Programming the statistical procedures from SAS

SAS

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Occasional Contributor
Posts: 18

SAS

Hello

How to integrate metric and ordinal variables within the same latent variable.

Thank you

Frequent Contributor
Posts: 132

SAS

Hi Radhi.

Are you sure that your metric variables are really metric? If so, are they merely interval or are they ratio?

A very, very common convienient assumption about agreement scales with semantic anchors (such as likert scales) is that they meet the criteria for interval measures. If you think carefully about, maybe they don't.

My point? Maybe ALL your variables are ordinal. That may not be as bad as you fear. Item measures only really have to be additive, as in those that meet Rasch Measurement Model criteria, not gaussian distributed.

Does this help if you are wanting to use the measures in path analysis, say wtih PROC CALIS?

I'd say go ahead and try, and see how far apart your most non-controversial (safest) model's error distribution  looks from a gaussian distribution. You can always bootstrap your model to quantify the impact of your ordinal items on your model parameters without relying on standard assumptions.

Occasional Contributor
Posts: 18

Re: SAS

Dear

Would you mind inform me about the sample size required in order to run a Structural equation Modeling using Jmp or SAS

thank you

Frequent Contributor
Posts: 132

Re: SAS

Start wtih the number of measurement items, double that. (two parameters per measurement item, a factor loading, and an error variance). E.g. if you have 7 latent factors with 3 measurement items each you have 21 measurement items each with 2 parameters = 42.

Add the 'triangular number' ((n x (n-1))/2) associated wtih the number (n) of latent factors (and/or manifest structural items if you have a non-standard SEM). E.g. if you have 7 latent factors, (7 x 6)/2 = 21. This is the number of covariances amongst your 7 latent factors you will need to specify for the initial measurement model.

subtotal = 42 + 21 = 63.

Multiply this by 10 = 630. This is your recommended sample size.


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