Handling Missing Data and Outliers in Dissertations, Essays and Assignments: Strategies and Examples

January 23, 2026 Chris

Missing data and outliers are common problems in research projects. Left unaddressed, they can bias results, reduce statistical power, and weaken the credibility of your dissertation, essay or assignment. This guide explains why these issues matter, practical strategies for detection and treatment, examples you can adapt, and how to report your choices clearly in academic writing.

Why missing data and outliers matter

Missing data reduces sample size, can bias parameter estimates if the missingness is systematic, and complicates hypothesis testing.
Outliers can distort means, inflate variances, and lead to misleading model fits (e.g., regression coefficients heavily influenced by a few extreme values).
In dissertations and assignments you must show transparent, reproducible choices and perform sensitivity checks so examiners can judge the robustness of your conclusions.

Types of missingness (brief)

Understanding the mechanism guides the method:

MCAR (Missing Completely at Random): probability of missingness is unrelated to observed or unobserved data. Deletion methods may be unbiased.
MAR (Missing at Random): missingness depends only on observed data. Imputation or model-based methods usually appropriate.
MNAR (Missing Not at Random): missingness depends on unobserved data; requires special modelling or sensitivity analysis.

Detecting missingness and outliers

Detect missingness

Summarise by variable: percent missing, patterns (matrix or heatmap).
Cross-tabulate missingness with key covariates to detect MAR patterns.

Detect outliers

Univariate methods:
- IQR rule: values < Q1 − 1.5×IQR or > Q3 + 1.5×IQR.
- Z-score: |z| > 3 (or stricter depending on sample size).
Multivariate methods:
- Mahalanobis distance for multivariate outliers.
- Robust methods: Minimum Covariance Determinant (MCD).
Visual checks:
- Boxplots, violin plots, scatterplots with smoothing, and residual plots for models.
See best practices for communicating charts in Data Visualization Best Practices for Dissertations, Essays and Assignments: Charts, Tables and Figures That Communicate.

Strategies for handling missing data

1. Deletion methods

Listwise deletion: drop any case with missing values.
- Pros: easy, default in many packages.
- Cons: loss of power; biased unless data are MCAR.
Pairwise deletion: use all available pairs for correlations.
- Use cautiously; can lead to inconsistent sample sizes across analyses.

2. Single imputation

Mean/median imputation: replace missing with mean or median.
- Quick but underestimates variance and can bias associations.
Regression imputation: predict missing values from other variables.
- Better but still underestimates uncertainty.

3. Multiple Imputation (MI) — recommended for many dissertation datasets

Generates multiple complete datasets, analyses each, then pools estimates.
Accounts for imputation uncertainty and works well under MAR.
Implementations: R packages (mice, Amelia), Python (statsmodels + custom approaches, scikit-learn IterativeImputer for chained equations).

4. Model-based approaches

Maximum Likelihood (ML): directly estimate model parameters from incomplete data using full-information ML.
Useful in structural equation modelling or mixed models.

5. Weighting and sensitivity analysis

Use inverse-probability weighting if missingness is associated with observed covariates.
Always perform sensitivity analyses for MAR vs MNAR assumptions.

Strategies for handling outliers

Options

Investigate first: check data entry and measurement error—correct if possible.
Transform: log, square-root or Box–Cox to reduce skew and influence.
Winsorize: cap extreme values at a percentile (e.g., 1st/99th).
Robust statistics: use median, trimmed means, robust regression (e.g., Huber, MM-estimators).
Remove (with justification): only when outliers are clearly erroneous or non-representative.
Model explicitly: consider mixture models if outliers are meaningful subgroups.

Practical example (small dataset)

Dataset (variable X): 2, 3, 4, NA, 5, 100, 6, NA, 7

Detect:
- Missing values at positions 4 and 8 (22% missing).
- 100 is an extreme value (compare with IQR or z-score).
Naive single imputation:
- Mean of observed (excluding 100): (2+3+4+5+100+6+7)/7 = 18.14 → replacing NA with 18.14 inflates mean.
Better approach:
- Use median imputation for numeric skew: median of observed = 5 → replace NAs with 5.
- For the outlier 100, check source; if valid and representative, consider robust methods (median-based) or transform X (log).
Preferred: Multiple Imputation + Robust Regression
- Impute missing values using other covariates (chained equations), run robust regression to reduce influence of 100, and run sensitivity checks with and without 100.

Comparison table: common methods

Method	Best for	Pros	Cons
Listwise deletion	MCAR, small missingness	Simple; easy to report	Loss of data; biased unless MCAR
Mean/median imputation	Quick prelim analyses	Easy	Underestimates variance; biases associations
Multiple Imputation (MI)	MAR, moderate missingness	Preserves variability; principled	More complex; requires proper implementation
Maximum Likelihood (ML)	Model-based analyses	Efficient; uses all data	Requires correct model specification
Transform / Robust methods (outliers)	Extreme skew/outliers	Reduces influence of extremes	Interpretation changes; transformations must be reported
Winsorizing	Non-error extreme values	Simple control of extremes	Arbitrary cut-offs; can mask issues

Recommended workflow for dissertations and assignments

Explore and report missingness/outliers with tables and plots.
Decide method based on mechanism and analysis aim (justify choice and cite sources).
Implement method using reproducible scripts (R/Python). See Reproducible Analysis Workflows for Dissertations, Essays and Assignments Using R and Python.
Run sensitivity analyses (e.g., compare MI vs listwise; robust vs OLS).
Report transparently (how many values missing, imputation model, diagnostics, how outliers handled).
Interpret results considering the treatment. For help writing results, see Interpreting Statistical Output for Dissertations, Essays and Assignments: Writing Clear Results.

Tools and packages (quick list)

R: mice, missForest, Amelia, VIM, robustbase.
Python: scikit-learn (SimpleImputer, IterativeImputer), statsmodels, fancyimpute (for advanced imputation).
For regression/ANOVA advice see Regression, ANOVA and Beyond: Applied Statistics for Dissertations, Essays and Assignments.
For selecting tests and integrating methods see Selecting the Right Statistical Tests for Dissertations, Essays and Assignments: A Practical Decision Tree and Mixed-Methods Data Integration: Techniques for Dissertations, Essays and Assignments.

How to report in your dissertation (example sentences)

“Missingness was examined for all variables; X had 12% missing values and patterns indicated MAR conditional on age and education. Multiple imputation (m = 20) using chained equations was applied; pooled estimates are reported.”
“One extreme value (score = 100) was investigated and retained; analyses used robust regression to reduce undue influence. Results were consistent in sensitivity analyses excluding the extreme value (see Appendix).”

For guidance on planning sample size and power (which helps with missingness planning), consult Power Analysis and Sample Size Planning for Dissertation and Assignment Studies.

Final tips

Always visualise before deciding.
Use multiple imputation or model-based approaches when assumptions allow.
Prefer robust statistical methods when outliers are meaningful rather than errors.
Document every step in your methods and append code so examiners can reproduce your work — see Reproducible Analysis Workflows for Dissertations, Essays and Assignments Using R and Python.

Need help?

If you need writing, data analysis, or proofreading assistance for your dissertation, essay or assignment, contact MzansiWriters:

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Email: info@mzansiwriters.co.za, or
Use the Contact Us page accessed via the main menu.

Handling missing data and outliers carefully will strengthen your methodology, improve credibility, and leave examiners confident in your conclusions.

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