Sampling Methods

Population

The entire group of people the researcher is interested in studying.

Sample

A smaller group selected from the population to take part in the study.

Target population

The specific group of people the research findings aim to generalise to.

Sampling technique

The method used to select participants from the population.

Representativeness

The extent to which the sample reflects the characteristics of the target population.

Sampling bias

When certain groups in the population are overrepresented or underrepresented in the sample.

Random sampling

Every member of the target population has an equal chance of being selected.

Strength of random sampling

Reduces researcher bias and increases representativeness.

Limitation of random sampling

Difficult and time-consuming; may still produce unrepresentative samples by chance.

Systematic sampling

Participants selected at regular intervals from an ordered list (e.g. every 5th person).

Strength of systematic sampling

More representative than opportunity sampling; reduces researcher bias.

Limitation of systematic sampling

Still possible that the sample becomes unrepresentative if the list has a pattern.

Stratified sampling

Population divided into strata (subgroups) and participants randomly selected from each proportionally.

Strength of stratified sampling

Highly representative because subgroups are accurately reflected.

Limitation of stratified sampling

Requires knowledge of population proportions; time-consuming.

Strata

Meaningful subgroups such as age, gender or ethnicity.

Proportional sampling

Ensuring sample percentages match population percentages.

Opportunity sampling

Selecting participants who are most easily available.

Strength of opportunity sampling

Quick, easy and economical.

Limitation of opportunity sampling

High risk of sampling bias and low representativeness.

Volunteer sampling

Participants self-select in response to an advert or request.

Strength of volunteer sampling

Easy to collect large samples and participants are motivated.

Limitation of volunteer sampling

High chance of volunteer bias; may attract certain personality types.

Volunteer bias

Participants may share traits such as being more helpful or having more time.

Convenience sampling

Another term for opportunity sampling.

Random number generator

Used in random sampling to select participants without researcher bias.

Sampling frame

A list of all members of the target population used in systematic and random sampling.

Self-selected sample

A sample comprised of participants who volunteer to participate.

Generalisation

Applying findings from the sample to the target population.

Improving representativeness

Use larger, randomly selected samples with clear demographic balance.

Sampling error

Differences between the sample and population that occur by chance.

External validity

Improves when the sample reflects the target population accurately.

Undercoverage

When some members of the population cannot be selected.

Oversampling

When certain groups are selected too frequently relative to their population proportion.

Unrepresentative sample

A sample that does not accurately reflect the target population.

Quota sampling

A sampling method where researchers fill quotas for each subgroup; not used in AQA but useful context.

Importance of sample size

Larger samples more likely to be representative due to the law of large numbers.

Ethical considerations in sampling

Participants must be treated fairly and given full rights of consent and withdrawal.

Pilot sample

A small sample tested before the main study to identify issues with recruitment.

Sampling in correlational research

No manipulation needed but representativeness still important.

Sampling in experiments

Controls participant variables and increases internal validity.

Sampling in observations

Observers may rely on opportunity samples, reducing representativeness.

Sampling in interviews

Often uses volunteer sampling which risks bias.

Population validity

How well findings generalise beyond the sample to other people.

Random sampling challenge

It may be hard to access all members of the target population.

Systematic sampling challenge

If the list has an underlying pattern, sample may become biased.

Stratified sampling challenge

Requires accurate population statistics to divide strata properly.

Volunteer sampling challenge

Participants may be overly confident, motivated, or have specific traits.

Principle of equal chance

Core component of random and systematic sampling.

Selecting participants fairly

Reduces bias and increases the external validity of findings.

Haphazard sampling

Informal selection without a clear method; extremely biased.

Self-selection ethical issue

Volunteers may not understand their rights unless clearly informed.

Purpose of sampling

To draw valid conclusions about the population from a manageable group.