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unrepresentative.","correct":false,"explanation":"Studying the entire target population cannot be unrepresentative of that population. Sampling is necessary because including everyone is usually impractical in terms of time, money and access."},{"text":"Studying everyone relevant is usually impossible to generalise from.","correct":false,"explanation":"Research covering everyone relevant would not require generalisation from a sample. Sampling is used because studying the whole target population is usually impractical."},{"text":"Studying everyone relevant is usually less valid than sampling.","correct":false,"explanation":"A census is not inherently less valid than a sample and may provide more complete information. Sampling is necessary mainly because studying everyone is usually impractical."}]},{"id":"013aa456-6eb8-458f-af52-b58f6045fe6b","cardType":"cloze","points":null,"front":"The [ ] is the whole group the researcher wants to make claims about; the [ ] is the smaller group actually studied.","back":"The target population is the whole group the researcher wants to make claims about; the sample is the smaller group actually studied.","mcqOptions":[{"text":"The target population is the whole group the researcher wants to make claims about; the sample is the smaller group actually studied.","correct":true,"explanation":"The target population includes everyone to whom the findings are intended to apply, while the sample consists of the people selected for direct study."},{"text":"The sampling frame is the whole group the researcher wants to make claims about; the sample is the smaller group actually studied.","correct":false,"explanation":"A sampling frame is the list used to select participants, not the entire group of interest. That whole group is the target population, while those studied form the sample."},{"text":"The target population is the whole group the researcher wants to make claims about; the sampling frame is the smaller group actually studied.","correct":false,"explanation":"The smaller group actually studied is the sample. A sampling frame is only the list or database from which researchers may select that sample."},{"text":"The sample is the whole group the researcher wants to make claims about; the target population is the smaller group actually studied.","correct":false,"explanation":"This reverses the two concepts. The target population is the whole group of interest, whereas the sample is the smaller group selected and studied."}]},{"id":"6f6c5feb-446a-4370-9707-0340021c24bd","cardType":"cloze","points":null,"front":"A [ ] is the list or database from which a sample is selected.","back":"A sampling frame is the list or database from which a sample is selected.","mcqOptions":[{"text":"sampling frame","correct":true,"explanation":"A sampling frame is the operational list or database of members of the target population from which researchers select the people in their sample."},{"text":"target population","correct":false,"explanation":"The target population is the whole group about which the researcher wants to make claims. The list used to select participants is the sampling frame."},{"text":"sample","correct":false,"explanation":"The sample is the smaller group that is actually studied, not the source list. The list or database used for selection is the sampling frame."},{"text":"census","correct":false,"explanation":"A census attempts to collect information from every member of a population rather than providing a selection list. That list or database is the sampling frame."}]},{"id":"cd0a5a64-7faa-43f8-9e10-f22103c9ff80","cardType":"cloze","points":null,"front":"A [ ] controls access to potential participants.","back":"A gatekeeper controls access to potential participants.","mcqOptions":[{"text":"gatekeeper","correct":true,"explanation":"A gatekeeper has the authority or practical ability to permit or restrict a researcher's access to a setting, group or potential participants."},{"text":"respondent","correct":false,"explanation":"A respondent supplies information by answering research questions but does not necessarily control access to others. The person controlling access is called a gatekeeper."},{"text":"researcher","correct":false,"explanation":"The researcher conducts the study and seeks access, but another person may decide whether access is allowed. That access-controlling person is the gatekeeper."},{"text":"key informant","correct":false,"explanation":"A key informant offers valuable knowledge about a group or setting but need not control entry. Someone who controls access to participants is a gatekeeper."}]},{"id":"75e1252f-0482-4e12-8300-a53ae4bd62fc","cardType":"basic","points":1,"front":"What can an incomplete sampling frame do to a random sample?","back":"It can make the sample biased or unrepresentative.","mcqOptions":[{"text":"It can make the sample biased or unrepresentative.","correct":true,"explanation":"If sections of the target population are missing from the sampling frame, they cannot be selected, causing systematic undercoverage and reducing the sample's representativeness."