# Use of 3 different methods - is it the right approach?

I am writing a master's thesis on crowdfunding. For this research I set N independent variables and 3 DVs. The reason for doing so is that I want to explore the phenomenon from the aspects of all 3 DVs, as they are giving me different insights, and to crosscheck the results from all 3 models. Here is a bit more details on the three DVs and the models:

1. Success [dummy] - I will use logistic regression for that.

2. Success Ratio - Ratio variable [0-N] - I use Linear Regression and to linearize the model I use the following transformations:
$\ln(x+1)$ to keep the zeroes, as the distributions are heavily skewed. ==> $\ln(y+1)=b_0+\ln(x_1+1)b_1$..., etc.

3. N_Backers - Count variable [0-N] - I plan to use a Poisson GLM with log function, but it might be reasonable as well to be inline with DV2 and to use Linear Regression with transformed variables $\ln(y+1)=b_0+\ln(x_1+1)b_1$..., etc.

Question: What is your advice on these methods, do they sound reasonable? Further, should I use linear or Poisson for the 3rd DV? My concerns are that I am not sure how the results will be interpreted using all different models. Probably the use of consistent models is the right approach.