Introduction

Before data can be analysed, it must first be collected. The quality of any investigation depends on the quality of the data gathered. If data is collected carefully and fairly, the conclusions are more likely to be accurate and reliable. However, if data is collected poorly or unfairly, the results may be misleading.

Researchers use different methods to collect data depending on the question they are trying to answer. Some investigations involve simply observing what happens, while others involve changing variables in carefully controlled experiments. Understanding these methods helps us recognise reliable evidence and identify possible sources of error or bias.

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What Is Data Collection?

Data collection is the process of gathering information for analysis.

Researchers collect data to:

• Answer questions.
• Test hypotheses.
• Identify patterns.
• Make predictions.
• Support conclusions.

Choosing an appropriate method of data collection is one of the most important parts of any investigation.

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Figure 1. Data can be collected in many different ways depending on the investigation.

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Common Data Collection Methods

Several methods are commonly used.

Observation

Researchers record what they see without changing the situation.

Examples:

• Watching animal behaviour.
• Counting birds in a park.
• Recording weather conditions.

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Experiments

Researchers deliberately change one variable to investigate its effect on another.

Examples:

• Testing how light affects plant growth.
• Comparing different fertilisers.
• Measuring the effect of temperature on reaction rate.

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Surveys and Questionnaires

People answer questions about opinions, behaviours, or experiences.

Examples:

• Favourite school subject.
• Exercise habits.
• Customer satisfaction.

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Measurements

Researchers use instruments to collect numerical data.

Examples:

• Measuring height.
• Recording temperature.
• Timing a race.
• Measuring rainfall.

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Existing Data

Researchers analyse information that has already been collected.

Examples:

• Government census data.
• Weather records.
• Hospital statistics.
• Scientific databases.

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Figure 2. Different investigations require different methods of collecting data.

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Observational Studies

An observational study records information without interfering with the subjects or environment.

Researchers simply observe and record.

Examples:

• Studying animal migration.
• Recording traffic flow.
• Monitoring weather patterns.
• Observing classroom behaviour.

Observational studies are useful when experiments are impractical or unethical.

However, they cannot usually demonstrate cause and effect, only relationships or patterns.

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Experimental Studies

An experimental study investigates the effect of changing one variable while keeping other variables controlled.

Experiments involve:

• An independent variable (the variable being changed).
• A dependent variable (the variable being measured).
• Controlled variables (variables kept constant).

Example:

Investigating how different amounts of sunlight affect plant growth.

Experiments are useful because they can provide evidence for cause-and-effect relationships.

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Figure 3. Observational studies record natural events, while experiments involve controlled changes to variables.

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Comparing Observational and Experimental Studies

Both methods are valuable, depending on the research question.

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Why Unbiased Data Collection Matters

Bias is anything that unfairly influences the results of an investigation.

Unbiased data collection helps ensure that:

• Results are fair.
• Conclusions are accurate.
• Data represents the population being studied.
• Other researchers can trust the findings.

Scientists work carefully to reduce bias whenever possible.

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Sources of Bias

Bias can occur in many ways.

Sampling Bias

The sample does not fairly represent the population.

Example:

Surveying only students from one class when studying the entire school.

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Measurement Bias

Measuring instruments are inaccurate or used incorrectly.

Example:

Using an incorrectly calibrated thermometer.

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Observer Bias

The researcher's expectations influence observations.

Example:

Recording only behaviours that support a hypothesis.

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Question Bias

Survey questions influence participants' answers.

Example:

"Don't you agree that school uniforms improve behaviour?"

This wording encourages a particular response.

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Selection Bias

Participants are chosen unfairly.

Example:

Allowing volunteers to participate instead of selecting people randomly.

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Figure 4. Identifying sources of bias helps improve the quality of investigations.

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Reducing Bias

Scientists reduce bias by:

• Using random sampling.
• Increasing sample size.
• Using calibrated equipment.
• Following standard procedures.
• Asking neutral survey questions.
• Repeating measurements.
• Allowing independent verification.

These methods improve the reliability of the data.

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Evaluating Data Collection Methods

When evaluating a method, consider:

• Is it appropriate for the question?
• Is the sample representative?
• Are measurements accurate?
• Could bias affect the results?
• Can the investigation be repeated?

Good investigations collect data that is:

• Accurate.
• Reliable.
• Fair.
• Relevant.

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Choosing the Best Method

The best method depends on the type of question being investigated.

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Figure 5. Different research questions require different methods of collecting data.

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Worked Example

Question

A scientist wants to determine whether a new fertiliser increases plant growth.

Should they use an observational study or an experimental study?

Solution

An experimental study is appropriate.

The scientist can:

• Apply the new fertiliser to one group of plants.
• Compare it with a control group.
• Keep all other variables constant.

This allows the scientist to determine whether the fertiliser causes increased growth.

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Real-World Connection

Health researchers often use observational studies to investigate relationships between lifestyle and disease because it would be unethical to deliberately expose people to harmful conditions. For example, researchers may compare the health of people with different exercise habits over many years. In contrast, scientists testing a new medicine usually conduct controlled experiments or clinical trials to determine whether the treatment causes improvements in health.

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Did You Know?

Many weather forecasts rely on millions of observations collected every day from satellites, weather stations, ocean buoys, aircraft, and balloons around the world. By combining these observations, scientists build computer models that predict future weather with increasing accuracy.

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Key Terms

Bias – A systematic influence that causes results to be unfair or unrepresentative.

Controlled variable – A factor kept constant during an experiment.

Data collection – The process of gathering information for analysis.

Dependent variable – The variable that is measured in an experiment.

Experimental study – A study in which researchers deliberately change one variable to investigate its effect on another.

Independent variable – The variable that is deliberately changed in an experiment.

Observation – Collecting data by watching and recording without interfering.

Observational study – A study that records information without changing the conditions being investigated.

Random sampling – Selecting participants so that every member of the population has an equal chance of being chosen.

Survey – A method of collecting information by asking questions.

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Key Takeaways

• Data can be collected through observations, experiments, surveys, measurements, and existing data sources.
• Observational studies record natural events without changing variables, while experimental studies investigate cause-and-effect relationships by controlling variables.
• Unbiased data collection is essential for producing fair, accurate, and trustworthy results.
• Common sources of bias include sampling bias, measurement bias, observer bias, question bias, and selection bias.
• Scientists reduce bias by using random sampling, accurate equipment, standard procedures, and repeated measurements.
• Evaluating data collection methods helps ensure that investigations produce reliable and meaningful conclusions.