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Month: August 2016

Exploring response time distributions using Python

Inspired by my post for the JEPS Bulletin (Python programming in Psychology), where I try to show how Python can be used from collecting to analyzing and visualizing data, I have started to learn more data exploring techniques for Psychology experiments (e.g., response time and accuracy). Here are some methods, using Python, for visualization of distributed data that I have learned; kernel density estimation, cumulative distribution functions, delta plots, and conditional accuracy functions. These graphing methods let you explore your data in a way just looking at averages will not (e.g., Balota & Yap, 2011).

Kernel density estimation, Cumulative distribution functions, Delta plots, and Conditional Accuracy Functions
Kernel density estimation, Cumulative distribution functions, Delta plots, and Conditional Accuracy Functions

Required Python packages

I used the following Python packages; Pandas for data storing/manipulation, NumPy for some calculations, Seaborn for most of the plotting, and Matplotlib for some tweaking of the plots. Any script using these functions should import them: