Indeed, you cannot reliably compare across logit models with different underlying data. Without repeating what has been written before, this post has a very good answer (or see this paper).

In your case, combine the data from different days, and model this:

$answer=\alpha+\beta_1Tues+\beta_2Wed+\beta_3Thurs+\beta_4Fri+\beta_5Sat+\beta_6Sun$

You can do simple Wald tests or likelihood ratio tests to compare whether the coefficients for each day are statistically different. You may find, for example, that there is no statistical difference between Sat and Sun, in which case you could update your model:

$answer=\alpha+\beta_1Tues+\beta_2Wed+\beta_3Thurs+\beta_4Fri+\beta_5Weekend$

You can also estimate the marginal effects of each day, as odds ratios can be confusing or misleading depending on what you are really interested in.

If you have time of day, that can be a multiplying effect, which may moderate the day, though interpreting interaction terms in logit models can be confusing.

In addition, other variables may mediate the effect of the specific day - employment status, marital and parental status, etc. If you have these you may want to include them as controls.

Indeed, you cannot reliably compare across logit models with different underlying data. Without repeating what has been written before, this post has a very good answer (or see this paper).

In your case, combine the data from different days, and model this:

$answer=\alpha+\beta_1Tues+\beta_2Wed+\beta_3Thurs+\beta_4Fri+\beta_5Sat+\beta_6Sun$

You can do simple Wald tests or likelihood ratio tests to compare whether the coefficients for each day are statistically different. You may find, for example, that there is no statistical difference between Sat and Sun, in which case you could update your model:

$answer=\alpha+\beta_1Tues+\beta_2Wed+\beta_3Thurs+\beta_4Fri+\beta_5Weekend$

You can also estimate the marginal effects of each day, as odds ratios can be confusing or misleading depending on what you are really interested in.

If you have time of day, that can be a multiplying effect, which may moderate the day, though interpreting interaction terms in logit models can be confusing.

In addition, other variables may mediate the effect of the specific day - employment status, marital and parental status, etc. If you have these you may want to include them as controls.