This video will introduce how to calculate statistical power in R using the pwr package.
All materials shown in the video, as well as content from our other videos, can be found here: https://osf.io/7gqsi/.
Topics covered:
Understanding reproducible research
Setting up a reproducible project
Understanding power
Preregistering your study
Keeping track of things
Containing bias
Sharing your work
Workshop goals
- Why are we teaching this
- Why is this important
- For future and current you
- For research as a whole
- Lack of reproducibility in research is a real problem
Materials and how we'll use them
- Workshop landing page, with
- links to the Materials
- schedule
Structure oriented along the Four Facets of Reproducibility:
- Documentation
- Organization
- Automation
- Dissemination
Will be available after the Workshop
How this workshop is run
- This is a Carpentries Workshop
- that means friendly learning environment
- Code of Conduct
- active learning
- work with the people next to you
- ask for help
Workshop goals
- Why are we teaching this
- Why is this important
- For future and current you
- For research as a whole
- Lack of reproducibility in research is a real problem
Materials and how we'll use them
- Workshop landing page, with
- links to the Materials
- schedule
Structure oriented along the Four Facets of Reproducibility:
- Documentation
- Organization
- Automation
- Dissemination
Will be available after the Workshop
How this workshop is run
- This is a Carpentries Workshop
- that means friendly learning environment
- Code of Conduct
- active learning
- work with the people next to you
- ask for help
Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.
For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).
This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.
Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.
For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).
This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.
Central location housing curriculum materials and planning tools for trainers of the COS Introduction to Open and Reproducible Research workshop.