For a study I am researching a quite simple research model (7 IVs - 1 DV), in which I am not interested in underlying relations between the IVs: the relation between the IVs and the DV is all that matters.
My sample size exists of n = 189. The data is non-normal distributed and the assumption of multivariate normality is not met. I have tried to transform the data in multiple ways (sqrt, log10, box-cox etc.) and to delete outliers to meet the assumptions, but this does not work. I also tested the assumptions of Homoscedasticity and Multicollinearity, and these are met.
I have measured the correlation coefficients with a non-parametric test, and the results show that the IVs and DV are very weak correlated (r2 < 0.2).
Now, I want to test my model. My professor tells me to test the model in AMOS with SEM, but, likely due to the small sample size, non-multivariate normality and the low correlations, the fit of the model (based on CFI/TLI/RMSEA/X2/df) is terrible. The coeficcient values are also close to zero. I could not improve the goodnes of fit by adjusting the model based on literature, by transforming variables, by enabling bootstrapping or by removing outliers. I have read multiple fora and papers, and come to the conclusion that the model fits poorly, and that the very low correlations between the IV's and DV lead to a poor model fit.
However, I still want to report something. In class we learned how to use MLR with block-regression in SPSS. In multiple papers I read that for MLR my sample size should not be a problem, and that with bootstrapping MLR should be more or less robust to non-multivariate normality.
My question: is it okay to report the results of the MLR (Multiple linear regression) if I report that I could not achieve a good model fit in AMOS? Or does MLR apply the same calculations as used in SEM, and is it of no use to also execute a MLR?
Thank you in advance, all answers or suggestions are welcome. I had to learn everything about statistic in less than a month, so my way of thinking or the formulation of this text may be poor, for which I apologize in advance.