Data from our local hospital's Emergency Department suggests that there are certain conditions with which females present more often than males. I wish to determine whether the difference in observations is statistically significant.
The following is the gender breakdown for all Emergency Department patients:
Gender GenderCount
F 70001
M 55466
Which is about a 5:4 ratio, or 1.26 females for each male.
The following is the gender breakdown for Emergeny Deparment patients who present with the studied condition:
Gender GenderCount
F 8516
M 3836
Which is about an 11:5 ratio, or 2.22 females for each male.
Is the correct test to use a Chi-Square Test of Independence? My current approach is to view the variables this way:
Condition Presented
Studied Condition | Any Other Condition
male | |
--------|-----------------------|---------------------------
female | |
Next, I would like to repeat the process for an arbitrary number of other conditions. Is there a good way to perform the same test on a set of independent categorical variables, at once? For example, could I perform a Chi-Square Test of Independence on the following matrix:
Condition Presented
Condition 01 | Condition 02 | ... | Condition n
male | | | |
--------|----------------------|---------------------------|---------------|--------------------
female | | | |
My data is in a SQL server, and I can use R.
I actually have data for all hospital departments, so should I be starting with that data as population data and treating the Emergency Department set as a sample?
Is a Chi-Square Test of Independence the right approach?
Bonus Question (not required for accepted answer) - If I get a statistically significant result, what measure of association should I use for this kind of data?