**Introduction to event history analysis for psychologists** **Sven Panis**<br/> *Experimental Psychology Unit, Technical University of Kaiserslautern, Kaiserslautern, Germany* Time-to-event or survival data are ubiquitous in psychological research. Examples include response time (RT) data, saccade latencies, fixation durations, time-to-force-threshold data, perceptual dominance durations, etc. In this 3-hour tutorial I will explain the basic concepts of discrete-time survival analysis (a.k.a. event history analysis, hazard analysis, duration analysis, transition analysis, failure-time analysis) which is the standard longitudinal approach in many scientific disciplines to study whether, when, and why certain events occur (an event is any qualitative change that can be situated in time, e.g., marriage, death, button-press, saccade onset, etc.). Each distribution of event times is described using the discrete-time hazard probability function of event occurrence, and hazard functions can be modeled using generalized linear mixed effects regression. In the case of choice RT data, for example, the hazard analysis of response occurrence is extended with an analysis of the timed accuracy data using micro-level speed-accuracy-tradeoff functions. R code is provided that allows participants to statistically describe and model their time-to-event data (see the supplementary resources of this paper by Panis & Schmidt, 2016: What Is shaping RT and accuracy distributions? Active and selective response inhibition causes the Negative Compatibility Effect). Participants are expected to bring a laptop with R and RStudio installed, and their own empirical data set (with RT data, saccade latencies, fixation durations, or any other type of time-to-event data).