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1

I strongly suspect that this particular part of the table, explicitly based on a multi-predictor logistic regression model stratified by sex, has an error in the p-value entry. The odds ratios are presented with respect to a reference category for each of the predictors, so that the p-values should represent the p-value corresponding to a null hypothesis of ...


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The research question is: I want to check the relation of the fixed effects to the dependent variable, taking into account the fact that the design is clustered. This is answered by the fixed effects estimates. How do I make interpretation of the random effect Generally there is no requirement to interpret the random effects - you are controlling for ...


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I assume the software you are using transforms them into Fisher's $z$ (the inverse hyperbolic arctangent). What you suggest is a plausible way forward but note that the value of $z$ is quite sensitive to small changes near unity. For r=0.99 it gives 2.65, for r=0.999 it gives 3.80, and for r=0.9999 it gives 4.95. It might be a good idea to use several values ...


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Neither. Those distributions are used only to approximate the distribution of your data. First of all, normality checking is pretty useless procedure. Use the distribution that is useful as an approximation of the distribution of your data, that shares the characteristics you find important. E.g. if the data is continuous and the distribution is roughly ...


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Normally data from such Likert scales are interpreted as holding ordinal but not interval scaled information, and the linearity assumption in standard linear regression will be problematic for sure with a response variable that only has 3 output values (it may be OK with Likert explanatory variables but then it may not, depending on the data). There's ...


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Thanks for making a reproducible example. That meant it was possible to give you an answer. I always find manova to be a painful technique, and it's never clear to me what it's actually testing. You've made a mistake in your manova code (I believe) because you've run a model with no intercept. It should be: m <- manova(cbind(PEVOCAB, RAVIN)~ NS + NA. + ...


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I am not very familiar with SPSS but as far as I see MANOVA in SPSS reports multivariate tests for each predictor separately (just like manova in R). So, it does not report multivariate tests of significance for EFFECT..WITHIN CELLS REGRESSION (maybe there are additional options). However, you can obtain these results using canonical correlation in SPSS and ...


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