Descriptive
From
₹6,500
24–36 hrs
Frequencies, cross-tabs, distribution checks.
- Up to 30 variables · n ≤ 1,000
- Cleaned dataset + diagnostics
- Tables in APA format
- 5-page interpreted summary
§ Data Analysis · Methodology & Interpretation
Data analysis with the interpretation written for you — SPSS, R, Python, defence-ready output.
We don't just run the tests. We pick the right ones for your hypotheses, document the assumptions, hand back a publication-grade results section, and stay on the line through your viva. Reproducible code included.
- 01Methodology first. Before a single line of code, we agree on the test, the assumptions, and the threshold. No surprises in chapter four.
- 02Interpretation included. You receive a written results section in your university's format — not just an SPSS output dump.
- 03Reproducible. Annotated `.R`, `.py`, `.sps`, or `.do` file plus the cleaned dataset. Re-run anything, any time.
- 04Viva-ready. A 30-minute call with the analyst before your defence. We answer the questions you'll be asked.
Working in
SPSS
v.29
Quantitative · social sciences
R
4.4
Statistical · advanced models
Python
3.12
ML · large datasets
STATA
18
Econometrics · panel data
AMOS
28
SEM · path analysis
NVivo
14
Qualitative · thematic
Smart-PLS
4
Structural equation
Excel
M365
Descriptive · pivot tables
Bring your dataset in any format — `.sav`, `.csv`, `.xlsx`, `.dta`, `.RData`, even a clean Word transcript for qualitative work. We handle the conversion.
§ 02 · The deliverable
What lands in your inbox — not just an SPSS dump.
A genuine excerpt from a Marketing dissertation: 412 respondents, brand-trust ↦ purchase-intent, mediated by perceived authenticity. Tables in your university's APA format, code annotated, interpretation written.
4.3 Mediation analysis
To test H3 — that perceived authenticity mediates the relationship between brand trust and purchase intent — a bias-corrected bootstrap mediation analysis (5,000 resamples) was conducted using the PROCESS macro (Hayes, 2022, Model 4). Sample-adequacy and normality checks are reported in §4.2.
Table 4.3 · Direct, indirect, and total effects (n = 412)
| Path | β | SE | t | p | 95% BC CI |
|---|---|---|---|---|---|
| Trust → Authenticity (a) | 0.612 | 0.041 | 14.93 | <.001 | [0.531, 0.693] |
| Authenticity → Intent (b) | 0.478 | 0.052 | 9.19 | <.001 | [0.376, 0.580] |
| Trust → Intent (c′, direct) | 0.214 | 0.048 | 4.46 | <.001 | [0.120, 0.308] |
| Indirect (a × b) | 0.293 | 0.039 | — | — | [0.220, 0.373] |
| Total (c) | 0.507 | 0.044 | 11.52 | <.001 | [0.421, 0.593] |
The bootstrap confidence interval for the indirect effect excludes zero (β = 0.293, BC 95% CI [0.220, 0.373]), supporting partial mediation: perceived authenticity carries 57.8% of the total effect of brand trust on purchase intent, while a meaningful direct path remains (c′ = 0.214, p < .001). H3 is therefore supported.
Brand
Trust
Trust
Perceived
Authenticity
Authenticity
Purchase
Intent
Intent
a = .612***
b = .478***
c′ = .214***
Figure 4.3 · Mediation path diagram. Solid: significant direct paths. Dashed: residual direct effect after mediator entered.
Analyst's note
The mediation is partial, not full — your committee will likely ask why authenticity does not absorb the trust effect entirely. The shortest defensible answer: brand-trust also operates through habituated repurchase, which the survey does not capture. I have flagged this in the limitations section (§5.4) and drafted two sentences you can lift directly. — Dr. P.
You receive
Results-chapter `.docx` · cleaned dataset · annotated `.sps` & `.spv` · figures as `.png` and editable `.pptx` · 30-min viva prep call.
§ 03 · The standard
Why this analysis holds up — under a thesis committee.
01
Methodology agreed before code
You will not receive a results section that uses the wrong test. We send you a one-page methodology brief — chosen test, assumptions to verify, hypotheses mapped — and only begin work after you sign off.
02
Assumptions documented, not skipped
Normality, homoscedasticity, multicollinearity, sphericity — every assumption is checked, reported, and footnoted. If your data violates one, we tell you which non-parametric path we took, and why.
03
Interpretation in your subject's language
Marketing dissertations get Marketing prose. Public Health gets Public Health phrasing. We do not return generic statistical paragraphs — the discussion reads like it belongs in your field's journals.
04
Reproducible. Always.
Annotated code, cleaned dataset, version pinned. Six months later your committee asks "where did the 0.293 come from?" — re-run the script, same answer. No hidden-spreadsheet magic.
§ 04 · Catalogue
Tests we run, indexed by family.
A working list, not exhaustive. If your method isn't here, ask — between SPSS, R, and Python the answer is almost always yes.
Undergraduate / Basic
- Descriptive statistics · frequencies · charts
- t-tests · chi-square tests
- Excel · SPSS analysis
- Clear result interpretation
Postgraduate / Advanced
- ANOVA · linear & logistic regression
- Correlation analysis
- Questionnaire & survey data analysis
- SPSS · R · Python support
- Results writing & interpretation
PhD / Research Level
- Multivariate analysis · EFA · CFA · SEM
- Advanced regression models
- Econometrics · panel data · time series
- Model validation · robustness checks
- End-to-end thesis data analysis support
Descriptive & data cleaning
- Frequencies · cross-tabulations · missing data · outlier checks
Hypothesis testing
- t-tests · ANOVA · non-parametric tests
Regression & prediction
- Linear · multiple · logistic regression
Multivariate & SEM
- Factor analysis · SEM · reliability · validity
Time-series & econometrics
- ARIMA · panel models · cointegration
Qualitative analysis
- Thematic coding · content analysis · discourse analysis
§ 05 · The process
From dataset to defence — step by step.
