Statistics For Your Dissertation: Choose, Run & Write Up
 

Statistical test selection, results interpretation & Chapter 4 writing made simple
What you'll learn
Choose the correct statistical test (t-test, ANOVA, chi-square, correlation, regression) based on their research question and data type
Identify and correctly classify data types (nominal, ordinal, interval, ratio) and determine when to use parametric vs non-parametric tests
Interpret statistical results with confidence, including p-values, effect sizes, and key output tables
Run core statistical analyses in SPSS, including data setup, descriptives, t-tests, ANOVA, chi-square, correlation, and regression
Understand the difference between comparing groups and analysing relationships, and apply this to real research scenarios
Use a simple step-by-step workflow to move from raw data to a fully written Results (Chapter 4) section
Requirements
Basic understanding of your research topic or dissertation question
Basic computer skills (e.g. opening files, using Excel or similar software)
Access to a dataset (your own research data or a practice dataset provided in the course)
Optional: Access to SPSS (helpful for the practical module, but not required to understand the concepts)
Willingness to apply the step-by-step methods to your own research project
Description
Are you staring at your dissertation data thinking
• "Which statistical test do I use?"
• "What do these results actually mean?"
• "How do I turn this into a proper Chapter 4?"
You're not alone - and this course is designed to fix exactly that.
What this course will help you do
By the end of this course, you will be able to
• Choose the correct statistical test based on your research question and data
• Understand and interpret your results with confidence
• Avoid common mistakes that cost students marks
• Write clear, accurate, APA-style results for your dissertation
• Structure your Results (Chapter 4) in a way examiners expect
What makes this course different
This is not a theory-heavy statistics course.
You will not be overwhelmed with formulas or complex maths.
Instead, you'll learn a clear, practical system to
Choose → Run → Understand → Write
Everything is explained in plain English, using real examples you can apply directly to your own project.
What's inside the course
• A simple step-by-step workflow from raw data to Chapter 4
• A test selection system you can use for any research project
• Clear explanations of key tests
• t-tests
• ANOVA
• chi-square
• correlation
• regression
• How to interpret outputs (p-values, effect sizes, key statistics)
• How to write results using ready-to-use APA-style templates
• A full module on structuring your Results chapter
Downloadable tools included
You'll also get practical resources you can use alongside your analysis
• Test chooser flowchart
• Assumption checklists
• Data cleaning checklist
• Graph selection guide
• APA phrase bank
• Chapter 4 structure template
• Common mistakes checklist
Who this course is for
Students in psychology, health sciences, education, business, and other social science disciplines
Undergraduate and master's students working on a dissertation or research project involving quantitative data
Students who feel unsure about which statistical test to use for their research
Students who can run analyses (or have output) but struggle to understand what the results actually mean
Students who want to confidently write their Results (Chapter 4) in clear APA format
Students using (or planning to use) SPSS for data analysis