Survey data cleaning
Variable review, missingness checks, duplicates, range and consistency checks, coding review and documented cleaning decisions.
S/07 Research data consulting · International studies
Research data consulting from questionnaire to defensible dataset.
Suhanur Rahman supports research teams that need the technical layer between study design and analysis: collection instruments, field workflows, data-quality controls, cleaning and structured handover for quantitative analysis.
0120+
field research projects
0204
quality checkpoints
03STATA
analysis-oriented handover
0401
connected workflow
Analyst-ready
A dataset with documented cleaning decisions.
01 — The problem
Where the complexity usually starts.
01
Your final dataset is clean-looking but you cannot explain how the cleaning decisions were made.
02
Duplicates, missingness and inconsistent coding are discovered too late.
03
Field collection and analysis teams use different definitions for the same variable.
04
You need an analyst-ready dataset with documented quality controls.
02 — Catch it early
Good data work is mostly about catching problems early.
A clean spreadsheet at the end of a study can hide weeks of avoidable problems. Weak validation, inconsistent coding, duplicate records, poor enumerator guidance or ambiguous questions all become expensive when discovered after fieldwork.
The consulting approach is therefore layered: improve the instrument, monitor collection quality, inspect incoming data, document changes and hand over a dataset whose structure is understandable to the analyst who will use it.
03 — Services
A focused data-quality layer for your study.
Quality assurance
Checks designed around the study’s actual risks rather than generic data-cleaning recipes.
STATA preparation
Structured, analysis-ready datasets with clear variable names, labels, codes and reproducible preparation steps.
Research infrastructure
Data collection systems, documentation, cloud tooling and workflows that connect field operations to analysis.
04 — A useful fit
Built for international research programs.
The strongest engagements are usually studies with distributed field teams, complex questionnaires, multiple rounds of collection or a high cost of bad data.
NGOs, development organizations, academic research teams and research agencies can use the service as a focused data-quality layer or as part of a broader research-technology engagement.
05 — Start here
Discuss the study Need a research data specialist?
Share the questionnaire, a sample export and the analysis outcome you need. The quality risks become much easier to identify from real study material.
06 — Related workAll services