Interpretation of MGA and MICOM results

[kini]

Dear All,
I have observed heterogeneity in my data. So I separated my data into two data groups based on the categorical moderator. I ran MICOM and I have achieved configural and compositional invariance. However, I do not have equality of composite means and variances. Thus my measurement model shows partial invariance and hence I can proceed to MGA. After this, I checked for MGA and none of the paths show any significant difference across the two groups, establishing structural invariance of my model.
I am confused how to interpret this case. What does it mean to have partial invariance in the measurement model but complete invariance in the structural model? How to proceed further? Any guidance would be much appreciated. Thanks.

Reference:
Henseler, Jörg, Christian M. Ringle, and Marko Sarstedt. "Testing measurement invariance of composites using partial least squares." International marketing review 33.3 (2016): 405-431.

[Nick32]

Hello,

When reporting your results, it's crucial to detail fit indices and parameter estimates. Just as in a cover letter where you highlight your qualifications and specific skills, every detail of your analysis reinforces the credibility and clarity of your conclusions. Chi-square difference tests and effect sizes offer valuable insights into the impact and significance of your results.

As far as partial invariance in the measurement model is concerned, identifying non-invariant indicators and testing possible sources of bias is an essential step. This reminds me of the way you adjust a cover letter to suit the target position, by identifying and highlighting the most relevant skills.
How might incorporating a comparative analysis between models improve our understanding of the validity and reliability of the measures in your study? Could this approach provide additional relevant information, similar to the way a well-targeted cover letter highlights a candidate's specific skills?

For more information on cover letters or business letters, click here.

[Nicholas Brock]

It's important to include fit indices and parameter estimates in your results report. Every piece of your investigation supports the validity and precision of your conclusions, much like in a cover letter where you highlight your credentials and particular abilities. Effect sizes and chi-square difference tests provide important information about the importance and influence of your findings.

[soberinstall]

Partial measurement invariance indicates differences in composite means or variances between groups, while structural invariance shows consistent relationships between constructs across groups. This means the theoretical model holds true despite group-level differences. Explore latent mean differences, contextual factors, and theoretical implications to understand and report these findings comprehensively.

[anupkumar]

Partial invariance means some differences between groups, but the relationships between factors stay the same. Look at the group mean differences for more understanding.