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Statistical Analysis Help
SPSS, R, STATA, NVivo and Python tutoring for dissertations and research — from £40/hour.
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Statistical Analysis Help — SPSS, R, STATA, NVivo & Python
Statistical Analysis Help: SPSS, R, STATA, NVivo & Python
For most students the data analysis chapter is the hardest part of a dissertation — not because the software is difficult, but because nobody teaches you which test to run and why. We provide one-to-one statistical analysis tutoring from £40 per hour covering SPSS, R and RStudio, STATA, NVivo, Python and Excel, for both quantitative and qualitative research. You run your own analysis; we make sure you understand every output well enough to defend it in a viva.
Choosing the right test
Almost every analysis problem is really a research-design problem. The test follows from three things: the level of measurement of your variables, the number of groups or time points, and whether your data meet the assumptions. Get those straight and the test chooses itself.
| Your question | Typical test |
|---|---|
| Difference between two independent groups | Independent-samples t-test, or Mann-Whitney U if assumptions fail |
| Difference before and after in the same people | Paired-samples t-test, or Wilcoxon signed-rank |
| Difference across three or more groups | One-way ANOVA, or Kruskal-Wallis |
| Relationship between two continuous variables | Pearson correlation, or Spearman for ranked or non-normal data |
| Predicting a continuous outcome | Multiple linear regression |
| Predicting a yes/no outcome | Binary logistic regression |
| Association between two categorical variables | Chi-square test of independence |
| Scale reliability | Cronbach's alpha |
| Underlying structure in many items | Exploratory factor analysis |
| Themes in interview data | Thematic analysis, usually in NVivo |
Assumptions are where marks are lost
Examiners rarely challenge whether you ran a t-test correctly. They challenge whether you were entitled to run one. Normality, homogeneity of variance, independence of observations, linearity, multicollinearity — each test rests on assumptions, and a dissertation that reports results without testing them has a visible hole in its methodology.
The other half of the problem is what to do when assumptions fail, which they often do with student sample sizes. Knowing that a non-parametric alternative exists, and being able to justify switching to it, turns a weakness into evidence of methodological awareness. That single move has lifted a great many merit dissertations into distinctions.
Reporting results properly
A common failure is pasting raw SPSS output into the dissertation. Examiners want results reported in the conventions of your discipline — usually APA style for social sciences — with the test statistic, degrees of freedom, p value and an effect size, followed by a sentence in plain English explaining what it means for your research question.
Effect size is the most commonly omitted element. A statistically significant result with a trivial effect size is not an important finding, and saying so demonstrates exactly the critical judgement that higher grade bands reward. Report Cohen's d, eta squared, or R squared alongside every significance test.
Qualitative analysis too
We also support qualitative work: coding frameworks, thematic analysis in NVivo, interview and survey design, systematic reviews, and research ethics applications. The principle is identical — the method has to follow from the research question, and you have to be able to justify each decision.
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How It Works, Plus Analysis Tips and Free Resources
How Statistical Analysis Tutoring Works
- Free 20-minute consultation. Send your research questions, your dataset structure and your deadline.
- Design review. We check that your variables and design can actually answer your questions — ideally before you collect data, when problems are still fixable.
- Analysis plan. A written plan listing each research question, the test that answers it, the assumptions to check and the fallback if they fail.
- Live software sessions. We run the analysis together on your screen, in your software, using your data.
- Output interpretation. Line by line, until you can explain every number without notes.
- Write-up support. Structuring the results chapter, reporting to APA conventions, and building clear tables and figures.
- Viva preparation. Practice defending your methodological choices.
Ten tips for a stronger analysis chapter
- Decide your tests before you collect data. The most expensive mistakes are made at the design stage.
- Do a power calculation. Knowing the sample size you need beats discovering afterwards that you never had a chance of detecting an effect.
- Clean and document. Record every exclusion and recode. Examiners ask, and "I can't remember" is a bad answer.
- Screen your data first. Missing values, outliers and impossible entries before any test.
- Test assumptions and report that you did. Even a sentence earns credit.
- Always report effect sizes alongside p values.
- Never say "proves". Statistics support or fail to support hypotheses; they do not prove them.
- Report non-significant results honestly. They are findings, not failures, and hiding them is misconduct.
- Build your tables in your word processor, not by screenshotting software output.
- Keep a syntax or script file. Reproducibility is a mark of quality, and it saves you when you need to rerun everything.
Five useful (and free) statistics resources
- Laerd Statistics — test-selection guides and SPSS walkthroughs; widely used and partially free.
- Your university's IT training and software licences. SPSS and NVivo are usually free through your institution, and many run free introductory workshops.
- R and RStudio — entirely free, with the official "R for Data Science" text available free online.
- G*Power — free software for power calculations and sample-size planning, and exactly what supervisors expect to see referenced.
- Your library's methods collection — Andy Field's SPSS text is on most reading lists and readable in a way statistics texts rarely are.
Pricing and booking
Statistical analysis tutoring starts at £40 per hour, with a 10-hour package at £280 covering most dissertation analyses end to end. Every engagement begins with a free 20-minute consultation and no payment details are required. Use the form on this page, call the UK office on +44 74648 84564 or Australia on +61 3 9016 2672, or message us on WhatsApp for a reply in under 15 minutes.
Statistical Analysis FAQs and Related Services
Statistical Analysis FAQs
How much does SPSS or statistics tutoring cost?
Statistical analysis tutoring starts at £40 per hour, with a 10-hour package at £280 that covers most dissertation analyses end to end. The first 20-minute consultation is free.
Which software do you support?
SPSS, R and RStudio, STATA, SAS, NVivo, Python and Excel, covering both quantitative testing and qualitative thematic analysis.
Do you run the analysis for me?
We run it with you, live, in your own software on your own data, so you can reproduce it and defend every decision in a viva. You keep the syntax or script file.
Which statistical test should I use?
It depends on your variables' level of measurement, the number of groups or time points, and whether assumptions are met. Two independent groups usually means an independent-samples t-test or Mann-Whitney U; three or more means ANOVA or Kruskal-Wallis; predicting a continuous outcome means multiple regression; a yes/no outcome means logistic regression.
What if my data fail the assumptions?
Non-parametric alternatives exist for most common tests, and justifying the switch demonstrates methodological awareness that higher grade bands reward. This is often the difference between a merit and a distinction.
Why do I need to report effect sizes?
A statistically significant result with a trivial effect size is not an important finding. Reporting Cohen's d, eta squared or R squared alongside p values shows the critical judgement examiners look for, and its omission is one of the most common avoidable weaknesses.
Can you help with qualitative analysis?
Yes — coding frameworks, thematic analysis in NVivo, interview and survey design, systematic reviews and research ethics applications.
Related services and guides
- Dissertation help UK — methodology and chapter support around your analysis.
- Statistics and SPSS assignment help
- Online tutoring UK and programming help
- Proofreading and editing — for the finished results chapter.
- Exam preparation — for quantitative methods exams.
- Free study guides on our blog
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