What is the minimum sample size per group for measurement invariance analyses?

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Claude Houssemand

Dear Marcel,

This article could help you: https://pdfs.semanticscholar.org/0513/2416492dd8d9d0c4f0916e040d8824e13277.pdf. It seems that sample of 100 persons gave results with low robustness.

Best regards

Look at this article, there is information related to sample size and references: Article Measurement Invariance Conventions and Reporting: The State ...

Best

Marcel Grieger

Dear Claude an Matthew,

thank you for the articles and suggestions so far. I took a close look at Meade's (2005) conference paper. Under ideal conditions, n=100 may suffice but as you stated, it is not very robust. The second article I had already been aware of.

Matthew Kerry: Do you happen to have a citable source that claims n=75 as common practice? Despite the test's robustness, it would be nice to have one just in case.

Thanks again

Marcel

Christoph Weber

Hi, I would do a small monte carlo Simulation. This way you can best take your actuall application (number of indicators, factor loadings, sample sizes, …) into account.

best Christoph

Dear Christoph

thanks for your suggestion. I do not have the faintest idea how to conduct one, but I will put it on the list of questions for the meeting with my supervisor next week.

As general rules of thumb seldom "fit" your actual model, I have learned that MC-Simulations are a quite flexible tool to understand the performance of ones estimation methods (mimicking your actual data and model).

But yes, it takes some time ...

you find good introductions here

Bandalos, D. L. (2006). The use of Monte Carlo studies in structural equation modeling research. In G. R. Hancock & R. O. Mueller (Hrsg.), Structural equation modeling: A second course (S. 385-426). Greenwich, Connecticut: Information Age Publishing.

Bandalos, D. L. & Gagne, P. (2012). Simulation methods in structural equation modeling. In R.H. Hoyle (Hrsg.) Handbook of Structural Equation Modeling (S. 92-108). New York: Guilford Press.

Muthén, L. K. & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599-620.

Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F. (2001). Monte Carlo experiments: Design and implementation. Structural Equation Modeling, 8(2), 287-312.