  Machine learning methods have gained a great deal of popularity in recent
years among public administration scholars and practitioners. These techniques
open the door to the analysis of text, image and other types of data that allow
us to test foundational theories of public administration and to develop new
theories. Despite the excitement surrounding machine learning methods, clarity
regarding their proper use and potential pitfalls is lacking. This paper
attempts to fill this gap in the literature through providing a machine
learning "guide to practice" for public administration scholars and
practitioners. Here, we take a foundational view of machine learning and
describe how these methods can enrich public administration research and
practice through their ability develop new measures, tap into new sources of
data and conduct statistical inference and causal inference in a principled
manner. We then turn our attention to the pitfalls of using these methods such
as unvalidated measures and lack of interpretability. Finally, we demonstrate
how machine learning techniques can help us learn about organizational
reputation in federal agencies through an illustrated example using tweets from
13 executive federal agencies.
