• Sampling: the difference between population and sample; sampling techniques including: random, systematic, stratified, opportunity and volunteer; implications of sampling techniques, including bias and generalisation.

What is a Population?

• Entire group researcher wishes to study.

Example of a Population

• All Year 13 students in UK.

What is a Sample?

• Smaller group selected from population and used in study.

Example of a Sample

• 50 Year 13 students from one school.

What is Sampling?

• Process of selecting participants from population.

Why is Sampling used in Research?

• Studying entire population is often too expensive, time-consuming and impractical.

Difference between Population and Sample

• Population is entire target group.
• Sample is smaller selected group from population.

What are the Main Types of Sampling Technique?

• Random sampling.
• Systematic sampling.
• Stratified sampling.
• Opportunity sampling.
• Volunteer sampling.

What is Random Sampling?

• Sampling technique where every member of population has equal chance of selection.

Example of Random Sampling

• Participants selected using random number generator or names from hat.

Strengths of Random Sampling

• Reduces researcher bias.
• Equal chance of selection may improve representativeness.

Why does Random Sampling reduce Researcher Bias?

• Researcher does not choose participants personally.

Limitations of Random Sampling

• Time-consuming because full population list needed.
• Sample may still be unrepresentative by chance.

What is Systematic Sampling?

• Selecting every nth person from population list.

Example of Systematic Sampling

• Selecting every 5th person from register.

Strengths of Systematic Sampling

• Less researcher bias because fixed system used.
• Simple and organised method of sampling.

Why does Systematic Sampling reduce Researcher Bias?

• Participants selected using fixed pattern rather than researcher choice.

Limitations of Systematic Sampling

• Patterns in population list may bias sample.
• Requires full population list.

What is Stratified Sampling?

• Sampling technique where population divided into subgroups and participants selected proportionally from each group.

What are Strata in Stratified Sampling?

• Subgroups within population.

Example of Stratified Sampling

• Population with 60% females and 40% males sampled using same proportions.

Strengths of Stratified Sampling

• Produces more representative sample.
• Reduces sampling bias because groups represented proportionally.

Why is Stratified Sampling usually more Representative?

• Sample reflects important characteristics of population proportionally.

Limitations of Stratified Sampling

• Time-consuming because population information needed.
• More difficult and complex to organise.

How do you Calculate Stratified Sampling?

• Divide number in subgroup by total population.

• Multiply answer by sample size needed.

• This gives number of participants needed from each subgroup.

What is Opportunity Sampling?

• Selecting participants available at time of study.

What is another Name for Opportunity Sampling?

• Convenience sampling.

Example of Opportunity Sampling

• Stopping people in shopping centre to participate.

Strengths of Opportunity Sampling

• Quick and convenient.
• Requires little planning and is inexpensive.

Limitations of Opportunity Sampling

• Researcher bias may occur.
• Sample may be unrepresentative of wider population.

Why can Opportunity Sampling produce Researcher Bias?

• Researcher chooses participants who are easiest to access.

What is Volunteer Sampling?

• Participants actively choose to take part in study.

Example of Volunteer Sampling

• Participants responding to advert or online sign-up form.

Strengths of Volunteer Sampling

• Participants usually motivated to take part.
• Recruitment process is easy because participants volunteer themselves.

Limitations of Volunteer Sampling

• Volunteer bias may occur.
• Sample may be unrepresentative of wider population.

What is Volunteer Bias?

• Certain personality types more likely to volunteer for studies.

What is Sampling Bias?

• Sample does not accurately represent population.

Why is Sampling Bias a Problem?

• Reduces ability to generalise findings to wider population.

What is Generalisation?

• Applying findings from sample to wider population.

Why is Representativeness Important in Sampling?

• Representative samples reflect characteristics of population improving validity and generalisation.

Difference between Random and Opportunity Sampling

• Random sampling gives everyone equal chance of selection.
• Opportunity sampling selects participants who are easiest to access.

Difference between Stratified and Random Sampling

• Stratified sampling selects participants proportionally from subgroups.
• Random sampling gives equal chance without considering subgroup proportions.

Difference between Volunteer and Opportunity Sampling

• Volunteer sampling involves participants choosing themselves.
• Opportunity sampling involves researcher selecting available participants.