Sampling Methods

A $\text{census}$ observes every member of the population. A $\text{sample}$ observes a subset. The $\text{sampling frame}$ is the list of population members from which the sample is drawn — a school register, an employee database, an electoral roll.

A census gives a completely accurate picture, but it is expensive, slow, and sometimes impossible: testing every match in a factory’s output would leave nothing to sell, because the test destroys the item. A sample is cheaper and faster but only ever an estimate, and it can be biased.

Not every population has a usable sampling frame. You cannot list every fish in a lake, which immediately rules out any method that requires numbering the population.

In a $\text{simple random sample}$, every member of the population has an equal chance of selection and every possible sample is equally likely. In practice you number the sampling frame and generate random numbers, discarding repeats. It is unbiased, but needs a full frame and can be laborious.

$\text{Systematic sampling}$ takes every $k$th member from an ordered list, where $k$ is the population size divided by the sample size, with a random start in the first block. It is quicker than simple random sampling and spreads the sample through the list — but if the list has a repeating pattern with the same period as $k$, the sample can be badly skewed.

$\text{Stratified sampling}$ divides the population into groups (strata) that matter — year groups, departments, age bands — and samples from each in proportion to its size. It guarantees every group is represented in the right ratio, which simple random sampling only does on average.

$\text{Quota sampling}$ sets targets for how many of each type to interview — 20 men and 20 women, say — and the interviewer chooses who fills the quota. It is cheap and needs no sampling frame, but the interviewer’s choices introduce bias: people who look approachable get picked disproportionately.

$\text{Opportunity sampling}$ (also called convenience sampling) takes whoever is available at the time. It is the fastest method and the least reliable, because the people who happen to be in one place at one time are rarely typical of the population.

Neither method is random, so strictly the inference machinery of later chapters does not apply. They appear in the specification because they are common in real research and you need to be able to criticise them.

Exam questions give a scenario and ask which method is appropriate, or ask you to criticise one. Two questions settle most cases: is there a usable sampling frame, and does the population contain groups that must be represented proportionally?

No frame available points to quota or opportunity sampling. A frame plus important subgroups points to stratified. A frame with no meaningful subgroups allows simple random or systematic.

Whatever you choose, name the specific advantage and the specific drawback in context rather than in the abstract.