In this Methods article, we discuss and illustrate a unifying, principled way to analyze response time data from psychological experiments—and all other types of time-to-event data. We advocate the general application of discrete-time event history analysis (EHA) which is a well-established, intuitive longitudinal approach to statistically describe and model the shape of time-to-event distributions. After discussing the theoretical background behind the so-called hazard function of event occurrence in both continuous and discrete time units, we illustrate how to calculate and interpret the descriptive statistics provided by discrete-time EHA using two example data sets (masked priming, visual search). In case of discrimination data, the hazard analysis of response occurrence can be extended with a microlevel speed-accuracy trade-off analysis. We then discuss different approaches for obtaining inferential statistics. We consider the advantages and disadvantages of a principled use of discrete-time EHA for time-to-event data compared to (a) comparing means with analysis of variance, (b) other distributional methods available in the literature such as delta plots and continuous-time EHA methods, and (c) only fitting parametric distributions or computational models to empirical data. We conclude that statistically controlling for the passage of time during data analysis is equally important as experimental control during the design of an experiment, to understand human behavior in our experimental paradigms.

在这篇方法文章中，我们讨论并说明了一种统一的、有原则的方法来分析来自心理实验的响应时间数据——以及所有其他类型的事件发生时间数据。我们提倡离散时间事件历史分析 (EHA) 的一般应用，这是一种行之有效的、直观的纵向方法，用于统计描述和建模事件时间分布的形状。在讨论了在连续和离散时间单位中事件发生的所谓风险函数背后的理论背景之后，我们使用两个示例数据集（掩蔽启动、视觉搜索）说明了如何计算和解释离散时间 EHA 提供的描述性统计数据。 ）。在判别数据的情况下，响应发生的危害分析可以扩展为微观水平的速度-准确性权衡分析。然后，我们讨论获得推论统计的不同方法。与 (a) 比较均值和方差分析，(b) 文献中可用的其他分布方法（如 delta 图和连续-time EHA 方法，以及 (c) 仅将参数分布或计算模型拟合到经验数据。我们得出结论，在数据分析过程中对时间流逝的统计控制是 (b) 文献中可用的其他分布方法，例如增量图和连续时间 EHA 方法，以及 (c) 仅将参数分布或计算模型拟合到经验数据。我们得出结论，在数据分析过程中对时间流逝的统计控制是 (b) 文献中可用的其他分布方法，例如增量图和连续时间 EHA 方法，以及 (c) 仅将参数分布或计算模型拟合到经验数据。我们得出结论，在数据分析过程中对时间流逝的统计控制是与实验设计过程中的实验控制同样重要，以了解我们实验范式中的人类行为。