I have a data set that is the result of a survey. The survey asks the respondents to name 5 people in their community whom they turn to for advice. It then goes to these 5 people and asks the same. I have calculated in-degree for each person (the number of people who have chosen them) and I would like to understand how the person's various features affect their in-degree.
The survey asked each person their age range, gender, location, religion, how often they read/watch news/go online..etc. (daily, weekly, monthly), job and other similar questions considering the person. I have information missing about 44% of the people who were mentioned, since not everybody who was mentioned was surveyed.
They also asked questions about the relationship between the person and the person they turn to for advice - how long thay have known each other (5-10 years, less than 5 years, 10-20 years, over 20 years), how often they meet and other similar things. I have relationship information for every connection.
I would like to understand which predictors best affect a person's chance to have a high in-degree as well as which relationship attributes influence a person's decision to choose somebody as the person they turn to for advice.
Which statistical methods should I be looking into? Do I need to transform all my categorical variables? What would be the best way to do that? Also, in-degree distribution, so the outcome variable, is not normally distributed, it is extremely left-skewed.