},{"text":"It can make the sample larger and more accurate.","correct":false,"explanation":"An incomplete frame excludes eligible people rather than increasing sample size or accuracy. This undercoverage can instead produce a biased or unrepresentative sample."},{"text":"It can make the sample free from sampling error.","correct":false,"explanation":"Random selection cannot correct omissions from the sampling frame, and sampling error remains possible. Missing groups can make the resulting sample biased or unrepresentative."},{"text":"It can make the sample identical to the target population.","correct":false,"explanation":"A sample cannot accurately mirror groups that are absent from the list used for selection. An incomplete frame can therefore create bias or unrepresentativeness."}]},{"id":"daa50888-5f4a-49b6-baf2-e022130836c3","cardType":"cloze","points":null,"front":"A [ ] sample reflects the target population's key characteristics.","back":"A representative sample reflects the target population's key characteristics.","mcqOptions":[{"text":"representative","correct":true,"explanation":"A representative sample reflects significant characteristics of the target population, such as age, class or ethnicity, allowing findings to be generalised more confidently."},{"text":"unrepresentative","correct":false,"explanation":"An unrepresentative sample differs systematically from the target population in important characteristics. A sample that reflects those characteristics is described as representative."},{"text":"biased","correct":false,"explanation":"A biased sample systematically overrepresents or underrepresents particular groups rather than reflecting the population accurately. The correct description is representative."},{"text":"self-selected","correct":false,"explanation":"A self-selected sample consists of volunteers and may overrepresent people with strong views or interests. A sample reflecting population characteristics is representative."}]},{"id":"2188ba1c-2fa6-4e5a-b630-f25099115e3b","cardType":"basic","points":2,"front":"Why are representative samples valued in large-scale surveys?","back":"They support generalisation to the wider population.","mcqOptions":[{"text":"They support generalisation to the wider population.","correct":true,"explanation":"When a sample accurately reflects the target population's characteristics, researchers can more confidently apply patterns found in the sample to that wider population."},{"text":"They support causal explanation of the wider population.","correct":false,"explanation":"Representativeness helps researchers generalise findings but does not by itself establish cause and effect. Causal explanation requires an appropriate design and control of variables."},{"text":"They support perfect reliability across the wider population.","correct":false,"explanation":"Representativeness concerns whether findings can be generalised, whereas reliability concerns consistency and replicability. No sample can guarantee perfect reliability across a population."},{"text":"They support researcher access to the wider population.","correct":false,"explanation":"Access depends on sampling frames, gatekeepers and participant cooperation, not on representativeness. Representative samples are valued because they support generalisation to the wider population."}]},{"id":"116454e0-5d2b-424b-a126-45c19ee132e0","cardType":"basic","points":2,"front":"How does simple random sampling reduce researcher bias?","back":"It uses chance selection, not researcher choice.","mcqOptions":[{"text":"It uses chance selection, not researcher choice.","correct":true,"explanation":"Simple random sampling selects participants through a chance procedure, giving each member of the sampling frame an equal opportunity and limiting conscious researcher selection."},{"text":"It uses researcher selection, not random choice.","correct":false,"explanation":"Allowing the researcher to choose participants creates opportunities for conscious or unconscious selection bias. Simple random sampling reduces this through chance selection instead."},{"text":"It uses volunteer selection, not researcher choice.","correct":false,"explanation":"Volunteer selection produces a self-selected sample and can overrepresent people with strong views or interests. Simple random sampling reduces researcher bias through chance selection."},{"text":"It uses quota selection, not researcher choice.","correct":false,"explanation":"Quota sampling requires researchers to find participants who meet categories, leaving scope for personal selection. Simple random sampling instead relies on chance selection."