Regression Analysis
Linear, logistic, multinomial, ordinal, hierarchical and multilevel models, with diagnostics that are actually run.
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Data & Statistics
Data Cleaning & Preparation
Missing values, outliers, recoding, merging and reshaping — the unglamorous work that decides whether the analysis is trustworthy.
Most analysis problems are data problems. Missing values deleted silently, outliers removed without justification, or a merge that quietly duplicated rows will invalidate everything downstream.
We document every decision: what was missing and how it was handled, which outliers were retained or excluded and why, and how the final analytic dataset was constructed.
What you receive
- Missing data analysis with a stated mechanism and handling strategy
- Outlier identification with justified decisions
- Recoding, variable construction and reverse-scoring
- Merge and reshape with row-count reconciliation
- Data preparation log for your methodology chapter
Questions about Data Cleaning & Preparation
The pattern matters more than the percentage. Data missing completely at random can often be handled well even at higher rates; a small amount of systematically missing data can bias everything. We test the mechanism before choosing an approach.
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Next step
Ready to talk about Data Cleaning & Preparation?
Send the brief, the deadline and anything you already have. You will get a reference number straight away and a considered reply, usually within one working day.
- Fixed quote agreed before any work begins
- A reference number you can quote on WhatsApp
- Confidential handling, and an NDA if you want one
- An honest answer if your deadline is not realistic
