General SEM Discussions

This link depicts a simple manifest-variable moderation as shown in the images below (along with Mplus code). The path diagrams don't inclde residuals, or, for instance moderator <-> predictor covariance, nor how covariance is implement (if at all) between XW and the main effects X and W.

A couple of questions.

1. Does anyone have a pointer to path diagrams that are complete (i.e with residuals and covariances?)
2. Using RAM, can the moderation be implemented as an algebra on the X->Y path?

Hi OpenMX community!

I would like to run a simple logistic regression model, but with a classical twin dataset, correcting for the fact that they are twins and their zygosity MZ or DZ. Is there a way for such analysis using OpenMx?

Thanks!

Hi guys,

I need help... My PhD thesis is about consumer behaviour analysis. In detail I look at what actions by companies trigger what customer reactions. In my quantitative study, I would like to look at the following dependent variables:

Behavior (with respective items)
Attitude (...)
Emotion (...)

And the following independent variables:

Opportunity for explanation (given/not given)
Alternative presented (yes/no)

My question is... Do I really need SEM? My professor wants me to do so, but I would rather like to use an experimental MANOVA design.

Is it possible to take a latent variable (for example Marketing) as for a composite variable which can be measured through its components ( for example promotional expenditure, price discounts etc). We can always argue that Marketing is itself a measured variable but if we take it as a composite function of all the activities that comprises of marketing then is it possible to use it as a latent variable? Will it violate any principles of SEM or factor analysis ?
A quick response will be highly helpful as I'm reaching the deadline of my project submission and I'm stuck due to this problem.

Hi, I'm new to SEM and I'm trying to build a model of marketing mix using sem.
My question is that is it possible to incorporate an observed variable in the structural component of SEM which has an effect on a latent variable or is being affected by a latent variable?
I have attached a pictoral representation of my question.
Please help.
Thank you in advance,
Sahil.

Hello!

I have a very general question that I would like to seek some help with. I know that the direct\indirect impacts for each variable in the model can be computed using a multiplication rule. However, this gives us the importance of the variable and not the path. How can I understand the importance of the path, should I simply add the co-efficients?

I would really appreciate a quick revert!

Thanks,
Vaibhav

Hello!

I have a very general question regarding Covariance based SEMs. I have a full SEM model (with latent variables) and my client is wondering how a change in one attribute for one of the factors could affect the end dependent variable. Are standard SEM methodologies capable of answering this question? If so, how should I approach it?

A quick revert will be really helpful! Thanks!

In short my question is: Are posterior predictive distributions used in the SEM world? If yes, under which name und for what purpose?

Hi, forgive me if you see duplicate messages. I previously posted the question on my last thread. But I think it's more proper to make it as a new topic.

I’m a PhD student in the field of economics very new using SEM and MX software.

I’d appreciate your feedback with regard to the following basic questions.

Let’s construct a SEM model where 3 measurable variables: X, Y and Z interact with a latent variable G and assume that the factor loadings of latent variable G over the measurable variables X, Y and Z are 0.3, 0.4 and 0.1 respectively. The model is constructed sourced on the correlation matrix (3x3) of the measurable variables.

Then, as far as factor loadings are considered as regression coefficients: