Missing Data: Deletion & Simple Imputation
Missing Data: Deletion & Simple Imputation groups the core ideas a learner needs at the 4 · data cleaning & missing data stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Complete-case deletionComplete-case deletion is a practical concept within Missing Data: Deletion & Simple Imputation. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02Pairwise deletionPairwise deletion is a practical concept within Missing Data: Deletion & Simple Imputation. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Constant-value imputationConstant-value imputation is a practical concept within Missing Data: Deletion & Simple Imputation. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
04Mean imputationMean imputation is a practical concept within Missing Data: Deletion & Simple Imputation. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Median imputationMedian imputation is a practical concept within Missing Data: Deletion & Simple Imputation. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06Mode / most-frequent imputationMode / most-frequent imputation is a practical concept within Missing Data: Deletion & Simple Imputation. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
07Group-wise imputationGroup-wise imputation is a practical concept within Missing Data: Deletion & Simple Imputation. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.