Hello! I have a sample size of N = 70 at the moment, hoping to aproach 100. My Model has 21 parameters to be estimated. The Variables are all not normally distributed.
Now the options that I know of are the following:
1. use MLM, but for small samples the standard errors can be overcorrected (
ULS is for ordinal variables and recommended because it considers fewer parameters and adjusts when the distribution of normality is missing
Mukaram Ali Khan and Alireza shabani shojaei Thanks for your suggestions! I didn't know about PLS before because it is not commonly used in my field. But I took a look at it. However I found some information, including the following Article, on the shortcomings of PLS that make me believe it is not suited for my research. It is a very interesting read that I can recommend!
Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47, 9-27.
Taken from the Abstract: "(...) The current lack of methodological justification for PLS prompted the editors of this journal to declare that research using this technique is likely to be desk-rejected (Guide & Ketokivi, 2015).To provide clarification on the inappropriateness of PLS for applied research, we provide a non-technical review and empirical demonstration of its inherent, intractable problems. We show that although the PLS technique is promoted as a structural equation modeling (SEM) technique, it is simply regression with scale scores and thus has very limited capabilities to handle the wide array of problems for which applied researchers use SEM. To that end, we explain why the use of PLS weights and many rules of thumb that are commonly employed with PLS are unjustifiable, followed by addressing why the touted advantages of the method are simply untenable." (Rönkko et al., 2016, p.2)
As you have mentioned "AMOS" in your question , therefore I thought you are having a model which can do analysis, using PLS as well. To the best of my knowledge , when we have small sample size and/or Non-Normal Data then PLS is recommended.
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