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@Karabiner - funny enough I read recently from someone of influence to just always use robust SEs as well. I don't think I will unless there is an obvious threat. And yeah, I have also heard it is just safe to always use pooled SEs in ttests. @tt13 - you should look for heterogeneity. Even if...

Can you provide a snippet of your data (fictitious or not), so we can better understand what you are writing about. Thank you.

You could, but if you think about the number of Obs, things will get fairly sparse. Meaning I believe with ordinal logistic reg you will be comparing multiple groups to a reference, plus the other main effect term and the interaction. So you would be comparing 1 to 5,..,1 to 2.

What is your sample size, how many school types are there, and how many options are in the Likert scale and what do the responses look like (near the top, bottom, or middle of response options)?

Not traditional practice yet, but I would fit a multiple quantile regression model. The process is just like linear regression, but you wont have to worry about that hideous DV. I haven't done one with a binary*continuous interaction, but it seems like it should be fine.

What do the distribution of response to the DV look like? Are they centered, or a whole bunch right at 5?

Please define your sample size and provide more information about the DV.

Well you also have your intercept, which would be when both predictors are set to zero, then purchage type when custom set to zero, vice versa, and then the when both set to 1. It is all about making sure you know which variable group is set as the reference, we can't tell from your post. Can...

What program do you think you will be using? I just grabbed this image off the internet, but this is what you would expect as the output: You sent them up similar to a regression and get output like this for each variable. So the y-axis is for the outcome and the x-axis is the comparison of...

I would look at the partial R-squared values in a multiple regression. So regress true height on X1 and X2 and calculate the partial R-square values with confidence intervals. This isn't a direct comparison between them, but it will quantify the amount of variability in Y explained by each...

You need to think about the relationships between the variables. If it is exogenous, it won't be a big deal. If it is a mediator, you just won't know its effect but its cause should help explain the outcome. It all depends on the purpose. If you are using ORs, RRs, or RDs as your outcome...

I have told you this before, run the model with and without the extreme outliers, if it doesn't substantially influence the estimates you are fine.

Ditto!

Cold people are not the only ones that shake.

I wouldn't call it post hoc, since you haven't ran the study analyses yet. Post hoc power analyses are a major faux pas, since most people do them after finding significant results.

No problem. Welcome to the forum!

If you are just desiring to report on historic data and are not generalizing or predicting, then you are correct. So your conclusions will be your conclusions. Though you can still use stats (means, correlations, rates), you just don't have to add precision values (confidence intervals or...

Please better describe this, it currently isn't clear what you plan to do. Also, since this is time to event data, a proportional hazards model may be more appropriate.

T cells, yes. I'll update duration next week, but someone could have 95% lower antibody response hypothetically, though what is the true threshold for not being able to combat exposure. UNKNOWN.

Aerosalized transmission is the treat. Idiots at my gym have never worn masks. I have been fully vaccinated for months and always wear my mask, part of the 3% that do. Though these idiots all of a sudden wipe the machines down, which wouldNt be such an issue if you wore a mask and balked...