Scientific Methods for Open Behavioral, Social and Cognitive Sciences

Editors:Sage Boettcher | Dejan Draschkow | Jona Sassenhagen | Martin Schultze Maintainers:Aylin Kallmayer | Leah Kumle | Leila Zacharias Citation:Boettcher, S.E.P., Draschkow, D., Sassenhagen, J., & Schultze, M. (Eds.). Scientific Methods for Open Behavioral, Social and Cognitive Sciences. https://doi.org/10.17605/OSF.IO/X4BCZ

The movement towards open and reproducible science prides itself on its grassroots and bottom-up development. However, even in these transformative times, most junior scientists will not discover the value and functional nature of transparent scientific practice before their graduate studies. This discovery is accompanied by transition costs, bad practices, and wasted time. Our project aims at placing open and reproducible scientific methods at the heart of scientific education - the first undergraduate years. To accomplish this we will establish and edit a teaching book covering not only the theoretical foundations of scientific methods for social and cognitive sciences, but also the practical skills necessary for transparent practice. It will offer standard text book knowledge accompanied by, but also presented via challenging and entertaining practical sessions in the programming environments R and Python – both of which are open source and freely available. The dynamic nature of a GitHub-based website will allow this resource to be both searchable with autonomous sections, while still including a narrative - students will be able to complete all the necessary steps for preparing, conducting and presenting scientific work. We aim at providing a standalone fully-comprehensible online book with the ability to be integrated in different teaching formats: e.g., assisting lectures, theoretical seminars, practical workshops, student-organized tutorials, or self-paced online learning. We hope this resource will change the skillsets and thinking of developing researchers early on and see such a project as essential to achieving transparent, open and reproducible scientific disciplines.

Chapters

• 1. Philosophical Foundations
  • 1.1. What is probability?
    • 1.1.1. A brief history of gambling and egos
    • 1.1.2. Objective probability
    • 1.1.3. Subjective probability
    • 1.1.4. Conclusion chapter
    • 1.1.5. References
• 2. Computer stuff
  • 2.1. Computers
  • 2.2. Setting up a scientific research environment
  • 2.3. Version Control, Git and Github
  • 2.4. Docker
  • 2.5. Introduction to Programming with Python
    • 2.5.1. What is Python?
    • 2.5.2. Basics
    • 2.5.3. Help and Descriptions
    • 2.5.4. Variables
    • 2.5.5. Data Types
    • 2.5.6. Operators and Comparisons
    • 2.5.7. Control Flow
    • 2.5.8. Functions
    • 2.5.9. Classes
    • 2.5.10. Modules <— BIS HIER
    • 2.5.11. Exceptions
    • 2.5.12. File I/O
• 3. Conducting Experiments
  • 3.1. Variables in Psychology
  • 3.2. Contrafactual Causality
  • 3.3. Experimental Designs
    • 3.3.1. e.g. 2*2 Design gambling task
  • 3.4. Programming Experiments in Python with Psychopy
    • 3.4.1. Story Time: Galton’s Pendulum
    • 3.4.2. Programming Experiments in Python with Psychopy
    • 3.4.3. Alternative software
    • 3.4.4. References
• 4. Analysing Experimental Data
  • 4.1. Introduction to Programming with R
    • 4.1.1. Why R?
    • 4.1.2. Gathering your tools
    • 4.1.3. Some first, wobbly steps
    • 4.1.4. Handling data
    • 4.1.5. Getting data into and out of R
    • 4.1.6. Closing words
    • 4.1.7. References
  • 4.2. Basic Visualisation
  • 4.3. Descriptive Statistics
  • 4.4. The Generalized Linear Model
    • 4.4.1. t-test
    • 4.4.2. correlation test
  • 4.5. Multivariate data
• 5. Reporting the Results of Scientific Experiments
  • 5.1. Pandoc intro - simple markup forms
  • 5.2. Loop of submission, review, and revision
• 6. Advanced Methods
  • 6.1. Data wrangling
    • 6.1.1. Introduction to the tidyverse
  • 6.2. Linear and generalized linear mixed-models
    • 6.2.1. Introduction to mixed-effects modeling
    • 6.2.2. Fixed and random effects
    • 6.2.3. Linear vs. generalized linear
    • 6.2.4. Contrast coding
    • 6.2.5. Model comparisons
    • 6.2.6. Determining the random effects structure
    • 6.2.7. Facing a real data set
    • 6.2.8. Power analysis for mixed models
  • 6.3. Multivariate Analysis and Machine Learning
    • 6.3.1. Introduction to scikit-learn
  • 6.4. Special methods
  • 6.5. Neuroscience
  • 6.6. Eye tracking
  • 6.7. Online Experiments
    • 6.7.1. Mechanical Turk
  • 6.8. Formatting - saving data (ext.)
  • 6.9. Data protection - security (ext.)
• 7. help
• 8. How to contribute

Indices and tables

• Index
• Module Index
• Search Page