Modelling Discrete Time-to-Event Data Gerhard Tutz and Matthias Schmid Springer International Publishing, 2016, x + 247 pages, $89.99, hardcover ISBN: 978-3-319-28156-8

2017 ◽  
Vol 85 (3) ◽  
pp. 542-542
Author(s):  
Shuangzhe Liu
Keyword(s):  
Discrete Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
International Publishing

scholarly journals Analyzing Response Times and Other Types of Time-to-Event Data Using Event History Analysis: A Tool for Mental Chronometry and Cognitive Psychophysiology

i-Perception ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 204166952097867
Author(s):  
Sven Panis ◽  
Filipp Schmidt ◽  
Maximilian P. Wolkersdorfer ◽  
Thomas Schmidt
Keyword(s):  
Discrete Time ◽  
Event History Analysis ◽  
Masked Priming ◽  
Event History ◽  
Event Data ◽  
Time To Event ◽  
Advantages And Disadvantages ◽  
Time To Event Data ◽  
History Analysis ◽  
Speed Accuracy

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.

scholarly journals Sequential tests of promise with discrete time-to-event data

2019 ◽  
Vol 85 ◽  
pp. 105818
Author(s):  
Bruce Levin ◽  
Louise Kuhn ◽  
Cheng-Shiun Leu ◽  
Wei-Yann Tsai
Keyword(s):  
Discrete Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Sequential Tests
Sign in / Sign up

Export Citation Format

Share Document