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  1. spunky

    Sports data set

    You can't treat the same team as "different" teams because they're correlated and mess up your analyses. You'd need to do some type of longitudinal modelling line time series or mixed-effects models to account for the repeated instances over time.
  2. spunky

    Am I posting questions in the wrong place?

    Have you considered posting on cross-validated? Sometimes questions that aren't answered here get an answer there!
  3. spunky

    Looking For The Formula Used for the Chi Square Table

    Well... you need to use the quantile function or inverse CDF of the chi-square distribution for that and I don't think there are closed-form expressions for it because of the gamma functions involved.
  4. spunky

    HLM Simulation

    Let's do this step by step. We'll start with something simple and build towards a full HLM. First, you have a few important-yet-ambiguous statements that need more precise definitions. What does "general characteristics of the attachment", "predicts the burnout risk" and "higher the attachment...
  5. spunky

    Some questions to answers

    Perfect. That means my "help" had the intended effect.
  6. spunky

    Some questions to answers

    This seems very relevant to these questions Particularly to Question 10.
  7. spunky

    Serial & Parallel mediation Hayes Process

    You mean there is no model in the Hayes PROCESS macro that matches yours?
  8. spunky

    Factor analysis, determinant < .00001

    Can you post the matrix on here?
  9. spunky

    Power analysis for moderated mediation (and a small research design question)

    For models like yours the only way to approximate power is through a computer simulation. This can get you started:
  10. spunky

    Non-linear diagnostics

    What's with the groupings in the "Allowed" plots? Do you have a categorical predictor there?
  11. spunky

    Journals statisticians read

    Well, the Harvard Data Science Review is a good place to start:
  12. spunky

    Regression model and G*Power

    I'm sorry but what is the question here? What you're reporting seems pretty normal with respect to how power works.
  13. spunky

    Simplifying Binomial Coefficients with large numbers, i.e., factorials of large numbers

    You're running into a combinatorial explosion problem, which is very typical of discrete mathematics. But it shouldn't be too difficult to simplify the expression to something you can evaluate. If you're unfamiliar with how to simplify factorial ratios, here's something to get you started:
  14. spunky

    What's the probability of getting at least 3 concecutive heads in 10 tosses of a coin?

    This seems useful and relevant to your question
  15. spunky

    Is my scatterplot homoscedastic/heteroscedastic?

    Definitely heteroskedastic. Is your dependent variable discrete?
  16. spunky


    Thank you. I was a tad bit surprised that there would be a proc for power in the case of generalIZED linear models (glims) as opposed to generaL linear models (glm). Anyhoo, with this out of the way the answer is simple: there is no such proc because there are no closed-form expressions for the...
  17. spunky


    Do you mean "glm power" or "glim power"? Believe it or not, the "i" can make a world of difference here...
  18. spunky

    Robust standard errors and REML

    Ok, this makes more sense now. Sure, the p-values should change because you're messing the the standard errors in one analysis (using the robust options) and in the other you're letting them be what they are. And you have a small number of clusters (11) which implies a small sample at the...
  19. spunky

    Robust standard errors and REML

    This is to be expected because both approaches are doing different things to the data. But different regression coefficients? Nah, that seems buggy. Although...wait. When you said "I re-ran the model using robust standard errors" does that mean you ran OLS regression with robust/sandwich...
  20. spunky

    Bayesian statistics for Spearman's

    Couple of things to keep in mind: The Spearman correlation captures a very specific type of association, monotonic. So a small rank correlation does not imply a lack of relationship. If you have two variables X and Y and define Y = X^2 you can see that the rank correlation is very small (it's 0...