I'm conducting a study about social media posting with pets. My dependent variable is pet posting frequency (total pet pictures/total pictures posted), and a significant number of participants did not post pictures with pets in the 2-week time frame that I set, leaving a lot of zeros in the dataset. The independent variables are various psychological measures that are scored to indicate things like anxiety and depression.
Because of the zeroes, I'm told I won't be able to use multiple regression as I had planned, and that I will need to analyze the data using nonparametric statistics. Here were some of the suggestions:
1) split the data into those who post/ those who don't, then I can use logistic regression. There is also the potential to use an ordinal logistic regression model and to separate the dataset into three categories: those who do not post at all, those who post a little, and those who post a lot of pet pictures (though these parameters would be determined arbitrarily).
2) A negative binomial model and a poisson model
If anyone can explain the pros/cons, or what the most statistically sound approach would be to figuring out an appropriate regression to model this, I'd really appreciate it. Also, any additional information about the above 2 suggestions or others (examples, links) would be immensely helpful. I'm not a statistics person, so if you can explain in as basic terms as possible that reference my problem, that would be most helpful.
Thanks in advance for your time.