Before collecting data, it is essential to specify exactly what data is needed and to choose an appropriate sampling method. Careful planning improves the quality, fairness and reliability of conclusions.

 

 

Specifying the Data Needed

The first step is to decide what information is required to answer the question being investigated.

 

This involves:
• identifying the variable to be measured
• deciding whether the data is qualitative or quantitative
• deciding whether quantitative data is discrete or continuous
• deciding what units or categories will be used

 

The data chosen must be relevant to the question.

 

For example, if the question is about travel habits, the data might include types of transport used or time taken to travel.

 

Unnecessary data makes analysis harder without improving conclusions

 

 

Choosing How Much Data to Collect

The amount of data collected affects reliability.

 

When deciding how much data is needed, consider:
• the size of the population
• the level of accuracy required
• time and resource limits

 

Larger samples usually give more reliable results, but practical constraints must be considered.

 

 

Considering Sampling Methods

A sampling method describes how individuals or items are selected from a population.

 

The aim is to choose a sample that represents the population fairly.

 

Common sampling methods include random sampling, systematic sampling and stratified sampling.

 

Each method has advantages and limitations.

 

 

Random Sampling

In random sampling, every member of the population has an equal chance of being selected.

 

This helps reduce bias and improve fairness.

 

Random sampling is often carried out using random number tables or generators.

 

 

Systematic Sampling

In systematic sampling, individuals are selected at regular intervals.

 

For example, every tenth person on a list might be chosen.

 

This method is simple to apply but may introduce bias if there is a pattern in the list.

 

 

Stratified Sampling

In stratified sampling, the population is divided into groups, called strata, and samples are taken from each group.

 

This ensures that key groups are represented.

 

For example, a school survey may include students from each year group.

 

Stratified sampling improves fairness when the population is not uniform.

 

 

Considering Practical Constraints

When choosing a sampling method, it is important to consider:
• time available
• cost
• access to participants
• ease of implementation

 

The best method is often a balance between fairness and practicality.

 

 

Key Points to Remember

Data must be relevant to the question being investigated.
Decide the type of data before collecting it.
Larger samples usually improve reliability.
Sampling methods affect fairness and bias.
The chosen method should be practical and appropriate.

 

Careful specification of data and thoughtful selection of sampling methods lead to more reliable data and more trustworthy conclusions.