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Missing Data and Multiple Imputation Decision Tree
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This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full information maximum likelihood (FIML). Streamlining procedures to address missing data and increasing the transparency of those procedures through consensus on reporting standards is inexorably linked to the goals of open scholarship (i.e., the endeavour to improve openness, integrity, social justice, diversity, equity, inclusivity and accessibility in all areas of scholarly activities, and by extension, academic fields beyond the sciences and academic activities; Pownall et al., 2021). Successfully implementing transparent and accessible guidelines for addressing missing data is also important for Diversity, Equity, Inclusion, and Accessibility (DEIA) improvement efforts (Randall et al., 2021). Structural barriers to participation in research can lead to participants from minoritized groups disproportionately dropping out of longitudinal, developmental studies or not completing measures (Randall et al., 2021). This selection effect can bias model estimates and confidence intervals, leading to unsubstantiated claims about equitable outcomes. In addition to often creating artificially small estimates of inequalities between groups, listwise deletion also limits statistical power for minoritized groups who are already underrepresented in many datasets.

Subject:
Mathematics
Statistics and Probability
Material Type:
Diagram/Illustration
Reading
Author:
Alex Uzdavines
Ben Van Dusen
Daria Gerasimova
David Moreau
Denver Brown
James M. Clay
Jayson Nissen
Jessica A. R. Logan
Kathleen Schmidt
Keven Joyal-Desmarais
Kevin M. King
Mahmoud M. Elsherif
Martin Vasilev
Max A. Halvorson
Menglin Xu
Pamela E. Davis-Kean
Rick A. Cruz
Sierra Bainter
Adrienne D. Woods
Date Added:
04/25/2022
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