Dissertation data analysis help
You have collected your data. We clean it, run the correct tests, and turn it into a defensible results chapter.
Dissertation data analysis help takes the data you have already collected and turns it into a defensible results chapter: cleaning the dataset, checking assumptions, running the correct statistical tests, and reporting the findings so they answer your research questions. It is the analysis-and-write-up stage, for when collection is done and the numbers need to hold up.
From a raw dataset to a chapter that holds up
Reaching the analysis stage with a full dataset is a milestone, but a spreadsheet of responses is not yet a results chapter. Before any test is run, the data has to be cleaned: missing values handled, variables coded correctly, and out-of-range entries checked. Skipping that step is the most common reason a dissertation analysis produces results that fall apart under scrutiny.
This service is the bridge from collected data to a written chapter. It is narrower than full dissertation statistics help, which also covers design and planning, and narrower than dissertation statistical consulting, which advises on approach. Here the focus is execution: your data, analysed and reported.
What the analysis stage covers
- 1
Data cleaning and screening
Handling missing data, recoding variables, and screening for errors and outliers before analysis.
- 2
Assumption testing
Checking normality, variance, and independence so the chosen tests are valid for your data.
- 3
Running the analysis
Executing the correct statistical tests in SPSS or R, with output you can reproduce.
- 4
Writing the results chapter
Tables, figures, and a written interpretation of effect sizes and significance in your required style.
For doctoral datasets that need advanced modelling rather than standard tests, see PhD statistics help.
The order we work a dataset
A clean analysis follows a fixed sequence, and most problems trace back to a step that was rushed or skipped. We import the raw data and check it against what you expected to collect, then move through screening, testing, and reporting in a deliberate order. Working a dataset out of sequence, for example running a regression before screening for outliers, is how a result ends up resting on an entry that should never have been there.
- Import the raw data and verify it matches the collection you expected.
- Recode and label variables so every field means what the analysis assumes.
- Screen for and handle missing values with a documented rule rather than a silent deletion.
- Check for out-of-range entries and outliers that would distort the result.
- Test the assumptions the planned tests depend on.
- Run the planned statistical tests.
- Build the results tables and figures.
- Write the interpretation that ties each number to a question.
The assumption stage is where a defensible analysis is won or lost; our guide to checking normality and other assumptions shows what we test and why. Once the numbers are settled, the write-up follows the conventions in reporting the results in APA style.
What you receive
The point of the work is a results chapter you can defend, so the deliverables are built for transparency rather than a single number dropped in an email. Everything is handed over in a form your committee can re-run, because a result nobody can reproduce is a result a viva can unpick.
- The cleaned dataset, ready to re-analyse.
- The full analysis output from every test that was run.
- The syntax or code so the work is reproducible.
- Correctly formatted results tables in your required style.
- A written results section that interprets the findings.
- A short plain-language summary you can speak to under questioning.
If you are still deciding which analysis your data calls for, our guide to how the correct test is chosen walks through how the research question and variable types point to one method over another, and our free statistics calculators let you plan the sample, check a test, and summarise your data before you hand it over.
Send your dataset and research questions, and we will confirm the analysis and turnaround before any work starts.
Request a quoteFrequently asked questions
- How to do data analysis for dissertation?
Work in a fixed order: import and check the raw data, recode and label variables, screen and handle missing values and outliers, test the assumptions the planned tests depend on, run the analysis, then build the tables and write the interpretation. Most problems trace back to a step that was rushed or skipped.
- How do I analyse data using SPSS?
After cleaning and coding your dataset, you select the procedure that matches your question, descriptives, a t-test, ANOVA, correlation, or regression, run it, then read the output tables against your hypotheses. The analysis is the same logic in R; the menu simply differs.
- How to write results chapter in dissertation?
Lead with the descriptive statistics so the reader sees the sample, then report each inferential test with its statistic, p-value, and effect size, and interpret what it means for the hypothesis. Every number should trace back to a research question, with tables and figures formatted in your required style.
- How long should a results section be in a 10,000 word dissertation?
There is no fixed rule, but the results commonly run to roughly a fifth to a quarter of the word count, with the analysis and discussion together forming the largest part. Length follows the number of research questions and analyses, not a target word count.