My technical background includes data processing and analysis using Stata, SPSS, Python and R, as well as Excel dashboards and Power BI visualisation. The aim is to move from a well-prepared dataset to outputs that answer the research questions and can be understood by the people who need to use them.
How I approach the work
When it comes to analysis, I always start by looking at the broader study design before drawing conclusions. On the quantitative side, that means diving deep into variable distributions, examining missingness patterns, and verifying that every indicator aligns with what the protocol intended. I treat reproducible scripting as non-negotiable: whether I am constructing composite variables, applying survey weights, or handling edge-case exclusions, every step is scripted and commented so the workflow is fully auditable.
For qualitative work, I bring that same discipline to text, audio, and thematic evidence. Using tools like ATLAS.ti and NVivo, I develop structured codebooks grounded in the research questions while leaving room for emerging insights. I focus on systematically organizing focus group discussions, key informant interviews, and observational notes so that the narrative captures the true nuance of respondents' voices rather than just cherry-picked quotes.
From experience, reporting is not just packaging numbers or soundbites at the end of a project; it is an extension of analytical thinking. I integrate quantitative trends with qualitative context, designing visual dashboards, tables, and figures that present comparisons honestly, without stretching what the data can support. In the narrative, I make sure the evidence speaks directly to the core questions, clearly flagging limitations and never confusing correlation with causal impact. Especially in monitoring, evaluation, and impact studies, keeping conclusions strictly within the scope of the evidence is how you deliver findings that partners, donors, and policymakers can trust.
What this looks like in practice
My CV also records technical report writing for quantitative and qualitative surveys, and experience creating Excel dashboards and visualisations with Power BI. The choice of software, statistical approach and reporting format depends on the data, questions and agreed deliverables.
Relevant areas of work
- Data processing and statistical analysis
- Quant: Stata and SPSS
- Qual: ATLAS.ti and Nvivo
- Python and R
- Excel dashboards and data visualisation
- Power BI reporting
- Technical report writing