Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
Quotation-level coding, code groups, co-occurrence tables and network views in ATLAS.ti, used to build and test the relationships at the heart of your study.
Tell us about your atlas.ti analysis need. We reply within one working day.
ATLAS.ti qualitative analysis is well suited to studies that aim to explain how concepts connect, not only which themes exist. Shodhyantri supports PhD and Master's scholars who use ATLAS.ti for grounded theory, qualitative content analysis, discourse analysis and case studies. We help you work at quotation level, organise codes into groups, examine co-occurrence and draw network views that show relationships between categories.
Your theory or framework must emerge from your own reading of the data. Our role is methodological and technical: structuring the project, reviewing open, axial or focused coding for consistency, helping you use memos for constant comparison, and preparing outputs for your findings chapter. Everything remains traceable from a network link back to the quotation that supports it. We also help you write the methods section that explains these steps clearly.
ATLAS.ti, developed by ATLAS.ti Scientific Software Development GmbH in Berlin, treats the quotation as a first-class object. You mark a segment, name it, link it to codes and comment on it. Codes can be gathered into code groups, documents into document groups, and any of these can be linked in network views with named relations such as is cause of or contradicts.
This design fits approaches where relationships matter. Grounded theory researchers use networks to show how categories link around a core category. Content analysts use the code-document table to count codes across document groups. Recent versions also offer AI-assisted coding suggestions, which need careful researcher review.
Network views with named relations let you show how categories connect, which is central to grounded theory and explanatory case studies.
The co-occurrence table shows where codes overlap in the same passages, supporting claims about how concepts relate in participants' accounts.
We help structure open, axial and selective or focused coding stages so your theory development is visible and orderly.
Interviews, field notes, policy documents, images and social media posts can be analysed together in one ATLAS.ti project.
If you use ATLAS.ti AI coding features, we help you review and document them so the analysis stays researcher-led and transparent.
Every link in a network and every count in a table can be traced back to the exact quotation behind it.
Ten stages, each with a clear output. Nothing moves to the next stage until you have reviewed the last one.
— You review every stage first, then decide —
Quotations are checked to be long enough for context yet focused on a single idea.
Codes follow a consistent naming pattern so groups and searches work reliably.
Every network link has supporting quotations and a memo explaining the chosen relation type.
High overlap counts are read in context to confirm they reflect meaning, not coding habits.
Category development is reviewed for whether new data still adds properties or dimensions.
Project bundles are exported and test-opened so nothing is lost between machines or versions.
Every stage is shared with notes explaining what was changed and why. You and your supervisor check it, and the next stage starts only after your go-ahead.
| Common problem | How Shodhyantri helps |
|---|---|
| Drawing network links based on hunches without quotations that support each relation. | We link every relation to supporting quotations and a memo, so networks represent evidence rather than assumptions. |
| Accepting AI-suggested codes in bulk without reading the underlying quotations. | AI suggestions are reviewed passage by passage, edited or rejected, and their use is disclosed in your methodology. |
| Mixing grounded theory terms from different schools in one methodology chapter. | We help you choose one tradition, such as Charmaz's constructivist approach, and use its terms and stages consistently. |
| Reading high co-occurrence counts as proof that one concept causes another. | Co-occurrence is presented as overlap in talk; causal or conditional claims rest on interpretation backed by quotations. |
| Coding whole paragraphs as single quotations so detail is lost. | We guide quotation boundaries so each segment captures one meaningful idea with enough context to be understood. |
| Losing work because the project was never exported as a bundle. | Regular project bundle exports are built into the workflow and stored outside the ATLAS.ti library. |
Prefixes such as BAR_ for barriers or SUP_ for support keep long code lists sorted and easy to group.
Giving key quotations short names makes them easy to find when writing findings and building networks.
Each emerging category needs a memo describing its properties, conditions and consequences. These memos become your findings text.
Document groups such as urban and rural schools let you compare categories across contexts with one table.
Use a small set of clearly defined relations. Too many relation types make networks hard to read and defend.
In grounded theory, choose later participants to develop categories further, and document why each was selected.
ATLAS.ti stores projects in an internal library. Weekly bundle exports protect months of coding work.
State the ATLAS.ti version and which features you used, including any AI functions, in your methodology chapter.
Your work is handled by people who read your field's literature, not by a single generalist.
A PhD-qualified specialist from your field who checks the research logic, gap and contribution.
Handles statistics, models and qualitative coding, and documents every analytical decision.
Improves structure, flow, clarity and academic tone without changing your meaning.
Builds and validates MATLAB, Python, ANSYS or COMSOL work where your study needs it.
Checks grammar, consistency, numbering, citations and the small errors examiners notice.
Applies your university or journal template, reference style and front matter exactly.
Neither is universally better. ATLAS.ti is strong for quotation-level work and network views, which suit grounded theory and relational analysis. NVivo is strong for case classifications and matrix queries. Your method, supervisor and institutional licence usually decide the choice.
We guide the process: coding stages, memo writing, category development and network views. The theory itself must emerge from your interpretation of your data. We review its coherence and how well it is supported by quotations.
It can speed up initial exploration, but suggestions must be checked against the text. Many universities and journals now expect AI use to be disclosed. We help you decide where it is appropriate and how to document it honestly.
ATLAS.ti supports Unicode, so Hindi, Marathi and Punjabi text can be imported and coded. Automated features may perform better with English. We plan manual coding where needed and help with translated quotations for reporting. We also check fonts display correctly in exports.
No. Committees and examiners make their own judgement. We help you follow a recognised method, keep a clear audit trail and present well-supported categories, which gives your work the best footing for review. Final judgement remains with them.
Yes, through project bundles, which open across platforms in compatible versions. We test bundle export and import so your project moves safely between your laptop and a university machine. We also advise on keeping one master copy so coding is never split across two versions.
No. You receive networks, tables, quotation reports and memo summaries, and we explain them. You write the chapter. We can review your draft for consistency between claims and the coded evidence. This keeps the analysis and the authorship clearly yours.
We provide research guidance, statistical analysis, editing, formatting and training support. All research content remains the scholar's own work, in line with UGC and university academic integrity norms. We do not guarantee publication, acceptance, indexing or academic outcomes: journals decide through peer review and universities award degrees.
Share your topic, draft or data. We will tell you honestly what it needs, and what it does not, before you pay anything.