Statistical Analysis
Support with selecting methods, analysing your data and explaining the findings, with outputs you can understand and review.
This is for you if…
You have data, or you are about to collect it, and the analysis chapter is where the dissertation stands or falls. You may be:
A quantitative dissertation student where Chapters 4 and 5 need the analysis written up properly.
A researcher with raw data but no clear analysis plan, and the deadline is closing.
A researcher with raw data but no clear analysis plan, and the deadline is closing.
Tools we work in
We run analysis in the software your marker, supervisor, or external examiner can open. You get the syntax file, the output, and the cleaned data. Everything we run can be re-run.
Tools we work in
SPSS
Descriptive, inferential, regression, ANOVA, factor analysis, reliability
R
Advanced stats, structural equation modelling, machine learning, replication code
Python
Data wrangling, ML, NLP, large datasets, reproducible Jupyter notebooks
NVivo
Qualitative coding, thematic analysis, framework analysis, mixed-methods
STATA
Econometrics, panel data, causal inference, instrumental variables
EViews
Econometric forecasting, time series analysis, panel data models, ARIMA
Excel
Smaller datasets, descriptive analysis, pivot tables, exploratory work
Power BI
Data visualisation, dashboards, business intelligence reporting
Tableau
Interactive dashboards, geographic analysis, visual storytelling
Your supervisor requires software not on this list? Send us the brief. We tell you upfront whether we have an analyst for it.
Analysis types we deliver
Pick the analysis that fits your research question, your data type, and your supervisor's expectations. We use the right test, not the trendy one.
Descriptive statistics
Means, medians, frequencies, distributions. The baseline for almost every quantitative dissertation.
Inferential tests
T-tests, ANOVA, chi-square, correlation. Hypothesis testing for group differences and relationships.
Regression analysis
Linear, multiple, logistic, hierarchical. For predicting outcomes from predictors and identifying significant variables.
Factor analysis
EFA and CFA for survey instruments. Construct validation. Dimension reduction.
Structural Equation Modelling
SEM, path analysis, mediation, moderation. For testing complex theoretical relationships.
Time series & longitudinal
Repeated measures, growth curves, panel data, ARIMA. For data with a time dimension.
Qualitative coding
Thematic analysis (Braun & Clarke), framework analysis, IPA, grounded theory. Coded in NVivo or by hand.
Mixed methods integration
Joint displays, meta-inference, triangulation. Bringing quant and qual together coherently.
Real analysis. Not generated. Reproducible.
ChatGPT can fabricate statistics that look real. AI can hallucinate p-values, citations, and effect sizes. Real analysis leaves a trail. Here is what you actually receive.
# Model: Y ~ X1 + X2 + X3 > summary(model_full) Call: lm(formula = engagement ~ age + training_hours + autonomy) Residuals: Min 1Q Median 3Q Max -2.1422 -0.5234 0.0921 0.5102 1.8732 Coefficients: Estimate Std. Error t value (Intercept) 2.41273 0.31204 7.73 age -0.01892 0.00451 -4.20 training_hours 0.34521 0.04832 7.14 autonomy 0.21043 0.06127 3.43 Multiple R-squared: 0.4267 F-statistic: 48.31 on 3 and 196 DF p-value: < 2.2e-16
The full reproducibility package
Every analysis we deliver comes with the files that prove it is real. Anyone (your supervisor, your external examiner, your viva panel) can re-run the entire analysis on their own machine.
- The cleaned dataset (CSV or SPSS .sav)
- The syntax or code file (R script, Python notebook, SPSS syntax)
- The full output file (PDF or HTML)
- Written interpretation in academic prose
- Assumption checks for every test
- Effect sizes alongside p-values
Our commitment to your work
Good analysis starts with the research question and the data. We agree the analytical scope before work begins.
Our commitment is to careful, clearly scoped support at each agreed stage.
Our commitment
- Methods selected around your question, data type and study design.
- Data limitations and relevant assumptions made clear.
- Outputs and interpretation explained in language you can follow.
- Required files, tables and documentation agreed in the project scope.
- Additional analyses and changes to the brief discussed before proceeding.
We do not promise significant results. Findings depend on your data, and limitations must be reported honestly.
THE PERSON BEHIND DDE
Meet Taiwo Oladokun
Founder & Creative Director
I founded Delight Data Exploration to support students and professionals with their academic and career goals. Bring us your topic, your brief and where you are stuck, so we can discuss the support that fits.
About DDE, our team and advisorsCommon questions
Which files will I receive?
Agree the package at the start, including any cleaned data, syntax, outputs, tables and written interpretation that your project requires.
What if my results are not significant?
Non-significant results can still be meaningful. Data must not be altered or invented to produce a desired result. The interpretation should explain what the evidence supports.
How do we agree the price and scope?
Share your requirements and deadline. Confirm the deliverables, price, payment schedule and any exclusions before work starts.
What if I need changes?
Check the included revision terms and timeframe in your agreement. A substantial change of direction may need a new scope and quote.
What should I check before starting?
For academic or application work, check the institution’s rules on authorship, outside support and disclosure. Share only information you are permitted to use, without unnecessary personal data.
Ready to discuss your analysis?
Share your research questions, data format and deadline.