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The bottom line is that (as Jeremy Miles says) the value of the negative log-likelihood doesn't really matter, only differences between the negative log-likelihoods. But you might still wonder why you are getting negative values. Reproducing an answer of mine from here: Technically, a probability cannot be >1, so a log-likelihood cannot be >0, so a ...


Yes. -2 LL means -2 multiplied by the log likelihood. AIC, BIC etc are (as far as I know) only interpreted in relation to other values from different models. An AIC of -100 doesn't mean anything on its own. It means something when a different model, using the same data, has an AIC of -90, so the difference is 10. The difference is the interesting thing.


Both models are identical and will lead to identical predicted probabilities. The apparent discrepancies between SPSS and R are due to different reference levels of the categorical predictors. As I explain here in detail, when using dummy contrasts, the coefficients for categorical predicators are differences between that level of the predictor and the ...


I don't mean to dog pile, but you really should not be doing stepwise regression. I won't include reasons why, kjetil has already listed some and you can search for key terms on this site if needed (or just google "Stepwise Regression" and "Frank Harrell" who has already commented here for the definitive list of reasons why not to use ...


You can just reverse it. Having different distributions of the categories at different levels of the ordinal variable is the same as having different distributions of the ordinal variable within the different categories. This would then just be the regular Kruskal-Wallis test.


Below is an overview of the data that you have collected (or will be collecting): Class Control: Inductive teaching approach (n=14) Student Score Pre Score Post Score Delayed Student 1 x y z ... ... ... ... Student 14 a b c Class Experimentation: Deductive teaching approach (n=16) Student Score Pre Score Post Score Delayed Student 1 x y z ... ... ... ...

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