Turning field submissions into reliable, well-managed, analysis-ready data and meaningful evidence for decision-making.
At PrimeMetrics, we understand that the quality of research findings is closely linked to the quality of the data behind them. Our data management approach therefore goes beyond simply receiving and cleaning datasets. We establish practical processes that help ensure that data are complete, consistent, well documented and fit for purpose before they reach the analysis stage.
Our team supports studies from the point at which questionnaires and digital data collection tools are developed through field data monitoring, data quality assurance, cleaning, documentation, analysis and reporting. We work closely with research and field teams throughout the process so that issues can be identified and addressed while data collection is still underway, rather than waiting until the end of fieldwork.
We are comfortable working with large and complex datasets, including household surveys, community surveys, health facility assessments, longitudinal studies, monitoring exercises and other research datasets. Our workflows are designed to maintain a clear audit trail of data decisions and produce datasets that are structured and documented for subsequent analysis or use by clients and research teams.
Our team has practical experience managing and analysing quantitative research data across different study designs and sectors. We work with raw field submissions, structured survey datasets and complex research datasets, applying systematic checks before analysis to ensure that the resulting data are consistent, traceable and ready for use.
Depending on the requirements of the study, our analysts can undertake data restructuring, variable generation, recoding, missing-value assessment, consistency checks, outlier review, descriptive analysis, subgroup comparisons, statistical testing, confidence intervals and other study-specific analyses.
We also place considerable emphasis on reproducibility. Where appropriate, analytical processes are documented through syntax, do-files, scripts, codebooks and other supporting documentation so that datasets and analytical outputs can be reviewed, updated and replicated by the research team.
We review datasets for duplicates, missing values, invalid responses, range errors, inconsistent responses and other patterns that may affect analysis.
We restructure, recode and derive variables to prepare datasets for statistical analysis while maintaining clear documentation of the transformations applied.
We produce descriptive statistics, cross-tabulations, confidence intervals, subgroup comparisons and other study-specific statistical outputs.
We develop clear statistical tables, charts and analytical outputs that can be incorporated directly into research reports, presentations and decision-making products.
Our team works across a range of statistical and programming environments. This allows us to select the appropriate tool depending on the study design, dataset, analytical requirements and reporting needs of each assignment.
Data management, statistical analysis, survey analysis and reproducible reporting.
Statistical computing, data analysis, visualisation and reproducible workflows.
Data processing, automation, analysis, visualisation and advanced data workflows.
Our analytical capacity extends beyond quantitative research. We also support qualitative studies involving interviews, focus group discussions, key informant interviews, observations and other forms of textual and multimedia research data.
Our team can support the organisation, preparation, coding and analysis of qualitative data, helping research teams move from raw transcripts and field notes to structured themes, patterns and findings. We can develop coding frameworks, organise documents, apply codes consistently and produce analytical summaries that remain closely connected to the research questions.
Where required, we use computer-assisted qualitative data analysis software to support systematic coding, retrieval and organisation of qualitative evidence. This provides a structured environment for managing large volumes of interview and focus group data while maintaining a clear link between source material, codes, themes and analytical conclusions.
Qualitative coding, thematic analysis, document organisation and evidence synthesis.
Qualitative coding, thematic analysis, memoing and organisation of research evidence.
For us, data quality is not an activity that starts after fieldwork has ended. We establish quality assurance processes that allow the research team to monitor incoming data while fieldwork is ongoing and respond quickly when potential issues are identified.
Our data management team works closely with field supervisors and project teams to review incoming submissions, identify unusual patterns and communicate findings back to the field. This creates a continuous feedback loop between data management and field implementation.
We combine automated checks with human review. Automated checks help us identify patterns and potential errors quickly, while experienced data managers investigate unusual findings and determine whether they reflect genuine field conditions, questionnaire issues or data quality concerns.
Where an issue requires attention, feedback is communicated to the field team so that corrective action can be taken promptly.
We use High Frequency Checks (HFC) as part of our field data quality assurance process. HFC allows the data management team to review selected indicators from incoming submissions on a regular basis and identify potential problems early in the data collection process.
Rather than waiting until the end of fieldwork to discover problems, we provide regular feedback to supervisors and field teams. This enables issues to be investigated while enumerators are still in the field and, where necessary, allows corrective action, clarification or retraining to take place.
Incoming field submissions are received and reviewed as data collection progresses.
Automated and manual checks are used to identify unusual patterns, inconsistencies and potential quality concerns.
Findings are summarised and communicated to supervisors and the field team.
The field team investigates issues and takes appropriate corrective action while data collection continues.
Digital questionnaires with logical skips, constraints, calculations, rosters, validation rules and other controls designed to improve data quality at source.
Structured review and cleaning of raw datasets, including duplicates, missing values, range errors, inconsistencies and other potential data problems.
Routine monitoring of incoming submissions and rapid feedback to field teams to support timely identification and resolution of data quality issues.
Descriptive and inferential statistical analysis using appropriate statistical software and reproducible analytical workflows.
Systematic coding, thematic analysis and synthesis of interviews, focus group discussions and other qualitative research materials.
Clear tables, figures, dashboards, analytical summaries and research reports designed for technical and non-technical audiences.
Let's discuss your data management, quality assurance or analysis requirements.