example. Thank you! 


A fuller account is given in the FAQ on our website: Random coefficient regression 


Yes, so let's use the pdf file that links to as our example. Let's also assume that all the variables including the beta1 below are standardized. Where does the data on the beta1 come from? Or since it is unmeasured, what are we doing? So we have: E(YX,Z) = beta1X + beta2Z E(beta1Z) = beta3Z so, E(YX,Z) = beta3ZX + beta2Z Is beta1 definitionally beta3Z (so that beta1 is simply generated by taking values of Z and multiplying them by beta3 estimated from regressing Y on ZX) or is there some data or something such that beta3Z is merely the conditional expectation of beta1? In the former case we have a mathematical relationship. In the latter case we potentially have a case where a variable Z has a causal influence on the strength of causation between two other variables. The latter case seems to be what is of interest whereas the mathematical relationship is not so interesting. 


Z has influence on the strength of relationship between two other variables. It acts as a moderator, but the moderation model is here more general. beta_{1i} is a latent variable and as usual with latent variable modeling such as SEM it has implications for the relationships among the observed variables. Its values need not be estimated. Information on its implications come from the observed heteroscedasticity. 


Ok, this is starting to make sense. So let's say the residuals from Y regressed on X and Z are biased such that E(resid) not= 0. We regress the residuals of Y on Z. Say we find that there is a statistically significant relationship between residuals of Y and Z. How can we use this information to estimate random slope of X>Y edge as a function of Z? I appreciate your time. This is very informative and useful. I'm getting there. 

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