Statistical Analysis (SPSS, R, AMOS, SmartPLS)
The right tests, assumptions checked, results interpreted — and syntax you can rerun and defend.
By the time most scholars reach the analysis, the data have taken a year or more to collect, and the pressure is to “get the results” quickly. That is where the common problems start: a t-test run on data that clearly aren’t suitable for it, a regression reported without checking whether its assumptions hold, a structural model in AMOS whose fit indices were never reported, or a table of p-values with no effect sizes and no plain-language interpretation. Examiners notice all of these, and the viva is where they ask about them.
We help you run the analysis your objectives and data actually call for, in SPSS, R, AMOS or SmartPLS: choosing tests, screening and preparing the data, checking assumptions, running the models, and interpreting the output in words you can explain. Everything is delivered with the syntax or script that produced it, so any number in your thesis can be rerun by you, your supervisor or an examiner and still come out the same.
The analysis reports what the data show. If a hypothesis isn’t supported, that is a finding, and we help you report and discuss it properly. The data stay exactly as collected, and each test follows the analysis plan rather than a search for p < 0.05.
Scope
What’s included
Each stage can be bought separately. If your analysis is done and you just want it checked before submission, the output review on its own is often enough.
Analysis plan and test selection
Matching each objective and hypothesis to an appropriate test or model, based on your design, variable types, measurement levels and sample size, and written down before the analysis is run.
Data screening and preparation
Coding and labelling, missing-data patterns and handling, outlier checks, reverse-scored items, and composite scores, with every step recorded in syntax so it is transparent and reversible.
Descriptive, inferential and multivariate analysis
From descriptives, reliability and group comparisons to correlation, regression, ANOVA and MANOVA, factor analysis, and non-parametric alternatives where the data require them.
Structural equation modelling
CB-SEM in AMOS or R (lavaan), or PLS-SEM in SmartPLS: measurement model assessment, validity and reliability, structural model testing, mediation and moderation, with the fit or quality criteria your field expects.
Interpretation and results write-up support
Tables and figures in APA or your department’s style, effect sizes and confidence intervals alongside p-values, and a plain-language explanation of each result that you can use to write your chapter.
Output review
Already run your analysis? We check the choice of tests, assumption checks, reporting and interpretation against your output files and flag anything an examiner is likely to question.
Quality
How quality is verified
These are the checks every piece of work goes through before it reaches you.
Syntax or script for every result
You receive the SPSS syntax, R script, AMOS model file or SmartPLS project behind every table. Running it on your data file reproduces the reported numbers exactly.
Assumptions checked and reported
Normality, homogeneity of variance, linearity, multicollinearity, independence and model-specific requirements are tested and documented, with the remedy used when an assumption fails.
Effect sizes alongside p-values
Every inferential result is reported with an appropriate effect size and, where possible, a confidence interval, so the reader can judge practical importance and not just significance.
Data changes logged
Any recoding, exclusion or imputation is recorded with the reason in a data log. Your original data file is always kept unchanged.
Second-analyst check
A second analyst reviews test choice, output and interpretation before delivery, asking the questions an examiner would ask before the examiner does.
Time
Realistic timelines
Analysis time depends less on the software than on the state of the data and the number of objectives. These ranges assume clean, complete data arrive at the start.
| Stage | Typical time | What it depends on |
|---|---|---|
| Analysis plan and test selection | 2–4 working days | Number of objectives and hypotheses |
| Data screening and preparation | 2–7 working days | Sample size, missing data and how the file was coded |
| Descriptive and inferential analysis | 1–2 weeks | Number of variables and tests |
| Structural equation modelling | 1–3 weeks | Model complexity, mediation or moderation, and measurement issues |
| Interpretation and write-up support | 1–2 weeks | Number of tables and your review of drafts |
| Output review | 3–5 working days | Size of the output and number of analyses |
Software
SPSS, R, AMOS or SmartPLS — which one?
The software matters less than the analysis, but there are real differences worth knowing before you start.
SPSS
Menu-driven and widely taught in Indian universities, which makes it easy for supervisors to follow. It covers most of what a thesis needs. Because menu clicks aren’t recorded automatically, we always work through, and deliver, the syntax, which is what makes your analysis repeatable.
R
Free, script-based and extremely flexible, with packages for almost every method (lavaan for SEM, lme4 for multilevel models, psych for scale analysis). A script is reproducible by design. It has a steeper learning curve, so we comment scripts line by line.
