Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
One complete service covering your analysis plan, data cleaning, test selection, software outputs, APA tables, interpretation notes and a walk-through before your review meeting.
Tell us about your statistical analysis support need. We reply within one working day.
Statistical analysis support at Shodhyantri is a complete, start-to-finish service for the data analysis stage of your thesis or paper. You bring a dataset and a set of objectives. We build an analysis plan, clean and screen the data, run the right tests in the software you prefer, format the results to your university or journal style and explain every number until you are comfortable with it.
This page describes the full service. If you only need one technique, such as regression or structural equation modelling, see the dedicated pages for those. Here the focus is on the whole workflow, the checks we apply at each step and what you receive at the end. The analysis supports your research. The interpretation and the writing of your chapter remain your own.
A full statistical analysis service covers everything between data collection and the written results chapter. That includes a written analysis plan mapped to each objective, data entry checks, coding, handling of missing data and outliers, descriptive statistics, reliability testing, assumption checks, inferential tests, post-hoc comparisons, effect sizes and reporting.
The plan is the backbone. Writing it first, objective by objective, prevents the common situation where scholars run every test the software offers and then struggle to explain why. A plan also lets your supervisor approve the approach before outputs are produced.
Planning, cleaning, analysis, tables and interpretation are handled together, so nothing gets lost between different helpers or software files.
We work in SPSS, R, STATA, Python, EViews, Minitab, JASP, AMOS and SmartPLS, choosing the tool your university or journal expects.
You receive syntax or scripts with outputs, so any figure can be rerun after a correction or verified by your supervisor.
Each test is explained in simple language, including why it was chosen and how to read the output, so you can present it yourself.
When your supervisor or reviewer asks for an extra test or a different approach, we help you update the analysis and tables quickly.
Your dataset is used only for your analysis and is not shared. Personal identifiers can be removed before you send the file.
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 —
No analysis starts until the objective-wise plan is agreed, keeping the work focused and explainable.
Every change to the raw data is recorded so the cleaned dataset can be justified if questioned.
A test proceeds only after its assumptions are checked, or an alternative test is chosen instead.
Figures in each table are matched back to software output to catch copying and rounding errors.
Sample sizes, decimals, labels and table numbering are checked to agree across the whole chapter.
Statements in the interpretation notes are compared against results so no claim exceeds the evidence.
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 |
|---|---|
| Starting analysis without a written plan and running every available test. | Write an objective-wise plan first, naming the variable, test and table for each objective, and share it with your supervisor. |
| Deleting respondents with missing data without checking how much is missing or why. | Examine missing patterns first, report the extent, and choose deletion or imputation with a clear justification in your methodology. |
| Forgetting to reverse-score negatively worded items before computing totals. | List reversed items in the codebook and recode them before calculating composite scores or reliability, then recheck alpha values. |
| Copying numbers from output into Word by hand and introducing errors. | Build tables carefully from exported output and cross-check each value, especially sample sizes, means and degrees of freedom. |
| Running many separate t-tests across groups instead of one suitable test. | Use ANOVA or its non-parametric equivalent with post-hoc comparisons to control error across several groups. |
| Interpreting a significant correlation as proof that one variable causes another. | Describe correlations as associations, consider confounding variables, and reserve causal language for designs that support it. |
Keep an untouched copy of your raw data. Work only on a cleaned copy so you can always return to the original.
Short, consistent variable names such as JS1 to JS8 make syntax readable and reduce errors when computing scale scores.
Present the respondent profile in one clear table early in the chapter rather than scattering demographic numbers across sections.
Start each results section by restating the objective, then present the table, then a short factual interpretation.
Include n in every table or note so readers can see if cases were excluded from a particular analysis.
Use two decimals for most statistics and three for p-values, applied the same way in every table of the chapter.
Use charts only where they show a pattern more clearly than a table. Decorative 3D charts weaken a results chapter.
Before your meeting, practise explaining why each test was chosen and what its assumptions were. It builds real confidence.
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.
Send your data file, the questionnaire or tool you used, and your objectives and hypotheses. If you have supervisor comments or a preferred software, share those too. With these we can prepare a feasibility note and an analysis plan before any outputs are produced.
Yes. Many Indian universities expect SPSS for survey work, while economics and panel studies often use STATA or EViews, and data science work uses R or Python. We use the software you or your supervisor prefer and share files you can open.
We provide formatted tables, outputs and interpretation notes, and we review your chapter draft for accuracy. The chapter itself remains your writing. This matters because you will need to explain every table at your pre-submission seminar and viva, and your own words help you do that.
No. Significance depends on your data and design, and changing data to force results would be research misconduct. We run correct tests and report results honestly. Non-significant findings are valid and, when reported with effect sizes and context, are fully acceptable in a thesis.
That is normal and expected. Send us the comments and we will update the analysis, add or change tests and revise tables. Because we keep syntax and a cleaning log, revisions can usually be made quickly and consistently across the chapter.
Yes. Your dataset is used only for your analysis and is not shared with anyone else. We recommend removing names, phone numbers and other identifiers before sending, and we can show you how to do that if needed.
Yes. Alongside the service we can hold a training session in the same software, using your own data, so you learn how each output was produced. Many scholars find this the best preparation for viva questions.
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.