This website uses Cookies. Click Accept to agree to our website's cookie use as described in our Privacy Policy. Click Preferences to customize your cookie settings.
Accept
Reject
Preferences
cancel
Turn on suggestions
Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.
Showing results for 
Show  only  | Search instead for 
Did you mean: 
Have your say in shaping JMP's future by participating in the new JMP Wish List Prioritization Survey
Choose Language Hide Translation Bar
McMaster
McMaster
Level I

Multigroup Analysis

Created: Jan 6, 2022 07:57 AM  |  Last Modified: Jun 8, 2023 08:43 PM (464 views)

Hello everyone,

 

I am new to JMP and I wonder if there is a capability to do multigroup analysis after specifying and identifying the model. Our model includes many latent variables.
An identical survey has been used to collect data from three different panels for three different study conditions. We would like to understand whether there are significant differences in effect sizes, path coefficients, and other test statistics between three groups for the same model.

 

Thank you.

0 Kudos
1 ACCEPTED SOLUTION

Accepted Solutions
SDF1
Super User SDF1
Super User
Solution

Re: Multigroup Analysis

Jan 6, 2022 03:01 PM (440 views)  |  Posted in reply to message from McMaster 01-06-2022

Hi @McMaster ,

 

  Yes, JMP can do this. Not knowing what the structure of your data table is, it's hard to provide more specifics, but if you already know what the model is and all the hidden variables, then it should be pretty straight forward.

 

  If your prediction formula for the response is easy, you can make extra prediction columns and swap out the Group factor in the column formula. If it's rather complex with many terms, you might want to re-run the model in the Fit Model platform and just swap out Group A, B and C each time, but keeping all the other terms in the model the same. You'll then get three different response model fits that you can do all your comparisons with.

 

  Or, and this would depend on the data structure of your table, you can use the Group (levels A, B, C for example) in the "By" role of your Fit Model platform. It will then run the model of your choice for each of the levels of Group. Take a look at the Diabetes.jmp file and use the continuous Y for the response and then the Binary or Ordinal Y as the By variable and select Age through Glucose as the model effects. When you run this model, it will create a new model fit for each level of the Binary or Ordinal Y. You can then do a Fit Y by X, with the Group (Ordinal Y) as X and the prediction formula as Y and then do all sorts of tests as you might do with an ANOVA.

 

Hope this helps,

DS

View solution in original post

1 REPLY 1
SDF1
Super User SDF1
Super User
Solution

Re: Multigroup Analysis

Jan 6, 2022 03:01 PM (441 views)  |  Posted in reply to message from McMaster 01-06-2022

Hi @McMaster ,

 

  Yes, JMP can do this. Not knowing what the structure of your data table is, it's hard to provide more specifics, but if you already know what the model is and all the hidden variables, then it should be pretty straight forward.

 

  If your prediction formula for the response is easy, you can make extra prediction columns and swap out the Group factor in the column formula. If it's rather complex with many terms, you might want to re-run the model in the Fit Model platform and just swap out Group A, B and C each time, but keeping all the other terms in the model the same. You'll then get three different response model fits that you can do all your comparisons with.

 

  Or, and this would depend on the data structure of your table, you can use the Group (levels A, B, C for example) in the "By" role of your Fit Model platform. It will then run the model of your choice for each of the levels of Group. Take a look at the Diabetes.jmp file and use the continuous Y for the response and then the Binary or Ordinal Y as the By variable and select Age through Glucose as the model effects. When you run this model, it will create a new model fit for each level of the Binary or Ordinal Y. You can then do a Fit Y by X, with the Group (Ordinal Y) as X and the prediction formula as Y and then do all sorts of tests as you might do with an ANOVA.

 

Hope this helps,

DS