For me odds ratios are something that is (mostly) used after a logistic regression (also called logit regression) has been estimated. And then the odds ratios are evaluated by some estimated parameter in the regression model. That will also give confidence intervals for the odds ratio.

I guess he wants to calculate OR for a 2x2 contingency table, analyzed using a chi-squared.

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Perhaps I'm using it wrong, but I'm using it to establish the relative differences in the span between education in the specific countries. I see that 11.45% is less different from 2.47% than 54.98% is to 16.54% in absolute numbers, but I wanted to show, that the difference is smaller in relative terms. Since the total number of respondents isn't the same across the survey, I needed something else to compare odds for "agree".

This specific question (among loads like it) is: "In times of few jobs, men should have more right to jobs than women" Agree/disagree. This is then paired to education level (primary, secondary and tertiary).

I think after merging two of those three educational profiles, you can run a chi-squred between the numbers (raw data needed) to obtain a P value, and if the result was remarkable, an OR would be good as well (otherwise, the OR is not so useful as it might be close to 1 [edit: or the difference was not representative of a true difference in population]).

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I think it depends on how you have calculated the percentages in the first place. I see in the second country, the sum of percentages surpass 100%, so it seems that each prcentage belongs to the number of individuals responded to that question divided by the number of individuals in that class. So since we don't know how many individuals were in each class, we are unable to base our calculations on these percentages.

For example, if in the second country we have 10, 100, and 1000 individuals in the first, second and third groups of the second country, the number of individuals would be 1, 30, and 200. But If we had 100, 10000, and 10 individuals in those groups, the numbers would differ (based on the given percentages). So we can't sum up the percentages in any of the two groups in order to merge them. Nor we can use these for odds ratio calculations.

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EDIT: and why would I merge the sets? All I need is the high/low values, right?

That is fine to run your test with low and high only, but then a considerable part of your valuable data would be disposed. Merging would give a larger sample, and can also introduce the moderately educated people to your study as well. Although it might also reduce the impact of the extreme groups.

As a suggestion, you can test both conditions (merging or high/low only) and see which one gives you a more good-looking result

and use it.

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BTW what was your P value?