Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
Classical and Bayesian analysis in JASP and jamovi, with APA-ready tables, saved analysis files and plain explanations you can carry into your viva.
Tell us about your jasp & jamovi analysis need. We reply within one working day.
JASP and jamovi analysis gives scholars a free, modern alternative to licensed statistics packages. Both programs produce clean APA-style tables, update results as you change options, and save analyses inside the data file so they can be reopened and checked. Shodhyantri supports PhD and Master's scholars in psychology, education, management and health sciences who use these tools for their thesis or paper.
A distinctive strength is Bayesian analysis. JASP in particular makes Bayes factors, prior settings and robustness checks accessible from menus. We help you decide whether a classical or Bayesian approach, or both, suits your questions, run the analysis correctly and understand what the numbers say. The interpretation and writing remain yours; we make sure the statistics are right and well reported.
JASP is developed at the University of Amsterdam and jamovi is a community-led project; both are free and built on R. They offer menu-driven analysis similar to SPSS but with results that refresh instantly and tables formatted close to APA style. Saved files keep data, settings and output together.
JASP offers Bayesian versions of t-tests, ANOVA, correlation, regression and contingency tables alongside classical tests, plus modules for SEM, meta-analysis and machine learning. jamovi extends through a module library and lets advanced users run R code inside the program. Both suit scholars without access to paid licences.
Scholars without university SPSS access can run thesis-grade analysis legally and rerun it later on any computer.
We explain Bayes factors, priors and robustness checks in plain terms so you can report evidence for the null as well.
Supervisors can open your .jasp or .omv file and see every setting used, which supports open and checkable research.
McDonald's omega is reported alongside alpha, reflecting current recommendations for scale reliability in psychology and education.
Both tools make effect sizes and confidence intervals easy to add, so your results meet APA reporting standards.
Scholars familiar with SPSS adapt quickly. We help map familiar procedures to JASP or jamovi menus.
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 —
Each variable's measurement level is confirmed, since JASP and jamovi restrict tests by type.
Normality, equal variances and sphericity checks are inspected before reporting parametric tests.
Default or informed priors are documented with a reason suitable for your methodology chapter.
Bayes factor conclusions are checked across a range of reasonable prior widths.
Exported tables are compared with software output to catch rounding and label errors.
Saved analysis files are reopened to confirm all results and settings are preserved.
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 |
|---|---|
| Reading a non-significant p-value as evidence that there is no effect. | We explain the difference and, where useful, add a Bayes factor to show whether data actually support the null. |
| Using default Bayesian priors without saying why or checking their influence. | We document the prior, explain it briefly and include a robustness check so conclusions are not prior-dependent. |
| Leaving Likert totals set as nominal so tests are unavailable or wrong. | Computed scores are set to continuous type, and item-level decisions are stated in your methodology. |
| Reporting Cronbach's alpha alone for a scale with unequal item loadings. | We report McDonald's omega alongside alpha, which is better suited when items load unequally. |
| Copying output tables directly into the thesis without fixing labels or decimals. | Tables are edited for clear variable names, consistent decimals and APA formatting before they go into your chapter. |
| Interpreting Bayes factors with rigid cut-offs as if they were p-values. | We present Bayes factors as continuous evidence and use descriptive labels cautiously, citing the scheme used. |
Clear variable names make output tables readable and reduce editing time when you export to Word.
Keep the .jasp or .omv file with your thesis records. It lets anyone verify exactly how results were produced.
Choose one direction for Bayes factors and explain it once. Switching confuses readers and examiners.
Tick effect size options for every test. Cohen's d, eta squared or omega squared help readers judge practical importance.
Both JASP and jamovi publish recommended citations with version numbers. Include them in your reference list.
Q-Q plots and residual plots are available in a click and often more informative than formal normality tests.
Stating hypotheses and priors before analysis strengthens Bayesian and classical results in many journals.
Note the version used. Updating mid-analysis can change defaults or table layouts.
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.
Yes, both are widely used in published research and cite their underlying R packages. Check with your supervisor, but most universities accept any established software if you name the version and describe the analysis clearly.
Both are free and R-based. JASP has a deeper built-in set of Bayesian analyses. jamovi has a flexible module library and allows R code inside the program. For most thesis tests either works; we recommend based on your needs.
Not necessarily. Classical analysis is still standard in most Indian theses. Bayesian analysis helps when you want to show evidence for no effect or when reviewers request it. We advise whether it adds value to your study.
No. Outcomes depend on your data. We guarantee careful method and honest reporting. Bayesian analysis can actually help here, because it lets you report support for the null clearly rather than treating it as a failed result.
Yes, both import .sav files, usually with labels. We check that value labels and measurement levels came through correctly before analysis. Missing value codes such as 99 or 999 are also checked, because they can be read as real numbers if not set correctly. We fix these before any test is run.
No. We provide tables, plots and interpretation notes and explain them. You write the section in your own words, and we can review it for accurate reporting of statistics. Our feedback focuses on test values, degrees of freedom, effect sizes and table formatting.
Yes, both offer SEM through lavaan-based modules, suitable for many thesis models. For complex models we may suggest R or AMOS, and explain the trade-offs. Fit indices such as CFI, TLI, RMSEA and SRMR are reported and explained so you can judge model adequacy yourself.
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.