}]},{"id":"ef210fc9-ae01-4dca-933f-9bb9971a9749","cardType":"basic","points":1,"front":"What weakness can simple random sampling have even when random?","back":"It may miss small important groups by chance.","mcqOptions":[{"text":"It may miss small important groups by chance.","correct":true,"explanation":"Random selection gives every member an equal chance, but chance alone does not guarantee that numerically small yet sociologically significant groups will appear in the final sample."},{"text":"It may over-represent all minority groups by design.","correct":false,"explanation":"Simple random sampling does not deliberately over-represent minorities because selection probabilities are equal. Its weakness is that it may miss small important groups purely by chance."},{"text":"It may make all respondents dishonest by chance.","correct":false,"explanation":"Random sampling does not automatically make respondents dishonest; response validity is a separate issue. The sampling weakness is that chance may exclude small important groups."},{"text":"It may require no sampling frame at all.","correct":false,"explanation":"Simple random sampling requires a sampling frame rather than removing the need for one. Even with a suitable frame, it may miss small important groups by chance."}]},{"id":"ab23424c-afd7-4f82-905a-57be50cb7d05","cardType":"cloze","points":null,"front":"Stratified random sampling divides the population into [ ] before randomly sampling from each subgroup.","back":"Stratified random sampling divides the population into strata before randomly sampling from each subgroup.","mcqOptions":[{"text":"strata","correct":true,"explanation":"Strata are subgroups based on relevant characteristics, such as class, gender or ethnicity. Researchers then randomly sample from each stratum, usually in proportion to its population size."},{"text":"clusters","correct":false,"explanation":"Clusters are naturally occurring groups used in cluster sampling, where selected groups may be studied as units. Stratified sampling instead divides the population into strata."},{"text":"quotas","correct":false,"explanation":"Quotas are target numbers used in non-random quota sampling, often filled through interviewer choice. Stratified random sampling divides the population into strata before selection."},{"text":"networks","correct":false,"explanation":"Networks describe social connections and are especially relevant to snowball sampling. Stratified random sampling first divides the population into strata based on relevant characteristics."}]},{"id":"a3d6def7-06bc-46cb-b128-e6c9bb01a88c","cardType":"basic","points":2,"front":"Why use stratified random sampling when social divisions matter?","back":"It includes key groups in suitable proportions.","mcqOptions":[{"text":"It includes key groups in suitable proportions.","correct":true,"explanation":"Stratification ensures that socially significant categories are represented according to their presence in the population, reducing the risk that random chance will omit or distort key groups."},{"text":"It removes key groups from social comparisons.","correct":false,"explanation":"Stratified random sampling does not remove every difference between groups; those divisions are precisely what define the strata. It includes key groups in suitable proportions."},{"text":"It includes key groups in identical numbers.","correct":false,"explanation":"Researchers do not normally give every group identical representation, because differently sized groups would then be distorted. Stratification includes key groups in suitable proportions."},{"text":"It lets interviewers choose convenient participants.","correct":false,"explanation":"Allowing interviewers to choose participants within targets describes quota sampling and can introduce bias. Stratified random sampling instead includes key groups in suitable proportions."}]},{"id":"8c1669c2-1724-4db9-9923-9b84626603cb","cardType":"cloze","points":null,"front":"Systematic random sampling selects people at [ ] after a random starting point.","back":"Systematic random sampling selects people at regular intervals after a random starting point.","mcqOptions":[{"text":"regular intervals","correct":true,"explanation":"After choosing a random starting point, researchers select every nth person from the sampling frame. This creates regular intervals while retaining a random element in the starting position."},{"text":"changing intervals","correct":false,"explanation":"Selecting people at changing intervals would not follow the fixed every-nth-person procedure of systematic sampling. The selections must occur at regular intervals after a random start."},{"text":"fixed quotas","correct":false,"explanation":"Quota targets specify how many people with particular characteristics are needed, rather than positions on a sampling frame. Systematic sampling selects people at regular intervals."},{"text":"social networks","correct":false,"explanation":"Social networks are used to recruit connected participants in snowball sampling. Systematic random sampling works through a sampling frame by selecting people at regular intervals."