pyWitness 1.0: A python eyewitness identification analysis toolkit.

Autor: Mickes L; School of Psychological Science, University of Bristol, Bristol, UK. laura.mickes@bristol.ac.uk., Seale-Carlisle TM; School of Psychology, King's College, University of Aberdeen, Aberdeen, UK., Chen X; School of Psychological Science, University of Bristol, Bristol, UK., Boogert S; Department of Physics and Astronomy, University of Manchester, Manchester, UK.
Jazyk: angličtina
Zdroj: Behavior research methods [Behav Res Methods] 2024 Mar; Vol. 56 (3), pp. 1533-1550. Date of Electronic Publication: 2023 Jul 19.
DOI: 10.3758/s13428-023-02108-2
Abstrakt: pyWitness is a python toolkit for recognition memory experiments, with a focus on eyewitness identification (ID) data analysis and model fitting. The current practice is for researchers to use different statistical packages to analyze a single dataset. pyWitness streamlines the process. In addition to conducting key data analyses (e.g., receiver operating characteristic analysis, confidence accuracy characteristic analysis), statistical comparisons, signal-detection-based model fits, simulated data generation, and power analyses are also possible. We describe the package implementation and provide detailed instructions and tutorials with datasets so that users can follow. There is also an online manual that is regularly updated. We developed pyWitness to be user-friendly, reduce human interaction with pre-processing and processing of data and model fits, and produce publication-ready plots. All pyWitness features align with open science practices, such that the algorithms, fits, and methods are reproducible and documented. While pyWitness is a python toolkit, it can also be used from R for users more accustomed to this environment.
(© 2023. The Author(s).)
Databáze: MEDLINE
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