I agree that pitting the “top priority” group against the “no resources” group in a correlation is an odd way of approaching the data. I was trained not to create dichotomous variables unless advanced statistics tell us that the data is, indeed, dichotomous. Doing so gets rid of variance that may well be useful. The current analysis goes even further by excluding large numbers of people, as you noted.
I would be interested in seeing non-parametric correlations that can deal with ordinal data (e.g, Spearman’s) used on the full sample. I’m guessing the story may be different.
I was trained not to create dichotomous variables unless advanced statistics tell us that the data is, indeed, dichotomous.
I had not heard this before your comment, probably due to being early in my coursework, but it makes perfect sense to me. I will remember this guideline in the future. Thank you for the feedback.
Hi David,
I agree that pitting the “top priority” group against the “no resources” group in a correlation is an odd way of approaching the data. I was trained not to create dichotomous variables unless advanced statistics tell us that the data is, indeed, dichotomous. Doing so gets rid of variance that may well be useful. The current analysis goes even further by excluding large numbers of people, as you noted.
I would be interested in seeing non-parametric correlations that can deal with ordinal data (e.g, Spearman’s) used on the full sample. I’m guessing the story may be different.
I had not heard this before your comment, probably due to being early in my coursework, but it makes perfect sense to me. I will remember this guideline in the future. Thank you for the feedback.
No problem, best of luck! :)