Need assistance from a qualitative content analysis service?
Our qualitative content analysis service (from £95) provides expert support for non-interview qualitative datasets such as social media content, open-ended survey responses, forum posts, policy/strategy documents, reflective diaries and other text-based sources. We deliver systematic coding, clear category structures, frequency outputs, and NVivo-style tables and matrices to support dissertations, theses, research articles, and organisational reports. Secure, confidential, and fast turnaround. If you need help with content analysis, we can conduct the full workflow and provide a comprehensive NVivo-style report with all the outputs you need to write up your findings and methods.
Full NVivo-formatted Content Analysis report
Including everything you need:
✔ Familiarisation Summary
✔ NVivo-style codebook (categories, definitions, inclusion/exclusion rules)
✔ Node structures (parent and child categories)
✔ Systematic coding across your full dataset
✔ Coding summaries (references, cases, category distributions)
✔ NVivo-style matrices (e.g., Categories × Cases / Categories × Questions / Categories × Attributes)
✔ Category refinement and abstraction (with a clear audit trail)
✔ Analytic interpretation & synthesis (manifest and, where appropriate, latent content)
✔ Findings + Methods chapter guidance (thesis/dissertation or article-ready)
✔ Guidance on how and where to use your tables/matrices in your write-up
✔ NVivo-style presentation for every output
👉 Take a look at our full sample report. This is what you’ll receive
Need Thematic Analysis instead?
If you are using Thematic Analysis (Braun & Clarke) with interviews, focus groups or transcripts, we offer a dedicated service for that too. Click here for our thematic analysis service.
Fast, low-cost service
Qualitative content analysis can be time-consuming—especially if you are learning NVivo (or similar software) at the same time as trying to analyse your data. Many students spend weeks preparing datasets, building codebooks, running coding queries, and producing matrices for their results chapter. It doesn’t have to be like this.
We aim to complete your data analysis within 48 hours of receiving your dataset.
What types of data can you analyse?
- Open-ended survey responses (single question or multiple prompts)
- Social media posts/comments (e.g., Reddit threads, public comments, platform excerpts)
- Policy and organisational documents (guidance, strategies, reports)
- Forum discussions and community threads
- Reflective diaries or learning logs
- Other text-based qualitative datasets (ask if unsure)
Preparing your data document
Before sending your data file, please ensure it is clean, complete, and consistently formatted. The goal is clarity and consistency rather than perfect writing. Where possible, organise your dataset so that each entry can be treated as a separate “case” (e.g., one respondent per row; one social media comment per row; one document per file). Remove or anonymise identifying information (names, handles, locations, workplaces, unique personal details) and replace these with neutral descriptors. Submit your file in an editable format (preferably Word, Excel, or CSV) with no tracked changes or comments.
Recommended formats:
- Survey data: one respondent per row, columns for Question 1, Question 2, etc., plus optional attributes (e.g., gender, year group, role).
- Social media/forum data: one post/comment per row, with columns for a Case ID and any metadata you have (platform, date, thread, stance, etc.).
- Documents: one document per file (or a single file with clear document headings), with a document ID and document type.
Does my data need proofreading/editing?
For content analysis, your dataset does not need to be “polished,” but it must be usable and accurate. The aim is to ensure entries are understandable and consistently formatted so that coding decisions remain stable across the dataset.
Before analysis, please check that:
- Each case/text entry is clearly separated (e.g., one row per case, or clear headings)
- The dataset is complete (no cut-off text, missing rows, or duplicates)
- Any identifiers are anonymised (names/handles removed where needed)
- Text is readable enough to retain meaning (fix obvious errors that change meaning)
- If you have attributes/metadata, they are consistent (same labels/spelling used throughout)
Finally, please indicate whether you are working with British (International) English or American English.
