# What kind of regression model would best suit this scenario?

I'm new to statistics so this may seem an obvious question for you guys. I have created a survey to collate peoples opinions on how U.S anti-money laundering regulation has impacted their financial privacy.

The survey is completely anonymous if you are interested and found here:

https://forms.gle/9Fwm1nH9E8P4ioPR9

So I am trying to model financial privacy loss (dependent variable) against U.S anti-money laundering regulation such as the Patriot Act / Bank Secrecy Act ...(the independent variables). In total I have 4 independent variables or 4 pieces of regulation.

In my model the independent variables can impact the dependent variable in 3 ways: Weak, Moderate, Strong.

From what I understand this scenario is trichotomous and not binary, and also discrete not continuous as there is no in-between)

For example: The Bank Secrecy Act has had a strong impact on financial privacy loss.

So my question is: What kind of regression model would best suit this scenario?

One example would be this to create a vector named 'Bank Secrecy Act' and use it as an independent variable. This variable is categorical taking values in the set $$\{weak, strong, moderate\}$$. You can use one-hot encoding to create dummy variables, which can then be used as an input in a regression model. Since you have 3 states you need 2 dummy variables. One example would be:
• $$[0,1]$$ indicates $$weak$$
• $$[1,0]$$ indicates $$strong$$
The absence of dummies, $$[0,0]$$, is the base case. In this example this correspond to $$moderate$$ effect. Note that most packages create these implicitly, so you only need to create the input vector prior to fitting the model.
• For example the glmnet package in R does this automatically. I would suppose SPSS has the same functionality, although I've never tried it to be honest. – Akylas Stratigakos Jul 24 at 15:06