I am doing a marketing research on whether purchase intention towards a popular brand is affected by people's identification with a socially responsible organisation (Fair trade) and their perception of the corporate social responsibility of that brand. My survey asked the participants for their purchase intention towards that brand, then their identification with Fairtrade and perception of corp. social responsibility questions and after that I showed the that the Fair trade foundation and that brand are related, so I asked about their purchase intentions again. I conducted normality tests and none of the constructs was normally distributed. Can I use a Multivariate Analysis for the data even though it's not normally distributed (and if not what test would be better) ?
Please take a look at my paper CSR: Croatian consumers' response available on RG - maybe it can give you an idea.
Wouldn't a SEM be possible in your case? Here you will find several strategies for dealing with non-normally distributed data (MLR, Bootstrapping, ... ). Maybe this is a good question for SEMnet?
It doesn't sound much like your data is going to meet the multivariate normality assumption which is pretty important for MANOVA. You haven't said what you plan to do with the data but I assume since you mentioned MANOVA that you have more than 1 dependent variable. Thus I would start with a contingency table analysis to see if these dependent variables are statistically independent of each other. If they are then life is good and you can quickly separate them into several univariate analyses for which would recommend some type of logistic regression as a starting point. I am attaching a set of notes by Frank Harrell, a real expert in this area. I would say the chances of success on this route are about 80%. Look over Frank's discussion, his examples and his software(which is Free by the way) and see if this looks appropriate for your research questions. If not, come back and we will try again. Best wishes, David Booth
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