}]},{"id":"6a2f43f9-e74c-413f-ab93-b314e2e451f4","cardType":"basic","points":1,"front":"When can systematic random sampling become biased?","back":"When the sampling frame has a hidden pattern.","mcqOptions":[{"text":"When the sampling frame has a hidden pattern.","correct":true,"explanation":"A repeating arrangement in the sampling frame may coincide with the chosen interval, repeatedly selecting or excluding particular categories and making the resulting sample systematically unrepresentative."},{"text":"When the sampling frame has an alphabetical order.","correct":false,"explanation":"Alphabetical ordering does not necessarily cause bias unless it creates a repeating pattern that coincides with the sampling interval. Bias arises when the frame has a hidden pattern."},{"text":"When the sampling frame has a random starting point.","correct":false,"explanation":"A random starting point is a required safeguard in systematic random sampling, not normally a source of bias. Bias can arise when the frame has a hidden pattern."},{"text":"When the sampling frame has a complete population.","correct":false,"explanation":"Including every member once makes a sampling frame comprehensive rather than systematically biased. The problem occurs when its ordering contains a hidden pattern matching the sampling interval."}]},{"id":"f766fd15-cc72-4886-bc22-708e6167090e","cardType":"basic","points":2,"front":"Why is quota sampling not fully random?","back":"Interviewers choose within quotas, risking interviewer bias.","mcqOptions":[{"text":"Interviewers choose within quotas, risking interviewer bias.","correct":true,"explanation":"Although quotas control the numbers recruited from specified categories, interviewers usually decide which accessible individuals to approach, allowing their preferences to influence selection."},{"text":"Interviewers sample randomly within strata, preventing interviewer bias.","correct":false,"explanation":"Random selection within each subgroup describes stratified random sampling, not typical quota sampling. Quota sampling is not fully random because interviewers choose within quotas."},{"text":"Interviewers select at regular intervals, preventing interviewer bias.","correct":false,"explanation":"Using a random starting point and fixed intervals describes systematic random sampling. In quota sampling, interviewers choose within quotas, creating a risk of interviewer bias."},{"text":"Interviewers recruit through social networks, avoiding interviewer bias.","correct":false,"explanation":"Snowball recruitment occurs when participants identify further contacts, especially in hidden populations. Quota sampling is non-random because interviewers choose within quotas, risking interviewer bias."}]},{"id":"b16c44ed-188d-49ae-9af7-27ee7329fd53","cardType":"cloze","points":null,"front":"Non-representative qualitative sampling often fits [ ], which values subjective meanings.","back":"Non-representative qualitative sampling often fits interpretivism, which values subjective meanings.","mcqOptions":[{"text":"interpretivism","correct":true,"explanation":"Interpretivism prioritises understanding subjective meanings and detailed social experiences rather than statistical representativeness, so purposive, snowball and other qualitative sampling methods often suit it."},{"text":"positivism","correct":false,"explanation":"Positivism generally prioritises objectivity, measurement and representative samples so findings can be generalised. Sampling focused on subjective meanings is more closely associated with interpretivism."},{"text":"functionalism","correct":false,"explanation":"Functionalism is a structural theory about how society’s institutions contribute to stability, not the methodological approach centred on subjective meanings. That approach is interpretivism."},{"text":"postmodernism","correct":false,"explanation":"Postmodernism questions fixed truths and overarching explanations, but the methodological tradition specifically emphasising actors’ subjective meanings is interpretivism, which often accepts non-representative qualitative samples."}]},{"id":"caa75c56-b840-43ee-b272-31453e684edf","cardType":"basic","points":2,"front":"Why might snowball sampling suit hidden populations?","back":"Participants can reach others through social networks.","mcqOptions":[{"text":"Participants can reach others through social networks.","correct":true,"explanation":"Members of hidden or hard-to-reach populations may know others in the same group, enabling referrals to recruit participants whom researchers could not easily identify directly."},{"text":"Participants can be listed on a complete sampling frame.","correct":false,"explanation":"Hidden populations often lack a complete list from which random selections could be made. Snowball sampling works because participants recruit others through social networks."