Developing Research Questions and Collecting Data (Grade 12 NSC Matric Mathematical Literacy): Revision Notes
Developing Research Questions and Collecting Data
Understanding data handling
Data refers to a collection of numbers, facts, and information that researchers use to study different topics. Think of data as the building blocks that help us understand patterns and make informed decisions.
The process of working with data involves several connected steps that work together to help us gather meaningful information.
Each step in this process is interconnected - weakness in one area can affect the quality of your entire research project. Understanding how these steps work together is essential for producing reliable results.
Each step in this process is important for conducting reliable research that produces trustworthy results.
Developing research questions
What makes a good research question?
Before starting any research project, you need to create a clear research question that states exactly what you want to find out. A well-written research question acts like a roadmap that guides your entire research process and helps you decide what information to collect.
Your research question should be:
- Specific and focused
- Measurable
- Clear enough that others can understand what you're investigating
A poorly constructed research question will lead to unfocused data collection and unreliable results. Take time to refine your question before beginning your research.
Types of questions used in research
Researchers use two main types of questions when collecting information from people:
Open-ended questions
Open-ended questions allow people to respond using their own words and express their personal opinions freely.
Advantages:
- You get detailed, insightful responses
- People can share thoughts you might not have considered
- Responses are less likely to be influenced by the researcher's expectations
Disadvantages:
- Some people might skip questions that seem too long or complicated
- Responses can be difficult to analyse and compare
- Takes more time for participants to complete
Closed questions
Closed questions provide specific answer options that people can simply select from (like multiple choice or yes/no questions).
Advantages:
- Quick and easy for people to answer
- Responses are easy to analyse and compare
- Higher completion rates since they require less effort
Disadvantages:
- The provided options might not match what someone actually thinks
- Some people may not answer if none of the options suit them
- You might miss important insights that weren't included in your options
Collecting data
Understanding populations and samples
When conducting research, you need to decide who you want to study. The population refers to the entire group you're interested in learning about. For example, if you want to study Gauteng matriculants, then all matriculants in Gauteng form your population.
Since studying an entire population is often impossible due to size and cost, researchers use a sample instead. A sample is a smaller portion selected from the population to represent the larger group.
Practical Example: Population vs Sample
If you want to study the study habits of all Grade 12 learners in South Africa:
- Population: All Grade 12 learners in South Africa (approximately 400,000+ learners)
- Sample: 1,000 Grade 12 learners selected from different provinces and school types
This sample allows you to study a manageable group while still representing the larger population.
Avoiding sample bias
Sample bias occurs when your sample doesn't accurately represent your target population. This creates problems because your findings won't apply to the broader group you're trying to study.
Common Bias Example: If you only survey learners from city schools to represent all Gauteng matriculants, your sample is biassed because it excludes learners from township and farm schools who might have different experiences.
Random sampling is one method used to reduce sample bias by giving every member of the population an equal chance of being selected. However, even random sampling can sometimes result in bias if certain groups are accidentally over or under-represented.
To minimise bias, researchers should control for important factors like:
- Age
- Gender
- Race
- Geographic location
- Socioeconomic background
Methods for collecting data
The way you collect your data affects the quality and type of information you receive. There are three main approaches:
Observation
Observation involves watching and recording what people do without directly interacting with them.
How it works: Researchers observe behaviours, events, or phenomena and record what they see.
Advantages:
- You get natural, uninfluenced behaviour
- People can't lie or give incorrect information since you're watching directly
- Useful for studying behaviours people might not accurately report
Best used for: Studying natural behaviours, interactions, or events
Observation Example: Studying Library Usage
A researcher wants to understand how students use library spaces. They position themselves in the library and record:
- How long students spend in different areas
- Which resources they use most frequently
- Peak usage times throughout the day
This provides authentic data about actual behaviour rather than what students think they do.
Questionnaires
Questionnaires are written lists of questions that participants complete independently.
How it works: Participants read questions and write their responses without researcher interaction.
Advantages:
- Participants can remain anonymous
- You can collect information from many people efficiently
- People might be more honest about sensitive topics
- Cost-effective for large groups
Best used for: Gathering opinions, attitudes, or factual information from many people
Interviews
Interviews involve direct conversations between a researcher and participant, where the researcher asks questions and records responses.
How it works: The interviewer asks prepared questions and can ask follow-up questions based on responses.
Advantages:
- You can ask for clarification if responses are unclear
- Allows for deeper exploration of topics
- Can adapt questions based on participant responses
- Builds rapport with participants
Best used for: Exploring complex topics that need detailed explanation
Interview Example: Understanding Career Choices
A researcher investigating why students choose certain careers might ask:
- "What influenced your career choice?" (initial question)
- Follow-up: "You mentioned your family's influence - can you tell me more about how that worked?"
- Probe deeper: "How did that conversation change your perspective?"
This allows for rich, detailed responses that reveal the complexity of decision-making.
Key Points to Remember:
- Research questions must be clear and measurable to guide your data collection effectively
- Open-ended questions give detailed responses but take more effort, while closed questions are quick but may limit responses
- Choose your sample carefully to avoid sample bias and ensure your findings represent your target population
- Observation captures natural behaviour, questionnaires efficiently collect information from many people, and interviews allow for detailed exploration of topics
- The method you choose for collecting data should match your research question and the type of information you need