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Secondary Data Preregistration
Unrestricted Use
Public Domain
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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.

Subject:
Life Science
Social Science
Material Type:
Reading
Author:
Alexander C. DeHaven
Andrew Hall
Brian Brown
Charles R. Ebersole
Courtney K. Soderberg
David Thomas Mellor
Elliott Kruse
Jerome Olsen
Jessica Kosie
K.D. Valentine
Lorne Campbell
Marjan Bakker
Olmo van den Akker
Pamela Davis-Kean
Rodica I. Damian
Stuart J Ritchie
Thuy-vy Nguyen
William J. Chopik
Sara J. Weston
Date Added:
08/03/2021
Secondary Data Preregistration
Unrestricted Use
Public Domain
Rating
0.0 stars

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.

Subject:
Applied Science
Material Type:
Reading
Author:
Alexander C. DeHaven
Andrew Hall
Brian Brown
Charles R. Ebersole
Courtney K. Soderberg
David Thomas Mellor
Elliott Kruse
Jerome Olsen
Jessica Kosie
K. D. Valentine
Lorne Campbell
Marjan Bakker
Olmo van den Akker
Pamela Davis-Kean
Rodica I. Damian
Stuart J. Ritchie
Thuy-vy Ngugen
William J. Chopik
Sara J. Weston
Date Added:
08/12/2021