AMOS and SmartPLS
Both are used for structural equation modelling, but for different purposes. AMOS runs covariance-based SEM, suited to confirming well-established theory, and reports fit indices such as CFI, TLI and RMSEA. SmartPLS runs partial least squares SEM, often chosen for prediction-oriented or exploratory models and for formative constructs; it assesses the model with criteria such as composite reliability, AVE and HTMT rather than global fit. Choosing between them should follow from your research aim. Examiners do ask why you chose one.
What we need from you
Your data file (Excel, CSV, SPSS or similar), your questionnaire or codebook, your objectives and hypotheses, and any analysis your supervisor has already asked for. If the design itself still needs work (sampling, instruments, sample size), that is better handled first through research methodology consulting. For interview and focus-group data, see qualitative and mixed-methods analysis.
Pricing
How pricing works
We quote after seeing your data file, your objectives and your instrument, because a single-survey descriptive study and a multi-group SEM with mediation are very different jobs. The quote is fixed for the stages you choose and agreed in writing before work begins.
If the data turn out to need substantially more preparation than we expected from the sample you sent, we tell you before continuing and agree any change with you. The price changes only with your agreement.
What the quote depends on
- Number of objectives, hypotheses and variables
- Sample size and the state of the data file
- Techniques required, for example SEM, multilevel or longitudinal models
- Software you need the deliverables in
- Whether you need interpretation and write-up support
Every quote includes
- A written scope listing exactly what will be delivered
- A fixed price agreed before any payment
- Payment in stages, tied to delivery
- Revision rounds stated in the scope
Process
How it works
Free consultation
A call or message with a specialist in your subject. We review your material and say plainly whether we are the right fit.
Written scope and quote
You get a written scope, a timeline and a fixed price. Work and payment begin once you agree to it.
Staged delivery
Work arrives in stages you review, each with a similarity report where text is involved. You pay stage by stage.
Revisions and hand-over
Revision rounds are included. At the end you get the final files, and an explanation of anything you’ll need to defend.
Keep reading
Guides and related services
Choosing the right statistical test
Match your question and data type to the right test, including non-parametric options.
Research Methodology Consulting
Design, sampling, instruments and analysis plan checked before you collect data, while changes are still easy.
Qualitative and Mixed-Methods Data Analysis
Coding, thematic analysis and mixed-methods integration, with an audit trail your examiner can follow.
APA 7 formatting guide for theses
In-text citations, reference examples, headings, tables and figures — the APA 7 rules scholars get wrong.
FAQ
Frequently asked questions
Can you just make my results significant?
Results reflect the data as collected. Changing data, removing respondents without a pre-stated reason, or running tests until one “works” counts as falsification or p-hacking. What we do is check that you are using the right tests (sometimes a genuine error in the analysis is hiding a real effect) and help you report a non-significant result properly.
Will you do the analysis and I just put it in my thesis?
We run and explain the analysis with you, and you receive every script so you can rerun it yourself. The aim is that you can explain every result; the examiner will ask why you chose each test, and you need to be able to answer. We include a walk-through session so you can.
My data aren’t normal. Is that a problem for my study?
Usually not. Many tests are robust to moderate non-normality with reasonable sample sizes, and where they aren’t, there are non-parametric alternatives, transformations or bootstrapped estimates. What matters is that you checked, chose sensibly and reported what you did.
My supervisor wants SPSS, but I’ve heard R is better. Which should I use?
Use what your supervisor and examiners can follow unless there is a method SPSS can’t do. Both give identical answers for standard tests. We can deliver in either, or both.
Can you collect the data or fill in responses for me?
Data collection stays with you, since generating or completing responses would be fabrication. We can help you design the instrument, plan the sample and prepare the data you collect yourself.
What if the examiner asks about my analysis at the viva?
You will have the scripts, a data log and written reasoning for every test choice, and you will have walked through them with us. If you want extra preparation, we can run practice questions on your methods and results.
Is my data kept confidential?
Yes. Your data file is accessed only by the analysts working on it, used only for your project, and deleted on request after hand-over. Please remove names and other direct identifiers before sending if you can; we can advise how. We will sign an NDA if you want one.
Talk it through with a specialist for free
Send us where you are and what you need. We’ll reply within one working day with clear advice, a written scope and a fixed quote. No obligation.
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