For my master thesis I conducted an online experiment where participants had to conduct a shopping task where they were provided with a local and a non-local product three times in a row. So for three times, they had to choose between either the local or the non-local product. The result of the shopping task is my dependent variable "Green Shopping Behavior". Every time someone chose the local product, they got a "1" and if they chose the non-local product they got a "0". In the end i added everything up, so for each observation the dependent variable can take the values 1,2 and 3. Originally i thought that my DV is scaled metrically, since the distance between the values is the same and can be quantified. Since I wanted to run a linear regression and have some problems with that I am now asking myself if my variable is even metrically scaled. Could you help me with that? Best regards Carina
These are basically counts, so measurement level is not a problem, these can be interpreted as metrically, interval, or even ratio scaled (some would even call counts an absolute scale).
Note though that just because your variable is scaled metrically, this doesn't mean that a linear regression will work fine with it. Note that generally statistical model assumptions are in terms of distributions and not in terms of measurement scales. Distribution and measurement scale level do not imply each other. I'm not sure whether you want to use your variable as predictor or response, but a response with just 4 possible values (it can also take 0, can't it?) is usually not very well suited for linear regression (note that regression responses are in theory unbounded!). I don't see a problem with such a variable as a predictor.
For response variables of this kind consider Binomial regression.