Aaron Bycoffe, a computational journalist for FiveThirtyEight reports on Introducing The Trump Score.
Scriber’s disclosure: In my academic career I did some research at the intersection of computational linguistics and computational models in cognitive psychology. So I am a true computer geek when it comes to things computational. Computational journalism? How cool is that? Now getting back on track …
Donald Trump has Republican majorities in both chambers of Congress — it’s the first time since Barack Obama’s first two years in office that the same party has controlled the U.S. Senate, the House and the White House. Trump’s ability to enact his policies, therefore, will largely come down to how often GOP senators and representatives buck the president’s agenda and, conversely, how often Democrats work with him. To help keep up with this, we’ll be tracking how often members agree with Trump and how that compares with expectations.
We’ll be using two primary measures for each member of Congress: the “Trump score” and “Trump plus-minus.”
The Trump score is a simple percentage showing how often a senator or representative supports Trump’s positions. To calculate it, we add the member’s “yes” votes on bills that Trump supported and his or her “no” votes on bills that Trump opposed and then divide that by the total number of bills the member has voted on for which we know Trump’s position.
We’re also calculating a metric that we’re calling plus-minus. Plus-minus measures how frequently a member agrees with Trump compared with how frequently we would expect the member to, based on Trump’s 2016 vote margin in the member’s state or district. (The “predicted score” is calculated based on probit regression.) Put simply, we would expect a member in a district where Trump did well to be more in sync with him than a member in a district where Trump did poorly. As members vote on more bills, their predicted agreement score will change.
I’ll try to track the Trump score and report it here as available.