Where dissertation data analysis usually goes wrong
Most quantitative dissertations do not fail on the statistics themselves. They stall earlier, when the research questions, the variables and the planned tests stop lining up. A hypothesis about prediction gets tested with a correlation; a five-point Likert item gets treated as a continuous outcome without comment; a repeated-measures design gets analysed as if every observation were independent.
Other common sticking points we see:
- A sample smaller than planned, with no power analysis to show what the study can still detect
- Survey scales that were never checked for reliability before being summed into totals
- Missing data handled by quietly deleting cases, which can bias results when data are not missing completely at random
- Multiple regression output reported in full without anyone checking multicollinearity, residual normality or influential points
- Dozens of exploratory tests run without any correction for multiple comparisons
Catching these early saves weeks of rewriting later, which is why we start with your proposal or methodology chapter rather than the output.
How we work with you on the analysis
We begin by reading your research questions, hypotheses and the methods section your committee approved. From there the statistician drafts an analysis plan in plain English and agrees it with you before touching the data. If your plan is already sound, we say so and move on; if it needs revising, we explain the reasons so you can discuss them with your supervisor.
The analysis itself is done in your software of choice and in a way you can follow. That can mean a live screen-share where you run the commands yourself, or a fully commented script that you rerun and check. Either way, nothing appears in your chapter that you could not reproduce on your own laptop. If your design is mostly about methodology choices, our methodology chapter help pairs well with this service.
Writing up data analysis for your dissertation
A results chapter reports findings; it does not interpret them at length or repeat every table in prose. A good write-up states the test, the key statistic with degrees of freedom, the exact p-value and an effect size with a confidence interval, then moves on. For example: Participants in the intervention group reported lower anxiety (M = 12.4, SD = 3.1) than controls (M = 15.0, SD = 3.6), t(118) = 4.24, p < .001, d = 0.77, 95% CI [0.40, 1.14].
We comment on your draft for accuracy, structure and APA 7 conventions: italicised statistical symbols, no leading zero on values that cannot exceed 1, and tables with clear notes. Our guides to regression analysis and reporting t-tests show worked examples you can model your own paragraphs on.
What to send and how long it takes
For an accurate quote, share whatever you have of the following:
- Your research questions or hypotheses, and the approved methods section or proposal
- The dataset (SPSS .sav, Stata .dta, Excel, CSV or similar) with a codebook or questionnaire
- Any feedback from your supervisor, advisor or committee about the analysis
- Your submission or defense date and university guidance on outside support
A focused analysis on a clean dataset with two or three research questions typically takes one to two weeks, including time for your questions. Large panel datasets, multilevel models or extensive data cleaning take longer, and we tell you so up front in your free quote.
You remain the analyst and the author
Your dissertation is assessed as evidence that you can carry out research independently. We take that seriously. You make the final decisions, you write the chapter, and you should be able to justify every choice to an examiner without us in the room. Many universities explicitly allow statistical advice from a consultant, sometimes with acknowledgement; some limit it. We ask you to check your programme's rules and we adjust our role to match.
What you get
- A written analysis plan that maps each research question or hypothesis to a specific test
- Cleaned, documented dataset with a codebook and a log of every transformation
- Syntax, do-file or R script that reproduces every result in your chapter
- Assumption checks with diagnostics and a note on what to do if one fails
- Draft APA-style tables and figures, plus comments on the results chapter you write
- A viva or defense prep session on the analysis questions examiners tend to ask
What we don't do
- You write the results and discussion chapters; we give detailed feedback and explain the statistics behind them
- We do not collect data for you or alter responses to reach significance
- Where your university restricts outside help with analysis, we work within those rules, which may mean coaching only
How it works
Describe your project
Level, deadline and what you need help with.
Agree a written scope
What's included, the price and the timeline.
Work in stages
Check-ins so you can steer the work.
Review and ask
Explanations and follow-up questions included.