| Summary: | Machine learning (ML) 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 ML methods, clarity regarding their proper use and potential pitfalls is lacking. This article attempts to fill this gap in the literature through providing an ML “guide to practice” for public administration scholars and practitioners. Here, we take a foundational view of ML 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 ML techniques can help us learn about organizational reputation in federal agencies through an illustrated example using tweets from 13 executive federal agencies. All R code, analyses, and data described in this article can be found in the Supplementary Appendix. © The Author(s) 2018.
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