KoboToolbox & XLSForm
Complex skip logic, relevance and validation, repeat groups, calculated fields, constraints, roster workflows and field-ready form design.
S/03 Research technology · Data systems · Bangladesh
Research data systems designed around the field, not just the questionnaire.
Suhanur Rahman helps research teams turn questionnaires into dependable data workflows: instrument design, KoboToolbox and XLSForm development, field deployment, quality checks, cleaning, analysis support and the technical infrastructure around the study.
0120+
field research projects
0204
workflow stages covered
033+
collection / analysis tools
0401
end-to-end technical owner
Clean data
A dataset another analyst can defend.
01 — The problem
Where the complexity usually starts.
01
The questionnaire is ready but the field team keeps finding edge cases.
02
Your data arrives with missing, duplicated or inconsistent records.
03
The form developer, field coordinator and analyst are working from different assumptions.
04
You need a dataset that another analyst can understand and defend.
02 — Just build the form?
A technically valid survey can still fail in the field.
Research data collection is an operational system. Skip logic has to reflect the instrument, enumerators need clear paths through difficult cases, connectivity constraints have to be anticipated, and the resulting dataset needs enough structure that quality problems can be found before they become findings.
The work here covers more than form syntax. It connects questionnaire logic to field operations, validation rules, coding, documentation and the downstream analysis team.
The result is a workflow in which the dataset is treated as an evidence product, not a pile of rows exported after the fieldwork ends.
03 — Core capabilities
A complete research-data workflow.
Field deployment
Enumerator preparation, pilot testing, troubleshooting, documentation and practical decisions for teams collecting data under real-world constraints.
Data quality
Validation, audit checks, outlier review, missingness analysis, consistency checks and cleaning before analysis or reporting.
Analysis support
STATA-oriented workflows, clean variable structures and reproducible preparation for quantitative analysis, tables, dashboards or reporting.
04 — Experience that shapes the workflow
More than 20 field-research projects, with technical and operational work connected.
Across more than 20 field-research projects, the recurring lesson has been that data quality is decided long before a statistic is calculated. A question that is ambiguous in the field becomes a coding headache later; a missing validation rule becomes a cleaning problem; poor documentation makes a finding difficult to audit.
That is why the preferred way of working is end to end: understand the study design, build the collection instrument, prepare the field process, monitor quality and hand over data that another analyst can understand.
This is particularly relevant for NGOs, research agencies, universities, development programs and international organizations running surveys in Bangladesh or across South Asia.
05 — Start here
Start the conversation Need a KoboToolbox developer or research data consultant?
Share the questionnaire, sampling plan and collection context. That is usually enough to identify whether the need is form engineering, field support, data-quality work or a broader research-technology engagement.
06 — Related workAll services