Dissertation statistical services and consulting
A qualified statistical consultant takes on the design, the analysis, and the write-up, and delivers work that is ready to defend.
Dissertation statistical services are done-for-you statistical consulting for a doctoral or master's project: a qualified statistical consultant sets the study design, writes the analysis plan, runs the analysis in SPSS or R, and delivers the written results with every methodological choice justified.
Why the methodology decisions come first
The expensive failures in a dissertation are decided before a single test is run. A study design that cannot answer the research question, an analysis plan that does not match the measurement level, or a sample size chosen without a power calculation all surface too late to fix cheaply. A statistical consultant settles those decisions at the start, then carries them through the analysis so the design and the results tell one consistent story.
This is the full engagement, from design through to a written results chapter. If your data is already collected and you only need the analysis and write-up, that is analysis of an existing dissertation dataset. For the doctoral-level version with advanced modelling, see statistical analysis support for a PhD.
What a consulting engagement covers
- 1
Study design review
Confirming that your design and measures can answer your research questions.
- 2
Analysis plan
Building a documented analysis plan and power analysis you can include in your proposal.
- 3
Analysis and reporting
Running the tests in SPSS or R and writing up the output in the format your department requires.
- 4
Defence documentation
A written justification for every methodological choice, so committee and viva questions are already answered.
How an engagement runs
The engagement starts from whatever you already have: a study design, a dataset, or a draft chapter. The assigned statistician reviews it, tells you what the data can and cannot support, and confirms the scope in writing before any work begins. Nothing is billed by the hour and the quote is fixed.
From there the consultant does the work. You receive the cleaned dataset, the analysis with assumption checks documented, the tables and figures formatted to your style guide, and the written results section. Each methodological choice comes with the reasoning behind it in writing, so when a committee asks why a particular test was used, the justification already exists.
The decisions that decide whether a dissertation passes
These are the choices that are contested, irreversible, or hard to defend after the fact. Getting them wrong is what sends a chapter back for revision, and they are settled first in every engagement.
- Choosing between competing analysis plans when more than one looks defensible.
- Justifying a sample size with a documented power calculation.
- Selecting a measurement instrument or scale that fits the construct you are studying.
- Deciding how to handle confounders so the design supports the claim you want to make.
- Wording the conclusion so it claims exactly what the test showed and no more.
The last point is where many projects overreach. Our guide to how to word a finding so it matches your design shows how to phrase a result without claiming more than the data can support.
Tell us where the project stands and what your deadline is. We will confirm the analysis it needs and send a fixed quote.
Frequently asked questions
- How to interpret SPSS output descriptive statistics?
Read the mean and median together to see where the data centres and whether it is skewed, the standard deviation for spread, and the minimum, maximum, and valid N for range and missing cases. In an engagement we produce and interpret that table for you, with each figure tied back to a research question.
- How to tell if SPSS results are significant?
Read the significance, or p, column against your chosen threshold, usually .05: a value below it means the result is statistically significant, a value above it means there is no evidence of an effect. Significance is not the same as importance, so it is read alongside the effect size.
- What does a p-value greater than 0.05 mean in SPSS?
It means the test found no statistically significant effect at the conventional threshold: the pattern in your sample is consistent with chance. That is a legitimate finding, not a failure, and how you word it matters so the conclusion matches what the test actually showed.
- What are the 4 components of power analysis?
Sample size, effect size, significance level, and statistical power are linked, so fixing any three determines the fourth. We run the calculation and write the justification for the sample you need, or document what your existing sample can realistically detect.