Open AccessA fully automated, transparent, reproducible, and blind protocol for sequential analyses
Author(s) -
Brice Beffara Bret,
Amélie Beffara Bret,
Ladislas Nalborczyk
Publication year - 2021
Publication title -
meta-psychology
Language(s) - English
Resource type - Journals
ISSN - 2003-2714
DOI - 10.15626/mp.2018.869
Subject(s) - intrapersonal communication , computer science , protocol (science) , automation , software , data science , obstacle , data collection , interpersonal communication , software engineering , machine learning , data mining , artificial intelligence , programming language , psychology , statistics , medicine , social psychology , mechanical engineering , alternative medicine , mathematics , pathology , law , political science , engineering
Despite many cultural, methodological, and technical improvements, one of the major obstacle to results reproducibility remains the pervasive low statistical power. In response to this problem, a lot of attention has recently been drawn to sequential analyses. This type of procedure has been shown to be more efficient (to require less observations and therefore less resources) than classical fixed-N procedures. However, these procedures are submitted to both intrapersonal and interpersonal biases during data collection and data analysis. In this tutorial, we explain how automation can be used to prevent these biases. We show how to synchronise open and free experiment software programs with the Open Science Framework and how to automate sequential data analyses in R. This tutorial is intended to researchers with beginner experience with R but no previous experience with sequential analyses is required.