My dataset comprises of 400 respondents. They are shoppers from different sociodemographic backgrounds.
I asked each of them (among other things) how likely or unlikely they are to purchased ice-cream. Similarly, I asked them how likely or unlikely they are to purchase yogurt. They responded on a five point Likert scale.
I am doing a correlation between ice cream purchase and yogurt purchase by gender. So I have r=.8296 for females and r=.7784 for males. Both are significant at p<0.05.
I have then compared the two corrleation coefficients to determine if there is a difference between purchase by male vs. female. I have used one of the online calculators that can compare the coefficients (The calculator is from a reliable source.). The result is not significant.
Is this the right way to test the three relationships? (i.e. male, female, and male-female)
I have asked this question because someone pointed out that using correlation coefficients to assess relationships in this way is at best a weak approach and at worst it is wrong and misleading. I was told to consider more conventional analyses.
I am not statistically trained (I know that is no excuse) and I have to present my findings to an audience who is not very statistically orientated, so I used Pearsons corrleation (which appears quite straight forward to me).