},{"text":"Participants can be selected at regular intervals.","correct":false,"explanation":"Regular intervals require an ordered sampling frame, which is often unavailable for hidden populations. Snowball sampling instead uses participants’ social networks to locate further members."},{"text":"Participants can reveal others through public announcements.","correct":false,"explanation":"Publicly advertising participants’ identities could breach confidentiality and discourage involvement in sensitive research. Snowball sampling succeeds because trusted referrals operate through social networks."}]},{"id":"9d48479e-d403-463a-97a7-c8d63ec26245","cardType":"basic","points":1,"front":"What is the key weakness of snowball sampling?","back":"It risks network bias.","mcqOptions":[{"text":"It risks network bias.","correct":true,"explanation":"Participants recruit people from their own social networks, so the sample may overrepresent interconnected groups and exclude people outside those networks."},{"text":"It requires a complete sampling frame.","correct":false,"explanation":"Snowball sampling is often used precisely when no complete sampling frame exists. Its key weakness is network bias because referrals cluster among socially connected people."},{"text":"It guarantees equal selection chances.","correct":false,"explanation":"Equal selection chances are associated with random sampling, not snowball sampling. Snowball sampling risks network bias because recruitment depends on participants’ social connections."},{"text":"It prevents access to hidden populations.","correct":false,"explanation":"Snowball sampling can improve access to hidden populations through trusted referrals. Its weakness is network bias, as recruits often come from similar interconnected groups."}]},{"id":"55975f96-d316-459a-a9c1-b4c1f50dbd84","cardType":"basic","points":2,"front":"What causes volunteer bias?","back":"People with strong opinions or experiences are more likely to respond.","mcqOptions":[{"text":"People with strong opinions or experiences are more likely to respond.","correct":true,"explanation":"People who feel strongly about a topic are more likely to volunteer, so their views may be overrepresented and the resulting sample may be biased."},{"text":"People with little interest or experience are more likely to respond.","correct":false,"explanation":"People with little interest are generally less motivated to volunteer. Volunteer bias occurs because those with strong opinions or relevant experiences are more likely to respond."},{"text":"Researchers with strong opinions or expectations are more likely to influence responses.","correct":false,"explanation":"A researcher influencing answers would create interviewer or researcher bias. Volunteer bias arises when people with strong opinions or experiences are especially likely to participate."},{"text":"People chosen through random selection procedures are more likely to respond.","correct":false,"explanation":"Random selection determines who is invited, not who chooses to participate. Volunteer bias occurs when strongly interested or experienced individuals are more likely to respond."}]},{"id":"988a5723-9975-481c-a543-0d9b93c85d44","cardType":"basic","points":2,"front":"Why use purposive sampling in qualitative sociology?","back":"It selects participants with relevant characteristics or experiences.","mcqOptions":[{"text":"It selects participants with relevant characteristics or experiences.","correct":true,"explanation":"Purposive sampling deliberately recruits people whose characteristics or experiences can provide detailed, relevant data about the qualitative research question."},{"text":"It gives population members an equal chance of selection.","correct":false,"explanation":"Equal selection chances define random sampling rather than purposive sampling. Purposive sampling deliberately selects participants with characteristics or experiences relevant to the research."},{"text":"It recruits participants who are quickest and easiest to access.","correct":false,"explanation":"Choosing whoever is easiest to access is opportunity sampling. Purposive sampling instead selects people because they possess characteristics or experiences relevant to the study."},{"text":"It recruits participants through referrals from existing participants.","correct":false,"explanation":"Recruitment through participants’ referrals is snowball sampling. Purposive sampling involves researchers deliberately choosing people with characteristics or experiences relevant to the research."}]},{"id":"490f81c3-3d64-4516-8f22-b5fa82351012","cardType":"basic","points":2,"front":"Why is opportunity sampling useful for a pilot study?","back":"Easy access makes it quick and cheap.","mcqOptions":[{"text":"Easy access makes it quick and cheap.","correct":true,"explanation":"Opportunity sampling uses participants who are readily available, allowing researchers to test methods and identify practical problems quickly without spending heavily on recruitment."