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.
Descriptive
- Frequencies
- Cross-tabulations
- Central tendency
- Distribution diagnostics
- Outlier & missingness reports
Group differences
- Independent / paired t-tests
- One-way ANOVA
- Two-way & repeated-measures ANOVA
- ANCOVA / MANCOVA
- Mann-Whitney · Kruskal-Wallis · Wilcoxon
Association & regression
- Pearson · Spearman · Kendall
- Linear & multiple regression
- Logistic · multinomial · ordinal
- Hierarchical & stepwise models
- Moderation · mediation (PROCESS)
Multivariate & SEM
- EFA · CFA
- Reliability (Cronbach's α, composite)
- Path analysis · SEM (AMOS / Smart-PLS)
- Cluster & discriminant analysis
- Multilevel · panel models
Time-series & econometrics
- ARIMA · SARIMA
- Cointegration · ECM
- GARCH · volatility
- Granger causality
- Difference-in-differences
Qualitative
- Thematic coding (NVivo)
- Framework analysis
- Content analysis
- Inter-rater reliability (Cohen's κ)
- Discourse · narrative 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
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The process is straightforward. Once you submit your dataset and requirements, our expert will connect with you on a call to understand your research objectives, clarify doubts, and agree on the approach. Work begins immediately after the discussion. You will receive the complete analysis along with output files, charts, and interpretation.
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Yes, absolutely. Before any work begins, our expert will connect with you to understand your exact needs, discuss the best approach, and answer any questions you have. This ensures the analysis is aligned with your research goals from the start.
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The timeline depends on the complexity of your data and the type of analysis required. It will be clearly communicated to you after the initial discussion with our expert, so you know exactly when to expect the final output.
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Pricing starts at ₹20,000 per analysis. The final cost depends on the complexity, size of the dataset, and specific requirements. Our expert will share a clear quotation after the initial discussion.
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Both options are available. Data can be collected through surveys and questionnaires, or generated using AI-based methods — it is completely your choice. Our expert will explain both approaches during the initial call and help you decide what best suits your research.
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Keep your data file and a clear note of your requirements ready before the call. If you have a research question, specific variables to test, or guidelines from your institution, share those too. The more context you provide, the more productive the discussion will be.
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We use industry-standard tools including SPSS, Excel, R, Python, and other advanced statistical software. The choice of tool depends on your project requirements and is decided during the initial discussion.
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Yes, absolutely. You do not need any prior knowledge of statistics. Our experts handle the entire analysis and explain the results in plain, easy-to-understand language. Many of our clients come with no statistical background and leave with a clear understanding of their results.
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Yes. We handle complete data cleaning and structuring before the analysis begins. Raw, messy, or incomplete datasets are common and our experts are experienced in organising them properly to ensure accurate results.
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Data cleaning is the process of identifying and correcting errors, handling missing values, removing duplicates, and preparing your raw data for analysis. Yes, we include data cleaning as part of our service so the analysis is performed on accurate, reliable data.
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That is completely fine. Our experts will review your data and research objectives and select the most appropriate statistical tests for your study. You do not need to specify the test — we handle that as part of the service.
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No, we can handle datasets of all sizes — from small research samples to large complex datasets. The approach and pricing may vary depending on size and complexity, which will be communicated to you upfront.
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Yes. We assist with report writing, formatting, and structuring your analysis as per academic or journal requirements. The final deliverable can be formatted to match your institution's guidelines.
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You need to share your dataset, your research objectives, and any specific requirements or guidelines. Our expert will guide you through the rest during the initial discussion call.
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Yes. You can reach out at any point during the process if you have questions, want an update, or need clarification. Your project manager is available to assist you throughout.
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Yes. You will receive the complete SPSS file along with all output files so you have full transparency and can use them for your academic or research submission.
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Yes. We include clear charts, graphs, and visual representations in the final output to make your data easy to understand and present.
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Yes. Every result comes with a detailed interpretation so you can clearly understand what the data indicates and how to use it in your research or thesis.
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Yes. We provide a detailed summary of findings with clear explanations so you can easily understand the results and apply them in your project, thesis, or presentation.
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Yes. We perform hypothesis testing as required and include properly formatted results with explanations in the final report.
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Yes. We can provide purely descriptive analysis if that is all you need, or combine it with advanced statistical analysis. Just let us know your requirements and we will tailor the output accordingly.
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Yes. The final deliverable is properly formatted and submission-ready, with analysis, interpretation, tables, and graphs all included and structured as per your requirements.
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Yes. We provide revisions to ensure the final output meets your expectations and requirements. Reach out to your project manager with your specific feedback and we will address it.
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Absolutely. Your data is completely confidential and secure. We follow strict privacy standards and never share your information, dataset, or research details with any third party.
§ 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.