Who is this website for?
Our world is complex. To make sense of it, data analysts routinely fit sophisticated statistical or machine learning models. Interpreting the results produced by such models can be challenging, and researchers often struggle to communicate their findings to colleagues and stakeholders. Model to Meaning is a book designed to bridge that gap. It is a practical guide for anyone who needs to translate model outputs into accurate insights that are accessible to a wide audience.
Model to Meaning introduces a conceptual framework to help you describe the statistical quantities that can shed light on your research questions, use models to estimate those quantities, and communicate the results clearly and rigorously. Based on this conceptual framework, the book proposes an analysis workflow which can be applied in consistent fashion to (almost) any model you need to fit.
The key idea that underpins this workflow is that we can often transform the raw parameter estimates obtained by fitting a model into quantities that are much easier to interpret. Converting results to a scale that feels natural to our audience can improve transparency, communication, and impact.
Part I of the book lays the groundwork by encouraging analysts to clearly define their goals, and by introducing a simple conceptual framework to guide model interpretation. Part II describes how this framework can be operationalized through quantities of interest and tests, using concrete examples and real-world datasets. Part III presents detailed case studies which demonstrate how a consistent workflow can be applied in model-agnostic fashion to various settings.
This book was conceived to empower a broad range of people—including data scientists, researchers, and students—who want to improve their ability to interpret and communicate the results produced by statistical or machine learning models. It is a book for the novice who seeks new practical skills and understanding; but also for the seasoned researcher who is ready to unlearn some old patterns, and embrace new tools that can improve their productivity and impact.
The level of technical sophistication required to follow the presentation is modest. Readers familiar with empirical strategies like logistic regression should feel comfortable with most of the material. Some of the case studies in Part III cover more advanced modelling approaches, and extra reading materials are cited when appropriate.
Throughout, explanations are accompanied by detailed code examples in R, with Python translations collected in 40 Python. The main software library used to interpret models—marginaleffects—is free, open source, and well documented (Arel-Bundock, Greifer, and Heiss Forthcoming). Readers who are not yet familiar with basic data manipulation commands in R or Python may want to consult an additional reference, such as Telling Stories with Data (Alexander 2023), R for Data Science (Wickham, Çetinkaya-Rundel, and Grolemund 2023), or Python for Data Analysis (McKinney 2022).
Writing this book would not have been possible without the help and feedback of many marginaleffects users, readers, and contributors. I warmly thank Rohan Alexander, Arthur Albuquerque, Marco Mendoza Aviña, Marcio Augusto Diniz, Etienne Bacher, Tyson Barrett, Daniel K. Berry, Mattan S. Ben-Shachar, Nicholas J Clark, Mark Clements, Simon P. Couch, Sam Crawley, Maël Coursonnais, Brett Gall, Isabella R. Ghement, Nadjim Fréchet, Alexander Fischer, Stefan Hansen, Karl Ove Hufthammer, Philippe Joly, Adrien Lamarche, Florence Laflamme, Daniel Lüdecke, Grant McDermott, Artiom Matvei, A. Jordan Nafa, Reiko Okamoto, Demetri Pananos, Roel Verbelen, Resul Umit, Matt Warkentin, Brenton Wiernik, Aaron Zipp.
Parts of this text were adapted from an article by Arel-Bundock, Greifer, and Heiss (Forthcoming) in the Journal of Statistical Software. Like all articles published by the JSS, this text is published under a permissive license.1 I thank my co-authors Noah Greifer and Andrew Heiss for their contributions to this article, the marginaleffects documentation, and code.
I acknowledge the use of LLMs as writing aids.