Dear Noor, 

1) The basic idea underlying Structural equation Modelling (SEM ) is the identification of a set of hypothetical underlying factors (latent) that related to observed variables, and the identification of the causal ordering of these factors in relation to each other. Then SEM could be considered a combination of factor analysis and path analysis. But the CB-SEM  enable also  the assessment of mediating effects (that you show in our file attached), moderation effects,  and also invariance/equivalence of constructs across multiple groups, and higher order modeling of constructs.

Both direct and indirect effects of full or partial mediation among constructs can be assessed simultaneously and can be interpreted and the optimal path coefficients among exogenous and endogenous constructs can be determined.

An important first step before examining a moderating effect is to assess measurement invariance or equivalence. Measurement invariance exists when the measurement models for two or more groups are equivalent representations of the same constructs.

To assess invariance using CB-SEM, you impose a constraint of equivalence between the measurment  models and apply a change in Chi square value test, which is included in  AMOS.

Your model is a first-order measurement model  in which the covariances between the constructs are explained by a single layer of latent constructs.

One of the  limitations of. CB-SEM requires: 1)  5 to 10 observations per indicator, which makes the sample size requirements large even for relatively simple models; 2)  data to be normally distributed.  

A confirmatory factor analysis (CFA)  is executed to check for model goodness of fit. The normed chi-square , the comparative fit index (CFI) and RMSEA   and all indicators should be statistically significant (p

Dear Noor, 

Nominal: sex, race, regions, countries.

ordinal: age group; social status  (low, medium, high), etc

dicotomics: favor/against; like/dislike; sucess/failure, etc...

Then, there is thousands of answers...you need to specify what is your subject and what do you wnat to analyse.

The theory should be being any investigation.

Have a nice time

Helena

Dear Noor,

i am sorry, but i do not understand your attached file. Could you be more specific, please? 

PS:if you want you could send an email to [email protected]

Categorical Variables could be treated as independent variables as well as moderating variables in both SEM and also in Pls-SEM.

I don't know what is actually your confusion here. The attachment give is not about your research model, it is just your application to your dean for a pay-leave. You wanted to go back to Pakistan for data collection etc which could easily be approved.

Sorry for file attachment mistake.

Kindly see the attached file, comprises of two categorical and two continuous variables. How to analysis them together in CB SEM or VB SEM.. looking for criteria or methods before analyzing?

Take you in advance....

Usually age is a continuous variable or if you categorize it  could be ordinal. Why you say that age is nominal? It is a dichotomous variable? 

Age as categorical and gender as a nominal...............

Dear Noor, 

1) The basic idea underlying Structural equation Modelling (SEM ) is the identification of a set of hypothetical underlying factors (latent) that related to observed variables, and the identification of the causal ordering of these factors in relation to each other. Then SEM could be considered a combination of factor analysis and path analysis. But the CB-SEM  enable also  the assessment of mediating effects (that you show in our file attached), moderation effects,  and also invariance/equivalence of constructs across multiple groups, and higher order modeling of constructs.

Both direct and indirect effects of full or partial mediation among constructs can be assessed simultaneously and can be interpreted and the optimal path coefficients among exogenous and endogenous constructs can be determined.

An important first step before examining a moderating effect is to assess measurement invariance or equivalence. Measurement invariance exists when the measurement models for two or more groups are equivalent representations of the same constructs.

To assess invariance using CB-SEM, you impose a constraint of equivalence between the measurment  models and apply a change in Chi square value test, which is included in  AMOS.

Your model is a first-order measurement model  in which the covariances between the constructs are explained by a single layer of latent constructs.

One of the  limitations of. CB-SEM requires: 1)  5 to 10 observations per indicator, which makes the sample size requirements large even for relatively simple models; 2)  data to be normally distributed.  

A confirmatory factor analysis (CFA)  is executed to check for model goodness of fit. The normed chi-square , the comparative fit index (CFI) and RMSEA   and all indicators should be statistically significant (p

Thank you very much for your comprehensive guide.... but a bit confusion... how to deal with nominal variable in CB-SEM... for example Gender as mentioned with continuous variable data

...CB_SEM has some principles that we have to obey, such as :1) do not use it in small samples; 2) You should carefully consider the levels of your scale and the distribution of measured observed variables (nominal; ordinal; continous) when selecting the matrix of associations to analyse. Data might be analysed using a variety of plausible matrices of association (see AMOS).

With the nominal variable the estimation process can not be maximum likelihoo (ML) nor GLS, but  asymptotically distribution-free (ADF), because it does not require the assumption of multivariate normality .

PS: If  you use PLS- SEM it focuses on maximizing explained variance of the endogenous constructs, is more suitable for exploratory FA, does not require normally distributed data and needs considerably smaller sample sizes then CB_SEM.

PS: i could help you with more information, but only tomorrow, if you want .

Your suggestions will be welcomed..

Hi Helena, I  have a follow-up question re using categorical variables in SEM with AMOS. I have an independent categorical variable with 12 groups (11 treatment conditions and1 control group). I want to control for the effects of control groups when measuring the effects of one of my treatment conditions on dependent variables. So, i am just wondering should I dummy code the categorical variable into 11 different dummy variables (using the control group as the reference group) and only include 10 of those 11 groups in the model? or should i just use the categorical variable?  I have heard contradictory comments on whether one can use an independent categorical variable in AMOS.  Thanks for your help with this.

can nominal scale using pls sem?