Time-to-event data, such as response times and saccade latencies, form a cornerstone of experimental psychology and have had a widespread impact on the understanding of human cognition. However, the orthodox method for analyzing such dataâcomparing means between conditionsâis known to conceal valuable information about the timeline of psychological effects, such as their onset time and how they evolve over time. The ability to reveal finer-grained âtemporal statesâ of cognitive processes can have important consequences for theory development by qualitatively changing the key inferences that are drawn from psychological data. Well-established analytical approaches, such as event-history analysis (EHA), can evaluate the detailed shape of time-to-event distributions and thus characterize the time course of psychological states. One barrier to wider use of EHA, however, is that the analytical workflow is typically more time-consuming and complex than orthodox approaches. To help achieve broader uptake of EHA, in this article, we outline a set of tutorials that detail one distributional method, known as discrete-time EHA. We touch on several key aspects of the workflow, such as how to process raw data and specify regression models, and we also consider the implications for experimental design. We finish the article by considering the benefits of the approach for understanding psychological states and its limitations. Finally, the project is written in R and freely available, which means the approach can easily be adapted to other data sets.