Dissertation statistical consulting
One-to-one guidance on study design, analysis plans, and interpreting your own output, so you stay in control of the methodology.
Dissertation statistical consulting is advisory, one-to-one guidance on the decisions behind your analysis: structuring the study design, building a defensible analysis plan, and interpreting your own output. It keeps you in control of the methodology while making sure the choices you commit to will hold up.
When advice matters more than execution
Some researchers do not need someone to run the analysis. They need a methodologist to confirm that the study design can answer the question, that the analysis plan is sound before data collection starts, or that they are reading their SPSS output correctly. Getting those decisions right early prevents the costly rework of discovering, after collection, that the design cannot support the intended test.
Consulting is the advisory counterpart to hands-on dissertation statistics help. If you would rather have the analysis carried out for you once the plan is set, see dissertation data analysis help.
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
Output interpretation
Working through your own SPSS or R output so you understand and can defend each result.
- 4
Defence readiness
Preparing for methodological questions from your committee or in your viva.
What a consulting session looks like in practice
A working session starts from what you already have. You bring a study design, a dataset, or the output from an analysis you have already run, and the statistician reviews it live in writing. Rather than handing back a verdict, the methodologist names the issues in order of importance and proposes the options that genuinely fit your question and your data.
The value is in the reasoning, not just the answer. For each decision you see the trade-offs, the assumption it rests on, and the wording you would use to justify it. You leave with documented decisions you can defend in your proposal or your viva, not a black box you have to take on trust. Everything stays in writing, so the record of why you chose each method is yours to keep and quote later.
The decisions consulting is best for
Consulting earns its place when a choice is contested, irreversible, or hard to defend after the fact. These are the points where a second methodologist changes the outcome most.
- 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.
- Interpreting your own output so the conclusion matches what the test actually showed.
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 you are in the project, and we will set up a consultation focused on the decisions you need to make.
Request a quoteFrequently 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. A consulting session walks through your own table so each figure connects 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. A consulting session uses them to justify the sample you need, or to show what your existing sample can realistically detect.