Conversation—a verbal interaction between two or more people—is a complex,
pervasive, and consequential human behavior. Conversations have been studied across many
academic disciplines. However, advances in recording and analysis techniques over the last
decade have allowed researchers to more directly and precisely examine conversations, in natural
contexts and at a larger scale than ever before, and these advances open new paths to understand
humanity and the social world. Existing reviews of text analysis and conversation research have
focused on text generated by a single author (e.g. product reviews, news articles, and public
speeches), and thus leave open questions about the unique challenges presented by interactive
conversation data (i.e., dialogue). In this article, we suggest approaches to overcome common
challenges in the workflow of conversation science, including recording and transcribing
conversations, structuring data (to merge turn-level and speaker-level datasets), extracting and
aggregating linguistic features, estimating effects, and sharing data. This practical guide is meant
to shed light on current best practices and empower more researchers to study conversations
more directly—to expand the community of conversation scholars and contribute to a greater
cumulative scientific understanding of the social world.