Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
Support with STATA do-files, data management, panel and time series models, survey-weighted analysis, post-estimation tests and publication-quality output tables.
Tell us about your stata analysis need. We reply within one working day.
STATA analysis help is for scholars in economics, finance, commerce, public health and demography who work with structured, often large, datasets. STATA from StataCorp is valued for its command-based workflow, strong panel and survey tools and reliable post-estimation commands. It suits theses based on company financials, district-level data or national surveys such as NFHS, PLFS or NSS rounds. We support all of these.
We help you work in STATA the way experienced researchers do. You define the research question and the economic or health logic. We help you write clean do-files, merge and reshape data, declare panel or survey structure, run the right estimators, perform specification tests, compute marginal effects and export tables. Every result can be regenerated from your do-file with one command.
A good STATA project is driven by do-files: text scripts that load raw data, clean it, create variables, run models and export results. Log files record output. This keeps work reproducible and makes revisions after supervisor or referee comments fast. Data management commands such as merge, append, reshape, egen and collapse handle complex data preparation.
For panel data, xtset declares the panel and commands such as xtreg estimate fixed and random effects, with hausman comparing them. Time series work uses tsset with unit root and lag tests. Survey data is declared with svyset so svy commands apply weights, strata and clusters. Post-estimation tools like test, margins and predict turn coefficients into interpretable results.
Your entire analysis lives in organised, commented do-files, so results can be rerun instantly after corrections or checked by your supervisor.
Fixed effects, random effects, Hausman tests, clustered errors, dynamic panels and diagnostic tests for serial correlation and heteroscedasticity are supported.
NFHS, PLFS, NSS and similar datasets are merged, recoded and analysed with correct sampling weights and design variables declared through svyset.
Marginal effects, predicted probabilities and elasticities make logit, probit and log models understandable for examiners and policy readers.
Unit root, cointegration, endogeneity and instrument tests are run where your design needs them, with plain explanations of each result.
Regression tables are exported to Word or Excel using commands such as esttab or outreg2, with consistent formatting across models.
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 —
Unit and time identifiers are checked for duplicates so panel and merge operations behave correctly.
Merge result codes are reviewed to confirm matched, unmatched and dropped observations are understood.
Survey weights and design variables are checked against official documentation before estimates are produced.
Model choice is checked against test results and theory, such as Hausman results for panels.
Clustering and robust options are reviewed so standard errors reflect your data structure correctly.
The master do-file is run from start to end to confirm every reported number reproduces exactly.
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 analysis interactively and having no record of which commands produced the results. | Write every step in a do-file with comments, and save a log, so results can be reproduced and checked anytime. |
| Using m:m merge, which silently produces incorrect combinations of observations. | Use 1:1, m:1 or 1:m merges based on unique identifiers, check them with isid, and review merge result codes. |
| Analysing NFHS or NSS data without sampling weights or survey design variables. | Declare the design with svyset using weights, strata and primary sampling units from official documentation, then use svy commands. |
| Reporting logit coefficients as if they were changes in probability. | Report odds ratios or use margins to compute average marginal effects that express changes in predicted probability. |
| Ignoring serial correlation and heteroscedasticity in panel models of firms or states. | Test for these problems and use cluster-robust standard errors at the panel unit level where appropriate. |
| Running time series regressions on non-stationary variables without any unit root testing. | Test stationarity with suitable unit root tests, then difference variables or use cointegration methods as results require. |
Keep original files untouched and let do-files create cleaned versions. This protects you if a cleaning step needs correction.
A master file that runs cleaning, analysis and export in order lets you regenerate the whole thesis analysis with one command.
Variable and value labels make outputs readable and help you remember coding months later when writing chapters.
Confirm unique identifiers before merging. A quick isid check prevents duplicate observations that quietly distort results.
Use estimates store or eststo after each model so several models can be combined into one comparison table.
National surveys provide guidance on weights and design variables. Following it exactly is essential for valid population estimates.
Average marginal effects and predicted values make non-linear models clear to readers who are not econometricians.
Add a version statement at the top of do-files so commands behave the same way when rerun on newer versions.
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.
STATA is especially strong for panel data, econometrics, survey-weighted analysis and reproducible command-based workflows. SPSS is very common for questionnaire-based research. Many economics, finance and health theses use STATA because referees in those fields often expect it. We support both.
Yes. We help you select the right NFHS file, such as individual, household or birth recode, merge files where needed, recode variables using the official documentation and apply sampling weights and design variables so estimates are correctly representative.
Theory comes first: fixed effects control for time-invariant unit characteristics, while random effects assume these are unrelated to your regressors. The Hausman test compares both. We help you run it, interpret it and justify the final choice alongside other diagnostic results.
Yes. You receive commented do-files, the cleaned dataset and logs. With access to STATA you can rerun everything. We also walk you through the files so you understand each command and can modify the analysis later.
No. Results depend on your data and model, and we never manipulate data to change them. We help you specify models correctly, apply suitable tests and report findings honestly, including non-significant results, which are legitimate and useful in econometric work.
Yes. We support difference-in-differences with checks on pre-treatment trends, and instrumental variable estimation with first-stage strength and over-identification tests where applicable. We help you explain identification assumptions clearly in your methodology chapter. Event-study plots can also be prepared.
Yes. Training covers do-files, data management, regression, panel commands, survey analysis, margins and table export, using your own data. Learning to read and edit your own do-files is one of the best preparations for viva and referee 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.