Theme: Designing, Collecting, Analysing, and Interpreting Numerical Data
Duration: 1 week (self-paced)
Level: MA / MSc / PhD Preparation
Format: Fully self-contained lesson for independent study
🔷 6.1 Purpose of This Module
This module introduces the principles, tools, and processes of quantitative research. You will learn how to structure a measurable, statistically valid study—from hypothesis to survey design to data analysis.
By the end, you will be able to:
- Explain the purpose and logic of quantitative research
- Distinguish among common quantitative methods
- Design surveys, experiments, or correlational studies
- Understand how to use basic statistical tests
- Interpret findings in valid, meaningful ways
📖 6.2 What Is Quantitative Research?
Quantitative research collects and analyses numerical data to explain, predict, or describe relationships between variables. It is deductive, structured, and often used for testing hypotheses under positivist or post-positivist paradigms.
✅ Key Features:
| Feature | Explanation |
|---|---|
| Objective | Minimises researcher bias through standardisation |
| Structured | Follows a clear, pre-set design |
| Replicable | Can be repeated with similar results |
| Statistical | Uses numerical data and analytical tools |
| Hypothesis-driven | Begins with assumptions to be tested |
🧪 6.3 Core Quantitative Research Designs
| Design | Description | Example |
|---|---|---|
| Descriptive | Documents conditions, behaviours, or characteristics | “What % of MSc students report sleep problems?” |
| Correlational | Measures relationships between variables | “Is there a relationship between time spent on social media and GPA?” |
| Quasi-Experimental | Compares groups without full randomisation | “Do students from flipped classrooms perform better than those in lectures?” |
| True Experimental | Uses control/treatment groups with random assignment | “Does AI tutoring improve writing scores compared to standard tutoring?” |
| Cross-sectional | Data collected at a single time point | “Surveying stress levels during exam week” |
| Longitudinal | Repeated measurements over time | “Tracking motivation changes over a semester” |
📊 6.4 Types of Quantitative Data
| Data Type | Example | Level |
|---|---|---|
| Nominal (Categories) | Gender, major, religion | No order |
| Ordinal (Ranked) | Satisfaction: Low–Med–High | Order but no exact intervals |
| Interval | Temperature (°C), IQ score | Equal intervals, no true zero |
| Ratio | Age, income, test scores | Equal intervals + absolute zero |
🔍 6.5 Data Collection Techniques
✅ 1. Surveys
- Tools: Online forms (Google Forms, Qualtrics)
- Types of questions:
- Multiple choice
- Likert scales (1–5 agreement levels)
- Ranking or rating
Example Questions:
- “How many hours per day do you use your phone?”
- “On a scale of 1–5, how supported do you feel by your institution?”
- “Which of these platforms do you use regularly?” (select all that apply)
✅ 2. Structured Observation
- Use: To count behaviours or phenomena
- Example: Count how often teachers ask open vs closed questions in a classroom
✅ 3. Secondary Data Use
- Use: Re-analyse existing datasets
- Sources: Government data, organisational reports, academic archives
Example: Using World Bank datasets to analyse unemployment trends by gender
🔢 6.6 Introduction to Basic Statistical Analysis
| Type of Test | Purpose | Example |
|---|---|---|
| Descriptive Stats | Summarise data (mean, median, SD) | “Average hours of weekly screen time” |
| T-test | Compare two means | “Do males and females differ in anxiety levels?” |
| Chi-square | Association between two categories | “Does gender affect choice of study programme?” |
| Correlation (Pearson’s r) | Strength and direction of relationship | “Time on revision vs test score (r = .64)” |
| ANOVA | Compare more than two means | “Do three teaching methods yield different outcomes?” |
| Regression | Predict value of one variable from another | “Does motivation predict GPA?” |
✅ Use spreadsheets (Excel, Google Sheets) or software (SPSS, R) for these tests.
🛠 6.7 Self-Learning Task Set (Independent Exercises)
✍️ TASK 1: Choose a Quantitative Method for Your Topic
Using your research question:
- Is it best addressed through survey, experiment, or secondary data?
- What variables will you measure? (e.g., hours studied, test score)
- What kind of data will you collect? (ordinal, ratio, etc.)
Example:
- Topic: Smartphone use and sleep
- Method: Survey
- Variables: Hours on phone, hours of sleep
- Data Type: Ratio (both)
📊 TASK 2: Draft a 6–8 Question Survey
Create your own questionnaire with:
- 2 demographic items (e.g., age, field of study)
- 3 Likert scale items
- 1–2 multiple-choice or numerical questions
Example Items:
- Age: __
- Field of Study: [Dropdown]
- “I feel productive when using AI writing tools.” (1–5)
- “How many hours do you revise per week?”
- “Which apps do you use for study?” (Select all)
🧠 TASK 3: Hypothesis and Statistical Test Planning
Write:
- A null and alternative hypothesis
- The appropriate test to analyse the data
- The expected outcome
Example:
- H₀: There is no relationship between hours of social media use and sleep quality.
- H₁: More social media use is associated with lower sleep quality.
- Test: Pearson’s correlation
- Expected Outcome: Negative correlation (r = –0.4)
📋 TASK 4: Design a Data Collection Plan
| Element | Description |
|---|---|
| Population | Who will you study? (e.g., MSc students in education) |
| Sample size | How many? (minimum 30 recommended) |
| Sampling method | Random? Convenience? Snowball? |
| Data tool | What format? (Online form, in-person form, dataset) |
| Timeline | When and how long will data collection take? |
✅ Draft this into a mini-project plan.
🔍 6.8 Summary of Key Takeaways
- Quantitative research answers questions with numbers, not narratives.
- Common designs include surveys, experiments, and correlational studies.
- Your design must define variables, data types, and analysis methods.
- Descriptive and inferential statistics help you summarise and test your data.
- Every quantitative study must be structured, ethical, and replicable.
✅ End-of-Module Self-Evaluation Checklist
| Concept | Yes / No |
|---|---|
| I understand what quantitative research is and when to use it | ☐ |
| I selected a suitable quantitative method for my topic | ☐ |
| I designed a basic questionnaire with valid question types | ☐ |
| I wrote hypotheses and matched them to statistical tests | ☐ |
| I created a data collection and analysis plan | ☐ |