Data Processing & Delivery
Transform raw survey output into clean, structured, and well-documented datasets ready for analysis.
We take raw survey output from multiple sources and convert it into cleaned, harmonized, and clearly structured datasets. Our goal is to remove noise and ambiguity so that analysts and stakeholders can focus on interpretation, not troubleshooting files.
From basic cleaning and coding to derived variables and simple tab views, we align our deliverables with your internal workflows and tools.
We can work with single-country or multi-country data, combining files where needed and ensuring consistent labels, scales, and codes across markets.
Well-structured, well-documented data shortens the path between fieldwork completion and decision-making. It also reduces rework in future waves or related studies, because definitions and transformations are captured clearly.
Datasets Processed
Markets Harmonized
Repeat Engagement
Typical Turnaround Post‑Field
We start by agreeing on target formats, variable naming conventions, and any derived metrics you need. This upfront clarity makes later waves of the study easier to compare and extend.
Throughout processing we keep a log of changes – from removed records to new variables – and share this alongside your final files, so every decision is traceable.
We commonly deliver in SPSS, Excel, and CSV, but can support other formats on request as long as specifications are clear.
Yes. We provide counts and, where appropriate, IDs or flags for removed or down‑weighted records, along with the rules used.
We can design variable naming and coding conventions with future waves in mind, making it easier to append data and run trends.
On request, we can supply simple tabulations or diagnostic views to help you quickly review fieldwork outcomes and data quality.
We clean and structure survey data, apply exclusion rules, and prepare files in formats that align with your analysis workflows.
Our team documents all quality actions and transformations, so you know what changed between raw and final data and why.