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A method that omits entire records with any missing values from the analysis.
DESCRIPTION
Listwise deletion removes observations containing missing values, simplifying analysis but potentially reducing sample size and introducing bias. It is valid under Missing Completely At Random (MCAR) but problematic under MAR or MNAR. Analysts often prefer multiple imputation or model-based approaches to preserve information. Careful diagnostics of missingness patterns guide the choice of method. Documenting exclusion criteria is essential for reproducibility and transparency.
ALTERNATIVE DEFINITIONS
Wikipedia
Listwise deletion, sometimes called complete case analysis, is a method of dealing with missing data, in which any case with missing values is omitted from the analysis.
Clarifies equivalence to complete case analysis and omission of cases.
UCLA Statistical Consulting
Listwise deletion deletes an entire record if any single value is missing for that record.
Emphasizes record-level deletion and practical implications.
Penn State STAT 501
With listwise deletion (complete case analysis), only cases with no missing data on any of the variables are included.
Highlights inclusion criteria for complete case analysis in practice.
RELATED TERMS
Missing Data MCAR MAR MNAR Complete Case Analysis Systematic Error Statistical Power Scrubbing
COMPETING TERMS
Multiple Imputation Pairwise Deletion
KNOWLEDGE AREAS
Data Science Testing & Validation Data Quality
TARGET AUDIENCE
Data Scientist Data Analyst BI Specialist Information Analyst Data Steward
EXTERNAL SOURCES
- Wikipedia - Listwise deletion
- CRAN - Multiple imputation package manual
- UCLA Statistical Consulting - Guide to missing data methods
- National Institutes of Health - Paper on missing data analysis
TOP QUESTIONS
When is Listwise Deletion appropriate?
Under MCAR assumptions and when missingness is minimal; otherwise, it can bias results and reduce power.
Category: Requirements
What is the main risk of Listwise Deletion?
Loss of data and potential bias if missingness relates to the outcome or predictors. Estimates may become unrepresentative.
Category: Risks
How does Listwise Deletion compare to Multiple Imputation?
Imputation preserves sample size and can reduce bias under MAR; listwise deletion is simpler but often inferior.
Category: Distinction
What is a common mistake with Listwise Deletion?
Applying it without checking missingness mechanisms or data patterns, leading to unintended exclusions.
Category: Mistakes
How do you diagnose missingness before using Listwise Deletion?
Profile missing, test MCAR (e.g., Little’s test), and visualize patterns across variables.
Category: HowTo
Where does Listwise Deletion show the biggest impact?
In small samples or high-dimensional data, where losing records drastically reduces statistical power.
Category: Example
Is Listwise Deletion acceptable in regulatory contexts?
Only with documented justification and sensitivity analyses; many guidelines prefer imputation or model-based approaches.
Category: Opinion
Can Listwise Deletion be combined with weighting?
Weighting doesn’t fix bias from nonrandom missingness; consider imputation or modeling instead.
Category: Misunderstanding
What is a hot topic around missing data methods?
Flexible Bayesian and machine learning imputation techniques outperform rigid deletion strategies in complex datasets.
Category: Topic