Recent content by hlsmith

  1. hlsmith

    Ignoring nested data structure?

    Your context is different from mine. Mine will usually be medical patients in the dataset more than once (so obs clustered in patients) or patients clustered in hospitals. Controlling for it, allows you to control for the between and within group variability and define if there is a difference...
  2. hlsmith

    Ignoring nested data structure?

    Wait - does this mean people could contribute more than one observation, and that the values would be correlated. If so, fitting an empty model controlling for the covariance structure is necessary. I usually liken this to allowing a person to vote twice or more. In the non-descriptive - but...
  3. hlsmith

    Ignoring nested data structure?

    I guess it depends on the purpose and how you are using the results. I believe in the past, I have fit empty models but controlled for the clusters. Can you tell us more about your context.
  4. hlsmith

    Methods for group comparison

    Yes that is the package. Welcome to the forum!
  5. hlsmith

    Methods for group comparison

    You will likely have issues. I would conduct analyses using your own data and make superficial comparisons in your discussion section. Even if you had the data, there could be missing confounders or selection bias making the sets not comparable. Of note, I know in R software there is a package...
  6. hlsmith

    Interpreting an interaction term

    Post the output from the model and we can help. But all coefficients in in reference to your base case (e.g., intercept). Some programs will let you define effect estimates to get at the other terms. SAS: LSMEANS R: emeans
  7. hlsmith

    Bonferroni correction

    I have never seen a correction for having an interaction and its main terms in the model.
  8. hlsmith

    Control variables

    If the IV is deterministically created from the other variables, they wouldn't contribute anything new. But you should tell us more, if you want additional insight.
  9. hlsmith

    Chi square on a huge dataset

    So where are these data coming from. How are you simulating them and the source of the reference? Are they supposed to be counts?
  10. hlsmith

    Question about the normality assumption

    That is how I read it. It it makes sense - but I hadn't thought about it that way.
  11. hlsmith

    Question about the normality assumption

    Yes, you could just use regression for a ttest here and examine residuals. Have you run many regressions? Because if you have, you will note that the residuals can help discern a non-linear relationship, wrong defined data generating function, or heterogeneity resulting in a funnel shape in...
  12. hlsmith

    Question about the normality assumption

    I hadn't heard this before. Can you elaborate.
  13. hlsmith

    AIC models

    P.S., This concept would fall under bias/variance trade off. Less variables means more potential bias and more variables (given finite sample) means greater standard errors. If you have a loss function or value you are trying to optimize (e.g., MSE or accuracy) -- having a random holdout set...
  14. hlsmith

    AIC models

    You need to create a table with the listed variables in the model along with the AIC values and share it - selection based on pvalues isn't always great. Model reduction can also be investigated using Least absolute shrinkage selection operator based regression. Though it is important to...