Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
Numbers and transcripts only matter once they answer your research questions. We analyse quantitative and qualitative data and help you interpret every result in plain terms.
Tell us about your research & analysis services need. We reply within one working day.
Many scholars receive analysis output that they cannot read. Pages of SPSS tables or a long list of NVivo codes arrive, but nobody explains what they mean for the objectives. Research analysis services from Shodhyantri pair the technical work with interpretation, so you understand why a test was chosen, what each result shows and how it connects to your hypotheses and literature.
We work with survey data, secondary datasets, experimental readings, interview transcripts, focus group notes and open-ended responses. Quantitative work uses SPSS, SmartPLS, AMOS, STATA, EViews, R or Python. Qualitative work uses NVivo, ATLAS.ti or MAXQDA, or structured manual coding. You get clean outputs, formatted tables and interpretation notes that guide your own results and discussion writing.
Analysis is not just running tests. It starts with the research questions, moves to data screening and choice of method, then to execution, and ends with interpretation that a reader can follow. When any link breaks, the results chapter feels disconnected. Our process keeps the chain intact from objective to conclusion.
In mixed methods studies, the two strands must speak to each other. Survey results may show that a factor matters, while interviews explain why. We help you plan whether the design is convergent, explanatory or exploratory, analyse each strand properly and integrate the findings through joint displays or a clear comparative discussion.
Every table comes with a plain explanation of what it means, so you can write and defend the results yourself.
We handle numerical and textual data, which matters when your study combines a survey with interviews or field observations.
Tests are chosen by measurement level, distribution and design, not by habit. Alternatives are explained when assumptions fail.
Tables and figures follow APA or your university format, with test statistics, degrees of freedom, p-values and effect sizes reported properly.
Syntax files, codebooks and coding frameworks are shared, so anyone can see how the results were produced.
Non-significant results are reported and discussed, not hidden. Examiners respect honesty far more than suspiciously perfect numbers.
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 —
Confirms each objective has an agreed test or qualitative approach before data work begins.
Checks coding, missing values and outliers are handled and recorded in a data log.
Verifies normality, linearity, homogeneity or other assumptions, or justifies alternative methods used.
Reviews a sample of qualitative coding for consistency and clear code definitions.
Checks statistics, sample sizes and quotations in tables match the source outputs exactly.
Confirms explanations stay within what the data shows and avoid causal claims without support.
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 |
|---|---|
| Running every available test and reporting all outputs in the results chapter. | Report only analyses linked to objectives and hypotheses. Extra tests confuse readers and invite unnecessary examiner questions. |
| Reporting only p-values without effect sizes or confidence intervals. | Include effect sizes and intervals so readers can judge practical importance, not just statistical significance. |
| Treating qualitative themes as a list of topics people mentioned. | Build themes that explain patterns, with clear definitions and quotations that show variation across participants. |
| Presenting mixed methods results in two unconnected chapters. | Add an integration section or joint display that shows where findings agree, differ or explain each other. |
| Using causal language for cross-sectional correlation results. | Use words like 'associated with' or 'predicts' carefully, and acknowledge design limits in the discussion. |
| Copying output tables directly from software into the thesis. | Rebuild tables in a clean format with only the needed values, proper titles, notes and consistent decimal places. |
Phrasing each hypothesis with its variables and expected direction makes test selection and reporting far simpler.
Code two or three transcripts first, refine the codebook, then code the rest for consistency.
Record every cleaning decision with a reason. Examiners often ask how missing values and outliers were handled.
A non-significant finding still answers a question. Discuss possible reasons such as sample size or measurement issues.
Choose short quotations that illustrate a theme well. Long blocks of transcript weaken analysis rather than supporting it.
Use the same number of decimal places across tables. Inconsistent rounding looks careless and causes small mismatches.
For each key result, note one or two studies that agree or disagree. This forms the backbone of your discussion.
Syntax or scripts let you rerun analysis quickly if a supervisor asks for a change or a variable is corrected.
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.
Data analysis often means producing outputs. Research analysis connects those outputs to your questions, hypotheses and literature, so you understand what the findings mean. We provide both the technical results and the interpretation notes that support your own writing.
Yes. We use thematic, content or framework analysis depending on your design, working in NVivo, ATLAS.ti, MAXQDA or a structured manual process. You receive a codebook, themes with definitions and supporting quotations.
We prepare tables, figures and detailed interpretation notes, and we review the chapter you write. The written chapter itself remains your work, which also prepares you well for questions in the viva.
No. We never alter data or select tests to produce a desired result. We choose methods that suit your data and report findings honestly. Non-significant results are still valid findings and can be discussed meaningfully.
Yes. We analyse each strand properly and help integrate them, using joint displays, comparison tables or a clear integration section, depending on whether your design is convergent, explanatory or exploratory.
We use the software your university or supervisor prefers where possible, including SPSS, SmartPLS, AMOS, STATA, EViews, R, Python, NVivo, ATLAS.ti and MAXQDA, and we share the project or syntax files.
Yes. We recommend anonymising data before sharing, and we use your files only for your project. Data and transcripts are not shared with anyone else or reused.
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.