The Impact of Missing Data on the Ethical Quality of a Research Study

The purpose of this chapter is to explore the impact of missing data on the ethical quality of a research study. In doing so, we borrow heavily from the work of Rosenthal (1994) and Rosenthal and Rosnow (1984). The overarching principle of Rosenthal's (1994) work is that ethics is closely linke...

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Published inHandbook of Ethics in Quantitative Methodology pp. 357 - 381
Main Authors Enders, Craig K., Gottschall, Amanda C.
Format Book Chapter
LanguageEnglish
Published United Kingdom Routledge 2011
Taylor & Francis Group
Edition1
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Abstract The purpose of this chapter is to explore the impact of missing data on the ethical quality of a research study. In doing so, we borrow heavily from the work of Rosenthal (1994) and Rosenthal and Rosnow (1984). The overarching principle of Rosenthal's (1994) work is that ethics is closely linked with the quality of a research study, such that high-quality studies are more ethically defensible than low-quality studies. Missing data pose an obvious threat to quality at the analysis stage of a study (e.g., when a researcher uses a missing data handling technique that is prone to bias), but ethical issues arise throughout the entire research process. Accordingly, we explore the linkage between quality and ethics at the design and data collection phase, the analysis phase, and the reporting phase. In doing so, we also apply Rosenthal and Rosnow's (1984) cost-utility model to certain missing data issues (see also Rosnow & Rosenthal, Chapter 3, this volume). In this framework, the costs associated with a study (e.g., potential harm to participants, time, money, resources) are weighed against its utility (e.g., potential benefits to participants, science, or society). As it relates to ethics, a study is more defensible when its benefits exceed its costs.
AbstractList The purpose of this chapter is to explore the impact of missing data on the ethical quality of a research study. In doing so, we borrow heavily from the work of Rosenthal (1994) and Rosenthal and Rosnow (1984). The overarching principle of Rosenthal's (1994) work is that ethics is closely linked with the quality of a research study, such that high-quality studies are more ethically defensible than low-quality studies. Missing data pose an obvious threat to quality at the analysis stage of a study (e.g., when a researcher uses a missing data handling technique that is prone to bias), but ethical issues arise throughout the entire research process. Accordingly, we explore the linkage between quality and ethics at the design and data collection phase, the analysis phase, and the reporting phase. In doing so, we also apply Rosenthal and Rosnow's (1984) cost-utility model to certain missing data issues (see also Rosnow & Rosenthal, Chapter 3, this volume). In this framework, the costs associated with a study (e.g., potential harm to participants, time, money, resources) are weighed against its utility (e.g., potential benefits to participants, science, or society). As it relates to ethics, a study is more defensible when its benefits exceed its costs.
Author Enders, Craig K.
Gottschall, Amanda C.
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Keywords Cost Utility Model
Missing Data Mechanism
MCAR Mechanism
Multiple Imputation
Missing Data Analysis
Mar Mechanism
Missing Data
Va Ri
Auxiliary Variables
Missing Data Handling
MNAR Mechanism
Incomplete Variables
IRT Model
MAR Assumption
MNAR Analysis
MNAR Model
Ethical Continuum
Imputation Phase
Automated Essay Scoring
Missing Data Rate
Jar
Ethical Issues Related
Missing Data Issues
Pattern Mixture Model
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Title The Impact of Missing Data on the Ethical Quality of a Research Study
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