Missing Data and Outlier Handling: Imputation and Robust Treatment

What this service covers

• Summaries of missingness by variable and respondent
• Tests and plots of missing-data patterns (MCAR, MAR, MNAR)
• Listwise or pairwise deletion when appropriate
• Mean, median, regression and multiple imputation
• Univariate and multivariate outlier detection
• Winsorising, transformation, robust methods or exclusion with justification
• Sensitivity analysis comparing results with and without treatment

Understand the missingness first

Data can be missing completely at random, at random given other variables, or not at random, and the right treatment depends on which applies. We examine which variables and groups have gaps, whether missingness is linked to other answers and how much is missing overall. Small amounts of random missingness can be harmless; large or systematic gaps cannot be ignored.

Choosing a method

Simple deletion is easy but can waste data and bias results. Mean imputation shrinks variance and is rarely recommended. Multiple imputation is generally preferred for analyses such as regression, because it reflects the uncertainty of the filled values. We document the model and number of imputations used.

Outliers: investigate, then decide

An outlier may be an entry error, a different population or a real extreme. We check the source first, then use rules such as interquartile range or Mahalanobis distance to flag cases, and decide whether to correct, transform, winsorise or keep them. Where the choice matters, we run the analysis both ways and report both.

Related services

This step follows data cleaning and validation and precedes descriptive and inferential statistics. Also see coding and recoding.

How the work runs

1. You send the dataset and describe the planned analyses.
2. We diagnose missing data and outliers.
3. We apply and document the chosen treatment.
4. You receive treated data and a sensitivity comparison.

What you receive

• A missing data report with patterns and percentages
• Imputed or treated dataset
• Outlier list with decisions and reasons
• Sensitivity analysis showing impact on results
• Methods text describing the treatment