Collecting data is only half the work — what happens between raw data and your results chapter determines whether your findings hold up under questioning. We clean, code, and analyze both quantitative and qualitative data, and document every step so your process is reproducible.
| Step | Why It Matters |
|---|---|
| Data cleaning | Missing values, outliers, and entry errors can distort results if left unaddressed |
| Variable coding | Categorical and ordinal variables need consistent, documented coding schemes |
| Assumption checking | Confirms the planned statistical test is actually appropriate for this dataset |
| Transcription (qualitative) | Accurate, verbatim transcripts are the foundation for trustworthy coding |
Skipping this stage is the most common reason a results chapter falls apart under questioning — an output table with no record of how the data was prepared looks suspicious to a careful reviewer, even when the underlying numbers are correct.
Your methods chapter should preview what your analysis will do; your results chapter should match it exactly. A common red flag for committees is a results chapter using an analysis approach that wasn't described — or was described differently — in the methodology. We check this alignment before delivery.
Cleaning, coding, analysis, and a clear audit trail — ready for committee scrutiny.
Yes — most of our data analysis work starts from a completed dataset, whether that's survey responses, interview transcripts, or secondary/archival data.
Yes, alongside the written results — so you have both the narrative explanation and the underlying files your committee may ask to see.
Non-significant or unexpected results are reportable findings, not failures. We write the results and discussion honestly, framing what the data actually shows rather than overstating support that isn't there.