SRS Course 02 · Data Analysis

Data
Analysis

Turn data into evidence you can understand, explain and defend.
A practical research-focused course that takes you from raw, messy data to meaningful analysis, visualisation and defensible research findings.
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Course Resources
From data → evidence
Raw data Analysis Evidence
p = .62
Statistical result
↑
Interpretation
The SRS Approach

Don't just calculate.
Learn to understand.

Data analysis is not simply about knowing statistical formulas or remembering which button to click in SPSS.
Good analysis begins with a research question. You need to know what your data represents, how it was collected, whether it is trustworthy, what method is appropriate and what your results actually mean.
“Why am I doing this analysis?”

comes before

“Which button do I click?”
What You'll Learn

From raw data to research findings.

01

Understand

Variables, observations, measurement and the nature of your data.
02

Prepare

Clean, organise, validate and prepare real research datasets.
03

Analyse

Apply descriptive and inferential methods appropriately.
04

Interpret

Turn statistical output into meaningful research findings.
Course Curriculum

Ten modules.
One analytical journey.

The course moves progressively from understanding data to preparing it, analysing it, choosing appropriate methods and finally communicating what the results mean.
  • What is data?
  • Variables
  • Observations
  • Cases
  • Quantitative vs qualitative data
  • Primary vs secondary data
  • Levels of measurement
  • Structured vs unstructured data
Research Exercise
Identify variable types in a sample research dataset.
  • Data entry
  • Coding
  • Missing data
  • Duplicate records
  • Outliers
  • Data validation
  • Data cleaning
  • Data organisation
Practical Project
Clean a deliberately messy research dataset and document the decisions you made.
  • Mean
  • Median
  • Mode
  • Range
  • Variance
  • Standard deviation
  • Percentiles
  • Frequency distributions
Interpretation Focus
What does a standard deviation actually tell you about your research participants?
  • Bar charts
  • Histograms
  • Box plots
  • Scatter plots
  • Line graphs
  • Pie charts — when not to use them
  • Tables
  • Academic figures
Assignment
Turn a raw dataset into three publication-quality visualisations.
  • Probability basics
  • Sampling distributions
  • Standard error
  • Confidence intervals
  • Statistical inference
Learning Focus
Understand uncertainty without getting buried in unnecessary mathematics.
  • Null hypothesis
  • Alternative hypothesis
  • p-value
  • Statistical significance
  • Type I error
  • Type II error
  • Statistical power
  • Effect size
Critical Concept
Statistical significance does not automatically mean practical significance.
  • Chi-square
  • t-tests
  • ANOVA
  • Correlation
  • Regression
  • Choosing an appropriate test
Decision-Making Focus
Learn to choose a statistical method from the research question and data — rather than memorising tests.
  • Transcription
  • Coding
  • Open coding
  • Categories
  • Themes
  • Thematic analysis
  • Interpretation
  • Researcher reflexivity
Hands-on Exercise
Code a short interview transcript and compare your coding against an instructor example.
  • Excel
  • Google Sheets
  • SPSS
  • Jamovi
  • R
  • Python
  • Software-independent analytical thinking
SRS Principle
Learn why you are performing an analysis before learning which software button performs it.
  • What happened?
  • How large was the effect?
  • How certain are we?
  • What does the result mean?
  • What does it not mean?
  • Does it answer the research question?
The Real Payoff
Learn to move from statistical output to defensible research interpretation.
Think Before You Test

Statistical tests are
decisions — not vocabulary.

Instead of memorising dozens of statistical tests, learners develop a structured way of thinking about research questions, variables and comparisons.
Two groups?
Compare the means of two independent groups.
→
Independent t-test
An introductory method for comparing two group means.
Three or more groups?
Compare several group means.
→
ANOVA
Examine whether group means differ.
Relationship between numerical variables?
Examine association.
→
Correlation
Examine the direction and strength of association.
Prediction?
Estimate an outcome from one or more predictors.
→
Regression
Model relationships and make predictions.
Tools Without Tool Dependence

Understand the analysis first.
Then choose the tool.

SRS introduces software progressively so learners develop analytical understanding rather than becoming dependent on menus and buttons.
01

Spreadsheet

Excel / Google Sheets
02

Statistics

SPSS / Jamovi
03

Advanced

R
04

Programming

Python
Module 10 · The Real Skill

A statistical result
is not a conclusion.

01What happened?
02How large was the effect?
03How certain are we?
04What does it mean?
05What does it not mean?
06Does it answer the research question?
Example
p < 0.05
This is a statistical result. It is not, by itself, the research conclusion.
Learners will practise moving from numerical output to appropriately qualified research interpretation.
Beyond Numbers

Data isn't always
numerical.

Research in humanities and social sciences often involves interviews, observations, field notes and textual material. Learners therefore need a framework for analysing qualitative evidence as well.
01Transcription
Prepare the interview or textual data.
02Open Coding
Identify meaningful segments.
03Categories
Group related codes.
04Themes
Identify broader patterns.
05Interpretation
Connect patterns to the research question.
06Reflexivity
Consider the researcher’s role in interpretation.
The Data Analysis Capstone

Don't finish with a quiz.
Finish with analysis.

Learners receive a research dataset and complete the full analytical workflow — from cleaning the data to writing a short analytical report.
01

Clean

Prepare the dataset for analysis.
02

Describe

Summarise the data appropriately.
03

Visualise

Create meaningful academic figures.
04

Select

Choose appropriate analytical methods.
05

Analyse

Perform the selected analysis.
06

Interpret

Explain what the results mean.
07

Write

Convert results into research findings.
08

Report

Produce a concise analytical report.
Learning Resources

Resources that continue beyond the classroom.

Build a practical research toolkit while you learn.
Free Resources

Start analysing with confidence.

  • Data Analysis Roadmap
  • Statistical Test Decision Tree
  • Data Cleaning Checklist
  • Basic Statistics Cheat Sheet
Get Free Resources →
SRS Premium Resource

SRS Data Analysis Lab Pack

  • Research datasets
  • Answer datasets
  • Excel templates
  • SPSS practice files
  • Interpretation worksheets
  • Visualisation templates
  • Statistical test selection guide
  • Analysis report template
Explore the Lab Pack →
Data Analysis · SRS

Don't just produce numbers.
Learn to produce evidence.

Build the analytical skills required to clean data, choose appropriate methods, interpret results and communicate research findings with confidence.
Enrol in Data Analysis →