Hello everyone,
I am writing a report looking at human sexual selection and how there is a difference in the preferred age of potential mate between sexes. I collected 104 lonely hearts advertisements and organised the data by grouping them into gender, age of person placing the advert (I have placed them in categories) and preferred age of potential mate (calculated median from a range).
The data distribution was abnormal so I went ahead and did a Kruskal-Wallis ANOVA between gender and median potential mate. I then wanted to strengthen the analysis by looking at the interaction of gender and age of person placing the advert on the preferred age of potential mate. The problem I had was that Kruskal-Wallis will not allow more than one grouping variable. To circumvent this, I attached a gender to each age category, essentially having two Independent factors in one column (Attached picture may make this clearer). I can do a pairwise comparison of each case now but I'm wondering if it is ok to group two factors in this way? would it be better to rearrange the data completely and do different tests?
Any help on this would be greatly appreciated,
L
Dear Kerry, I would advise subtracting the preferred age from the middle value of the age interval for each person. This will transform your data into a single numerical value. You can then run a t test or a Mann Whitney U test.
Are your data non-normal? or are the residuals from the fit non-normal? In general, we aware whether the error term in the ANOVA is normaly distributed. In other words, if the residuals look normal, then there is no significant problem, which is means that you can use two-way ANOVA.
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