Missing data and attrition can mess up your study results big time. They're like sneaky gremlins that can make your findings less trustworthy. But don't worry, there are ways to deal with them.
You've got options like deleting incomplete cases or filling in the blanks with educated guesses. The key is picking the right method for your situation. It's all about keeping your results solid and your conclusions strong.
Missing Data Patterns and Reasons
Types and Patterns of Missing Data
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Top images from around the web for Types and Patterns of Missing Data
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Advanced methods for missing values imputation based on similarity learning [PeerJ] View original
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Missing data encompasses incomplete information in datasets, while attrition involves participant loss over time in longitudinal studies
Common missing data patterns include:
(MCAR): No systematic relationship between missingness and any values
(MAR): Missingness related to observed variables but not unobserved ones
(MNAR): Missingness related to unobserved variables
Visualization techniques help identify missing data patterns:
Heat maps display missingness across variables and observations
Missingness patterns plots show combinations of missing variables
Causes and Implications of Missing Data
Reasons for missing data stem from various sources: