Відео

Thank you!

I am glad it was helpful!

Quite easy. Use ESEM within CFA and respecify your model as a CFA and run a normal mgroup Analysis. See this video as an example. We also creates a tool to estimate ESEM and ESEM within CFAs ua-cam.com/video/yK_myOYNi-o/v-deo.html

Yes, you can use ordered categorical variables such as Likert scale survey items for ESEM. The most appropriate estimation procedure for these data are WLSMV. You may also use Bayes estimation, which does not rely on the assumption of a normal distribution.

@@dianamindrila8510 Thank you! Yoh helped me a lot.

Hello, Below is a very simple example of how to specify an ESEM model in Mplus. The model includes three factors (f1-f3) measured by observed categorical indicators (Q1-Q20) and two covariates (gender and age) regressed on f1 and f2 respectively. You will notice that the factor model is specified just like an EFA model but allows the specification of structural paths. This example is very similar to a MIMIC model but relies on EFA rather than CFA. Title: ESEM Example data: file is data.dat; variable: names are ID Gender Grade Age Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20; categorical are Q1 Q2 Q3 Q7 Q8 Q9 Q10 Q12 Q14 Q15 Q16 Q19 Q20 ; usevariables Q1 Q2 Q3 Q7 Q8 Q9 Q10 Q12 Q14 Q15 Q16 Q19 Q20 gender time; model: f1-f3 by Q1 Q2 Q3 Q7 Q8 Q9 Q10 Q12 Q14 Q15 Q16 Q19 Q20 ; f1 on gender; f2 on age; output: stdy; savedata: FILE=fs.dat;

@@dianamindrila8510 thank you so much! Very useful!

THANK YOU SO MUCH

I am glad it was helpful!

Traditional SEM relies on CFA measurement model, whereas ESEM relies on EFA to measure latent variables. In other words, item cross-loadings are estimated and taken into account rather than being fixed to zero. This allows a more accurate estimation of the measurement model and reduces measurement bias.

@@dianamindrila8510 thank you.

Hello, The SEM model relies on CFA, which forces crossloadings to be equal to zero. By fixing crossroading to zero a certain amount of bias is induced in the estimation process. ESEM calculates cross-loadings and takes them into account when estimating model parameters.

Thank you, no I do not have anything on canonical analysis yet

diyako4 I am glad it helped :)

You need ordinal or binary variables to be able to run EFA. Having a larger number of categories is preferable. If you tell me more about the nature of your variables I could give you more specific suggestions.

Diana Mindrila I currently have a set of variables from survey that asked respondents about multiple aspects of their mental health. These were usually ordinal with responses such as "often - never" or "Strongly agree - strongly disagree." What I want to do is to reduce those numerous variables using EFA. Do I need to convert those variables into dummies in order to do the EFA like you did? I am running stata if that is of any relevance to your answer. Thank you so much for the video and for replying.

I am glad it was helpful :)

Thank you, I am glad it was helpful.

I am glad it was helpful