What we’re geeking out about . . .

In Democracy’s Data, author Dan Bouk digs into the evolution of the census and the U.S. government’s process for deciding how to turn the people living in the U.S. into data points.

The government’s intentional omissions (for example, removing the category “Mexican” as a response option, which effectively erased the entire Mexican American population from historic records) are as important as the additions, like adding “partner” as a response option to describe how two adults in a household were related.

Alexandra Jacobs of The New York Times called the book “endearingly nerdy.” Frankly, we can’t think of a bigger compliment. If someone called those of us at LRA “endearingly nerdy,” we’d be pretty darn pleased. (Heck, it’s what inspired the name of this newsletter.)

We love the history and the detail in this book, but more than anything, we love how it brings to life a philosophy around data and data collection that we strive to acknowledge in every study we conduct at LRA.

In our view, data is not entirely objective. Every data point collected and described is the result of dozens (and sometimes hundreds) of decisions made by imperfect humans rife with their own biases and ideas about what is and isn’t important.

At LRA, we’re constantly considering how our own biases and choices impact each step we take on the decision-making path when designing studies and evaluations, developing hypotheses, selecting analytic approaches, and generating reports.

As an example, think about the word “only.” Only is a hugely loaded word when you stick it in front of a data point. Consider this sentence: Only 20% of participants reported increases in physical well-being after completing the 10-week course.

What happens when you remove “only”?

Suddenly, the reader has an opportunity to react to the information from their own perspective. One reader might think that 20% is a pretty darn big deal. Another reader might think that a 20% increase isn’t impressive enough for them to invest in that particular curriculum.

Democracy's Data

Here are some of the things we ask ourselves:

  • How can we identify and be aware of our own biases so we can better reduce their impact?
  • How can we err on the side of scientific rigor within the constraints of the study context?
  • How can we present meaningful results when writing up projects while still allowing the reader to interpret the results through their own lens?
Copyright © 2023 - Lynch Research Associates | Website by Cold Spring