How do social researchers choose a sample, and what makes a sample representative in Advanced Higher Modern Studies?
Sampling: the population and sampling frame, probability sampling (random, systematic, stratified, cluster) and non-probability sampling (quota, snowball, convenience), sample size, and representativeness.
How sampling works in SQA Advanced Higher Modern Studies. Covers populations and sampling frames, probability methods (random, systematic, stratified, cluster), non-probability methods (quota, snowball, convenience), sample size, representativeness and the trade-offs that decide which method fits a study.
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What this key area is asking
Almost no study can examine an entire population, so researchers study a sample and use it to draw conclusions about the whole. This dot point covers how samples are chosen, the difference between probability and non-probability methods, and what makes a sample representative enough to trust. Sampling is examined directly in the question paper and is a decision you must justify in your project-dissertation.
Population, sampling frame and sample
The frame matters as much as the method. If the frame omits part of the population, no sampling technique can repair the resulting bias, so a researcher must judge how well the frame matches the population they want to describe.
Probability sampling
- Simple random. Every member has an equal chance; selection is by lottery or random numbers. Strong for generalisation but needs a full frame.
- Systematic. Every nth member of the frame is chosen after a random start. Quick, but risky if the list has a hidden pattern matching the interval.
- Stratified. The population is split into strata (such as age or region) and each is sampled in proportion, guaranteeing every subgroup is represented. Ideal for comparing groups, but needs a frame that records the strata.
- Cluster. The population is divided into clusters (such as schools or constituencies), some clusters are randomly chosen, and members within them are studied. Cheaper over a wide area, but less precise if clusters differ.
Non-probability sampling
Non-probability methods are common in qualitative and exploratory work, and in studying groups with no usable frame (for example, undocumented workers). The trade-off is clear: you gain access and speed but lose the ability to generalise, so conclusions must be stated cautiously.
Sample size and representativeness
A larger sample reduces sampling error, the random difference between the sample and the population, but raises cost and time, so size is always a compromise. Crucially, size is not the same as representativeness: a huge but skewed sample (for example, only online respondents) is less trustworthy than a smaller, well-structured one. Representativeness means the sample mirrors the population in the characteristics that matter for the study, which is what licenses generalisation.
Worked example
Try this
Q1. What is the difference between a population and a sampling frame? [2 marks]
- Cue. The population is everyone the study is about; the sampling frame is the actual list from which the sample is drawn.
Q2. Why can findings from a convenience sample not be generalised to the whole population? [2 marks]
- Cue. Selection is not random and the chance of selection is unknown, so the sample may not represent the population and sampling error cannot be estimated.
Exam-style practice questions
Practice questions written in the style of SQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
SQA AH (research methods)12 marksEvaluate the usefulness of stratified sampling for a study of political attitudes across different age groups.Show worked answer →
A strong answer weighs the strengths and limitations of stratified sampling against the specific aim, rather than describing it in the abstract.
Strengths: stratified sampling divides the population into strata (here, age bands) and samples each in proportion, so every age group is represented in the right share and comparisons between groups are valid. It reduces the risk that a key subgroup is missed by chance, which a simple random sample can do. Limitations: it requires an accurate sampling frame that already records age, which may not exist or may be out of date; it is more complex and costly to organise; and within each stratum selection must still be random or the benefit is lost. The judgement should conclude that for comparing attitudes across age groups stratified sampling is well suited, provided a reliable frame exists, but is harder to run than a simple random sample.
SQA AH (research methods)8 marksExplain the difference between probability and non-probability sampling and give one example of each.Show worked answer →
The marks reward a clear distinction plus correctly classified examples.
In probability sampling every member of the population has a known, non-zero chance of selection, which supports generalising to the whole population and estimating sampling error; an example is simple random sampling. In non-probability sampling the chance of selection is unknown and selection is not random, so findings cannot be generalised with statistical confidence but the method is cheaper and useful for hard-to-reach groups; an example is snowball sampling, where participants recruit others. A full answer names the defining feature of each (known versus unknown probability of selection) and gives one valid example for each.
Related dot points
- The social research process: framing a research question and aim, forming a hypothesis, choosing a method, gathering and analysing data, and reporting conclusions as a repeatable cycle.
How the social research process works in SQA Advanced Higher Modern Studies. Covers framing an aim and research question, hypotheses, choosing methods, gathering and analysing data, drawing conclusions, and why research is a structured, repeatable cycle that underpins both the question paper and the dissertation.
- Primary research methods: questionnaires and surveys, interviews (structured, semi-structured, unstructured), focus groups, observation and field research, with their strengths, limitations and the quantitative-qualitative distinction.
How primary research methods work in SQA Advanced Higher Modern Studies. Covers questionnaires and surveys, structured to unstructured interviews, focus groups, observation and field research, the quantitative-qualitative distinction, and how to justify a method against a research aim.
- Evaluating research quality: reliability and replicability, validity, objectivity versus bias, representativeness and generalisability, and research ethics (informed consent, confidentiality, harm).
How research quality is judged in SQA Advanced Higher Modern Studies. Covers reliability and replicability, validity, objectivity versus bias, representativeness and generalisability, and the ethics of social research including informed consent, confidentiality and avoiding harm.
- Secondary research methods: official statistics, academic literature, media and online sources, content analysis, and critically evaluating secondary data for bias, accuracy and currency.
How secondary research works in SQA Advanced Higher Modern Studies. Covers official statistics, academic literature, media and online sources, content analysis, and how to evaluate secondary data critically for bias, accuracy, currency and the purpose behind it.
- Drawing conclusions: synthesising evidence to answer the research question, judging the hypothesis, supporting conclusions with data, acknowledging limitations, and the source-based conclusions question in the exam.
How to draw sound conclusions in SQA Advanced Higher Modern Studies. Covers synthesising evidence to answer the research question, judging the hypothesis, supporting each conclusion with data, acknowledging limitations, and the source-based draw-conclusions question in the exam.