Recent content by ooostats

  1. O

    One-way ANOVA with string variables as factor

    I think you might need to code these as discrete numeric values rather than strings. Let's say in a study testing the effects of 3 different drugs between people, I'll create a factor (column) called "Drug" and it will take on values 0, 1, and 2, where the values might correspond to placebo...
  2. O

    Normality: DV itself or residuals?

    There are lots of interesting points raised in this thread that I'll no doubt be going back to. One thing that I'm a little confused about though is what you said (@spunky): Why are parametric assumptions only really important for inference and not modeling in general? For example if I have...
  3. O

    Normality: DV itself or residuals?

    I guess I tend towards this opinion as well. Although I also think that this gets blown way out of proportion: Some sub-fields are much worse than others, and in different ways (e.g. theoretical vs statistical vs experimental design & control), and journal companies are def not helping given...
  4. O

    Normality: DV itself or residuals?

    Yeah this is what he means. Interesting post. I would like to learn as much as I can about all of this now, just I don't have a maths background so this is quite heavy. And that's the thing, if it were taught in psych classes then it would be quite pointless because most wouldn't have a clue...
  5. O

    Normality: DV itself or residuals?

    Well I do have to say that the Field book is the best book I've used, maybe it's just me not getting my head around this in general. He does state that normality refers to the residuals of the model, "or the sampling distribution". However, we don't have access to the sampling distribution so we...
  6. O

    Normality: DV itself or residuals?

    Good point. I guess I'm one of those students then! So could you both please recommend a textbook/resource that covers these topics correctly? All I ever hear about is Andy Field's book, but I can't see him covering this in a definitive way. On one page we should check assumptions, and our DV...
  7. O

    Normality: DV itself or residuals?

    Not so much counter intuitive, just completely the opposite of what I thought I was being taught! I study psychology, and all resources explicitly talk about checking assumptions prior to applying tests. It makes me wonder whether people teaching/writing the textbooks don't fully understand it...
  8. O

    Normality: DV itself or residuals?

    Thanks, I'll give it a read. Just skimming over the part on normality though, it seems the assumption of normality depends on the difference between the observed data and the regression model's predictions i.e. the residuals. But what I don't understand is that in order to find out what the...
  9. O

    Normality: DV itself or residuals?

    I always thought that we should be testing the assumption of normality by looking at the distribution of the DV in our design (by each level of the IV). But I keep seeing people talking about the residuals of the DV. For example, in ANOVA (and I suppose by extension the t-test and regression)...
  10. O

    Basic Stats - T-test, ANOVA, Chi sq?

    Also, you mention that you are looking at "the mean BMI (normal, overweight, obese)". BMI is a continuous variable, whereas the classifications of BMI into normal/overweight/obese means you treat it as a categorical variable. In either case, you should be clear as to what level of measurement...
  11. O

    small sample size problems

    Right, but how can the results possibly be interpretable? Whatever the result, the risk of it being a T1/T2 is too high. Also if any test were to be used, shouldn't it be a Mann-Whitney U?
  12. O

    small sample size problems

    Your sample sounds like it is just way too small to make any claims like this, especially between groups. You'll need to collect more data and that is your only option if you want to talk about differences.
  13. O

    Selecting the best subject's data and features to optimize the analysis

    Why do you say it is fine to exclude subjects? It's almost never ok to just exclude subjects - especially if the reason is that they don't meet your expectations. The issue is if you cherry pick like that then any model you build will be completely fictitious and not representative/predictive of...
  14. O

    Longitudinal causal modelling

    Wouldn't you need to directly manipulate gut health today (and see how that affects the psychological factors tomorrow) in order to establish causality? I'm curious - I have heard people talking about causality in similar cases and I never quite understand how it is possible without a direct and...