Lucia - I would stratify by age; together with gender, your project would look more appealing - in my opinion.

I found this link useful to chose an appropriate test.

https://www.scribbr.com/statistics/statistical-tests/

Use the chi-squared test for the goodness of fit with 50/50 as expected frequencies.

Example in R (see RCompanion

https://rcompanion.org/rcompanion/b_03.html

):

observed = c(770, 230) # observed frequencies

expected = c(0.75, 0.25) # expected proportions

chisq.test(x = observed,

p = expected)

It is not clear which hypothesis you want to test.

If you want to check whether men (or women) are more likely to fall sick, you can check if the number of cases (in other words, the proportion of positive cases) is higher in men (or women) than in the other sex.

Unfortunately, you do not provide enough information about your study population and how you obtain the number of COVID cases. But I think that your two variables can somehow be considered as qualitative variable: if your study population corresponds to people who have been tested for COVID-19, and the number of COVID-19 cases corresponds to the proportion of people with a positive COVID-19 test, then you can use the dichotomous variable "COVID-19 status": positive or negative.

In that case, I can suggest you compare the proportion of positive cases in men and women, using the Khi-Square test. Or you can also use a logistic regression with "COVID-19 status" (positive vs negative) as the dependent variable and sex as the independent variable.

https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiG2c2I5_XuAhUsAWMBHaICA5EQwqsBMAt6BAgtEAM&url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3D4K9PVYqiA4c&usg=AOvVaw3ixc8vF81KFYWcoioWSTAI

Kindly check https://www.researchsquare.com/article/rs-72021/latest.pdf

Also check https://bmjopen.bmj.com/content/10/10/e040129

And https://pubmed.ncbi.nlm.nih.gov/32753136/

Lucia - I would stratify by age; together with gender, your project would look more appealing - in my opinion.

A variable is a characteristic of an any object. The values may occur more than once for a set of data. There are two main types of variables, Quantitative Variables and Qualitative Variables.

Quantitative Variables: refers to the values result from counting or measuring something. For examples: height, weight, time, number of items, etc.

Qualitative Variables: Variables that are not measurement variables. Their values do not result from measuring or counting.

In the link below I wish you find what you are need . with my respect.

Gausman J., Langer A. Sex and gender disparities in the COVID-19 pandemic. J Womens Health (Larchmt) 2020;29(4):465–466. - PubMed

It seems that you first ask for scales and second appropriate analysis techniques. If school degree is linked to persons it can be seen as a ordinal scale which is not anymore really quantitative. But as you are able to define ranks for salaries as well as the degree level you can calculate a Spearman rank correlation. Nationality is to me a nominal and by this a qualitative variable. If you have N different nationalities in your data you can transfer them to N-1 binary variables (if all N-1 binary are 0 the nationality is the last "N" one - be careful not to use N binary as otherwise you create a multicolinearity in your dataset of independent variables). Of course you can use regression with binary data - by this you will see which nationalities have a significant positive influence to salaries. Finally - which is statistically not really proper you also can treat an ordinal variable as quantitative if there is not a to big difference between Pearson and Spearman correlation. But doing this you should be carful with interpreting test statistics. And very finally you also can code a variable degrees with D degree levels as D-1 binary variables.