ǂan ǂopen source R package for Bayesian statistics in psychology
Jure Demšar (Author), Grega Repovš (Author), Erik Štrumbelj (Author)

Abstract

Research in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and students that have the technical and statistical fundamentals that are required for probabilistic programming. Such knowledge is not part of the typical psychology curriculum and is a difficult obstacle for psychology students and researchers to overcome. The goal of the bayes4psy package is to bridge this gap and offer a collection of models and methods to be used for analysing data that arises from psychological experiments and as a teaching tool for Bayesian statistics in psychology. The package contains the Bayesian t-test and bootstrapping along with models for analysing reaction times, success rates, and tasks utilizing colors as a response. It also provides the diagnostic, analytic and visualization tools for the modern Bayesian data analysis workflow.

Keywords

psihologija;statistika;statistične analize;Bayesova analiza;obdelava podatkov;računalniški programi;R;psychology;statistics;statistical analysis;Bayesian analysis;data analysis;computer software;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FRI - Faculty of Computer and Information Science
UDC: 159.9.075:[311:004.4R]
COBISS: 14800387 Link will open in a new window
ISSN: 1664-1078
Views: 441
Downloads: 144
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary keywords: Bayesova statistika;R;psihologija;reakcijski čas;uspešnost;Bayesov t-test;analiza barv;linearni model;
Type (COBISS): Article
Pages: str. 1-20
Volume: ǂVol. ǂ11
Issue: ǂart. ǂ947
Chronology: May 2020
DOI: 10.3389/fpsyg.2020.00947
ID: 12907173
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