scholarly journals Planned missing data designs and methods: options for strengthening inference, increasing research efficiency and improving animal welfare in ecological and evolutionary research

2021 ◽  
Author(s):  
Daniel W.A. Noble ◽  
Shinichi Nakagawa
Keyword(s):  
Missing Data ◽  
Animal Welfare ◽  
Planned Missing Data ◽  
Research Efficiency ◽  
Planned Missing

scholarly journals Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution

2018 ◽  
Author(s):  
Daniel W.A. Noble ◽  
Shinichi Nakagawa
Keyword(s):  
Missing Data ◽  
Animal Welfare ◽  
Experimental Designs ◽  
List Type ◽  
Data Imputation ◽  
Data Types ◽  
Planned Missing Data ◽  
Research Questions ◽  
Data Design ◽  
Planned Missing

AbstractEcological and evolutionary research questions are increasingly requiring the integration of research fields along with larger datasets to address fundamental local and global scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns.Planned missing data design (PMDD), where data are randomly and deliberately missed during data collection, is a simple and effective strategy to working under greater research constraints while ensuring experiments have sufficient power to address fundamental research questions. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and evaluating how data imputation procedures work under PMDD situations.Using realistic examples and simulations of multilevel data we show how a variety of research questions and data types, common in ecology and evolution, can be aided by using a PMDD with data imputation procedures. More specifically, we show how PMDD can improve statistical power in detecting effects of interest even with high levels (50%) of missing data and moderate sample sizes. We also provide examples of how PMDD can facilitate improved animal welfare and potentially alleviate research costs and constraints that would make endeavours for integrative research challenging.Planned missing data designs are still in their infancy and we discuss some of the difficulties in their implementation and provide tentative solutions. Nonetheless, data imputation procedures are becoming more sophisticated and more easily implemented and it is likely that PMDD will be an effective and powerful tool for a wide range of experimental designs, data types and problems in ecology and evolution.

Planned missing data designs in psychological research.

2006 ◽  
Vol 11 (4) ◽  
pp. 323-343 ◽  
Author(s):  
John W. Graham ◽  
Bonnie J. Taylor ◽  
Allison E. Olchowski ◽  
Patricio E. Cumsille
Keyword(s):  
Missing Data ◽  
Psychological Research ◽  
Planned Missing Data ◽  
Planned Missing

scholarly journals Reflection on modern methods: planned missing data designs for epidemiological research

2020 ◽  
Vol 49 (5) ◽  
pp. 1702-1711 ◽  
Author(s):  
Charlie Rioux ◽  
Antoine Lewin ◽  
Omolola A Odejimi ◽  
Todd D Little
Keyword(s):  
Missing Data ◽  
Gold Standard ◽  
Epidemiological Research ◽  
Missing Data Treatments ◽  
Planned Missing Data ◽  
Missing Completely At Random ◽  
Modern Methods ◽  
Missing Items ◽  
Data Design ◽  
Planned Missing

Abstract Taking advantage of the ability of modern missing data treatments in epidemiological research (e.g. multiple imputation) to recover power while avoiding bias in the presence of data that is missing completely at random, planned missing data designs allow researchers to deliberately incorporate missing data into a research design. A planned missing data design may be done by randomly assigning participants to have missing items in a questionnaire (multiform design) or missing occasions of measurement in a longitudinal study (wave-missing design), or by administering an expensive gold-standard measure to a random subset of participants while the whole sample is administered a cheaper measure (two-method design). Although not common in epidemiology, these designs have been recommended for decades by methodologists for their benefits—notably that data collection costs are minimized and participant burden is reduced, which can increase validity. This paper describes the multiform, wave-missing and two-method designs, including their benefits, their impact on bias and power, and other factors that must be taken into consideration when implementing them in an epidemiological study design.

Planned Missing Data Designs for Developmental Researchers

2013 ◽  
Vol 7 (4) ◽  
pp. 199-204 ◽  
Author(s):  
Todd D. Little ◽  
Mijke Rhemtulla
Keyword(s):  
Missing Data ◽  
Planned Missing Data ◽  
Planned Missing
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