Preregistration is the process of specifying project details, such as hypotheses, data …
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 …
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.
No restrictions on your remixing, redistributing, or making derivative works. Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make derivative works.
Most restrictive license type. Prohibits most uses, sharing, and any changes.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based educators, or other custom arrangements. Go to the resource provider to see their individual restrictions.
