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Subtypes of the missing not at random missing data mechanism
1Department of Psychology, University of Notre Dame.
Psychological Methods
|June 28, 2021
Summary
Missing not at random (MNAR) data have two subtypes that distort relationships differently. Understanding these subtypes is crucial for accurate statistical analysis and generalizable research findings.
Area of Science:
- Statistics
- Data Science
- Psychometrics
Background:
- Missing data pose challenges in statistical analysis.
- Missing not at random (MNAR) data arise from various underlying processes.
- Existing literature often overlooks the distinct subtypes of MNAR mechanisms.
Purpose of the Study:
- To systematically introduce and name two fundamental subtypes of MNAR data.
- To highlight the distinct ways these subtypes distort data relationships.
- To provide tools for identifying and simulating MNAR subtypes in research.
Main Methods:
- Conceptual definition and naming of two MNAR subtypes.
- Case study to demonstrate mechanical distinctness from other missing data mechanisms.
- Development of methods for generating MNAR subtypes in simulation studies.
Main Results:
- MNAR subtypes exhibit characteristic differences in distorting data relationships.
- Simulation studies reveal differential impacts of MNAR subtypes on statistical inference.
- Regression and growth curve modeling contexts show varying severity of distortion.
Conclusions:
- The two MNAR subtypes have significant, distinct implications for statistical validity and generalizability.
- Researchers can use provided methods to identify and simulate these subtypes.
- This work provides a foundation for organized consideration of MNAR subtypes in research.
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