-
i.
Brief
Send your dataset, hypotheses, and your university's template. A 20-minute call (free) to scope the work.
-
ii.
Methodology brief
You receive a one-pager: chosen tests, assumptions to verify, deliverables. Approve, and we begin.
-
iii.
Analysis & interpretation
Code is written, assumptions tested, results interpreted. Daily updates by email if the project runs over 48 hours.
-
iv.
Delivery + viva prep
You receive the chapter, the dataset, the code, and the figures. A 30-minute call before defence is included — bring questions.
Working on a journal manuscript instead of a thesis? We deliver in journal-ready format — APA / AMA / Vancouver — with figures sized to publisher specs. Mention the target journal at brief and we'll match the house style.
§ 06 · Pricing
By scope of work, not by page count.
Indicative starting fees in INR. Final quotation arrives within twelve hours of brief — no payment until you approve.
Most common
Inferential
From
₹12,500
48–72 hrs
Group differences, regression, mediation. The most common slab.
- Up to 6 hypotheses tested
- Assumption checks footnoted
- Full results-chapter draft (15–25 pp.)
- Annotated `.sps` / `.R` / `.py`
- 30-min viva prep call
Advanced
From
₹22,000
4–7 days
SEM, CFA, multilevel, panel, time-series, ML.
- Model fit · convergent / discriminant validity
- Up to 3 model revisions
- Path diagrams · publication-grade figures
- Reproducible repository
- 60-min viva prep call
Qualitative
From
₹14,000
5–7 days
Thematic, framework, content analysis.
- Up to 30 transcripts · NVivo project
- Codebook + theme map
- Inter-rater κ on a sub-sample
- Findings chapter draft
QuotationWithin 12 hrs of brief.
RevisionsUp to 3, included.
Viva-prep call30–60 min, included.
ConfidentialityNDA on request, no charge.
§ 07 · Voices
What scholars say after the chapter lands in their inbox.
★★★★★
I came in with a messy SPSS file and three hypotheses that did not match the test I'd been told to run. The methodology brief reframed the entire chapter four. The defence was the easiest hour of my PhD.
Dr. M. Kapoor
IIM Indore · Marketing PhD
Inferential
★★★★★
Smart-PLS with seven constructs, two mediators, one moderator. They returned the full SEM, the path diagram, and a results section that needed three minor edits. Worth twice what I paid.
A. Rao
Anna University · PhD
Advanced (SEM)
★★★★★
The viva prep call was the unexpected gift. We rehearsed the three questions my external could ask about the indirect effect — and he asked exactly one of them.
Pranay G.
Manipal · MPhil
Inferential
★★★★★
Eighteen NVivo transcripts, three weeks until submission. They sent the codebook for review on day three, the theme map on day five, and the chapter on day seven. The ICC was 0.81.
Dr. S. Iqbal
JNU · Sociology
Qualitative
★★★★★
I asked for the analysis in R because my supervisor reads code. They sent a knitted RMarkdown document with assumption plots inline. I have used it as the methodology template for two more papers since.
Dr. R. Iyer
IIT Madras
Advanced (R)
★★★★★
Honest about scope. They told me my dataset (n = 84) was underpowered for the moderation I wanted, recommended a simpler model, and saved me from a viva I would have lost.
A. Sengupta
JU Kolkata · PhD
Inferential
Questions
FAQ
-
Either works. If you already know what test you need, we run it and write the interpretation. If you have hypotheses but are unsure of the right test, the first deliverable is a methodology brief — we read your hypotheses and your supervisor's comments, and recommend the test, the assumptions to verify, and the threshold for significance. Most scholars take this route.
-
Yes. Data cleaning is included in every package — missing-value treatment (listwise / mean / multiple imputation), outlier flagging (boxplot, Mahalanobis, Cook's D), recoding, reverse-scoring of negatively-worded items, and reliability checks. The cleaned dataset is returned alongside the original, with a change log.
-
Both. The standard deliverable for the Inferential and Advanced packages is a 15–25 page results chapter draft in your university's format — APA, Harvard, Vancouver, or a custom template. Tables and figures are formatted to publication or thesis standard, ready to drop into your final document.
-
Up to three revisions are included in every package — adding a control variable, switching from listwise to multiple imputation, swapping a moderator, re-running with a sub-sample. Larger structural changes (e.g. moving from regression to SEM after delivery) are quoted separately.
-
We sign an NDA on request, no charge. Datasets are stored on encrypted servers, accessed only by the assigned analyst, and deleted from our systems 90 days after delivery. We do not share datasets with third parties, do not use them to train models, and do not retain anonymised copies for our own use.
-
Yes — classification, regression, clustering, and basic NLP, in Python (scikit-learn, statsmodels, occasionally PyTorch for deep models). For pure deep-learning research projects (CNNs, transformers, custom architectures) we will scope the work case-by-case.
-
Yes. For journal manuscripts we deliver the analysis in the target journal's house style — figure dimensions, table format, statistical reporting conventions. Mention the journal at brief and we match it.
-
Thirty to sixty minutes with the analyst who did your work. We anticipate the three or four most likely questions on your methodology — what test, what alternatives, what assumptions, what limitations — and rehearse plain-English answers. Most scholars find it the highest-value part of the engagement.
§ Begin
Send the dataset. Receive the chapter.
SPSS, R, Python, STATA, AMOS, NVivo. Methodology brief before code. Cleaned data, interpreted results, reproducible scripts, and a viva-prep call before your defence.
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