One variable "ruining" an otherwise interesting model I'm running a logistic regression model with several variables. I'm picking variables with a p less or equal to 0.2 to include in the multivariate model, and a lot of variables have really low p values but there is one categorical variable in which a few of the levels have moderate correlations (p values around 0.1-0.2) so nothing exciting but when I include it in my model, which I have to because I specify the correlation as minimum 0.2, the entire thing breaks apart and nothing is interesting anymore. The p values all becomes mediocre and not significant for the other variables, which were very exciting both on their own and in a multivariate model without that one ruining variable.
Why is this happening?
 A: You wrote

I'm picking variables with a p less or equal to 0.2 to include in the
  multivariate model,

This is known as bivariate screening and it is a terrible method of building a model. This has been discussed here before or see Frank Harrell's book Regression Modelling Strategies.

but when I include it in my model, which I have to because I specify
  the correlation as minimum 0.2

Correlation or p value?  In either case, you don't "have to".

The entire thing breaks apart and nothing is interesting anymore. The p
  values all becomes mediocre and not significant for the other
  variables, which were very exciting both on their own and in a
  multivariate model without that one ruining variable.

When a new variable has big effects on the other parameters, that is usually interesting. That is one reason we add control variables. "Ruining" is entirely the wrong word here.  I would look into mediation models.

Why is this happening?

Without context it's hard to say more than "because your IVs are not orthogonal to each other". 
