I have 313 males and 356 females. Males and females each took two (gender specific) surveys (an original and a modified version). The survey I'm investigating is a measure of unwanted sexual experiences with parallel versions for men and women (e.g. female version = have you ever been victimized? male version = have you ever perpetrated?). Female's reported rates of being victimized (on the original version) are generally around 15%, while male reports of perpetration are generally around 4%. This discrepancy between female reports of being victimized, and male reports of perpetration, is what I'm interested in. I've revised the original survey into a modified version, and my hypothesis is that the female[victim]/male[perpetrator] discrepancy will diminish by using my modified survey version. So, I'm looking at frequency count data of dichotomous choices (yes [I've had this experience], or no [I've never had this experience]).
I've done the McNemar test for each gender, comparing survey versions and they show that male responses change (significantly) and are increased on the modified version, compared to the original, while female rates are equivalent across survey versions. I've also done paired sample t-tests comparing male-original X male-modified & female-original X female-modified, and the results here also show significant increase in male responding on the modified version, but no significant difference in women's scores on survey versions. So, I know that modified significantly increases male response rates (and does not increase female rates) so that the discrepancy has diminished, but now I want a more direct and statistically potent way of saying directly that the male-female discrepancies are significantly different between measures.
I was wondering if I could do a McNemar's again by combining male and female scores (e.g.
ORIGINAL SURVEY YES NO __________________________________________________ YES | (male YY + female YY) | (male YN + female YN) | MODIFIED SURVEY |________________________|________________________| NO | (male NY + female NY) | (male NN + Female NN) | |________________________|________________________|
Would this configuration preserve the correct proportions to address my question about male-female discrepancy-rate change across survey versions? If I'm off track here, can somebody please suggest some other method for comparing discrepancy rates of two groups on two different measures?