},{"text":"Random selection makes it representative and generalisable.","correct":false,"explanation":"Opportunity sampling does not use random selection and is unlikely to be representative. Its pilot-study value comes from easy access making recruitment quick and cheap."},{"text":"Participant referrals make it ideal for hidden populations.","correct":false,"explanation":"Participant referrals are characteristic of snowball sampling, not opportunity sampling. Opportunity sampling suits pilot studies because accessible participants make it quick and cheap."},{"text":"A sampling frame makes it systematic and unbiased.","correct":false,"explanation":"Opportunity sampling requires no sampling frame and can be biased towards available people. It is useful for pilots because easy access makes it quick and cheap."}]},{"id":"9984d786-86d1-421b-82df-17256e8533bf","cardType":"basic","points":2,"front":"How can gatekeepers weaken validity?","back":"They may restrict access to unrepresentative participants.","mcqOptions":[{"text":"They may restrict access to unrepresentative participants.","correct":true,"explanation":"Gatekeepers can decide which people researchers meet, producing a selective sample that fails to reflect the wider group and therefore reduces the validity of findings."},{"text":"They may provide access to all relevant social groups.","correct":false,"explanation":"Access to all relevant groups would improve representativeness rather than weaken validity. Gatekeepers may instead restrict researchers to unrepresentative participants, creating a selective sample."},{"text":"They may encourage participants to give fully honest responses.","correct":false,"explanation":"Fully honest responses would strengthen validity rather than weaken it. Gatekeepers reduce validity when they restrict access to participants who are unrepresentative of the wider group."},{"text":"They may ensure the researcher remains completely objective throughout.","correct":false,"explanation":"Gatekeepers cannot guarantee researcher objectivity, and objectivity would not explain reduced validity. The problem is that they may restrict access to unrepresentative participants."}]},{"id":"9619c3b4-20ca-49b4-bcf6-40507f556900","cardType":"basic","points":2,"front":"What ethical problem can gatekeepers create?","back":"Participants may feel unable to refuse.","mcqOptions":[{"text":"Participants may feel unable to refuse.","correct":true,"explanation":"A gatekeeper may hold authority over potential participants, making them feel pressured to participate and undermining the requirement that consent must be freely given."},{"text":"Participants may feel completely free to withdraw.","correct":false,"explanation":"Feeling free to withdraw is an ethical safeguard, not a problem. Gatekeeper authority may instead make participants feel unable to refuse, undermining voluntary consent."},{"text":"Participants may feel fully informed about the research.","correct":false,"explanation":"Being fully informed supports valid informed consent rather than creating an ethical problem. Gatekeepers may create pressure that leaves participants feeling unable to refuse participation."},{"text":"Participants may feel confident their data is private.","correct":false,"explanation":"Confidence in privacy reflects appropriate confidentiality procedures, not gatekeeper pressure. The ethical concern is that participants may feel unable to refuse because of the gatekeeper’s authority."}]},{"id":"982903f4-f764-4252-8c0b-1316cb5eba7c","cardType":"basic","points":2,"front":"Why can excluding marginalised groups from sampling frames understate inequality?","back":"Their experiences become less visible in findings.","mcqOptions":[{"text":"Their experiences become less visible in findings.","correct":true,"explanation":"Excluding marginalised groups removes evidence of the disadvantages they experience, making inequality appear less extensive or severe than it actually is."},{"text":"Their experiences become more visible in findings.","correct":false,"explanation":"Exclusion makes marginalised experiences less visible, not more visible. Without their evidence, findings can conceal disadvantages and understate the true extent of inequality."},{"text":"Their disadvantages become fully represented in findings.","correct":false,"explanation":"A group’s disadvantages cannot be fully represented when that group is missing from the sampling frame. Their experiences become less visible, causing inequality to be understated."},{"text":"Their perspectives become overemphasised in findings.","correct":false,"explanation":"Excluding marginalised groups causes their perspectives to be underrepresented rather than overemphasised. Their experiences become less visible, making inequality appear less significant."}]}]}],"examCountdown":{"examDate":"2027-05-14","paperLabel":"Component 1"},"heading":"Sampling","title":"Sampling Flashcards","topbar":{"backHref":"/a-level/sociology/eduqas/flashcards"},"topicSidebar":"$L37"}]}],"topicId":"6425ae93-02d9-5106-9d29-36fd07491477","walled":"$L38"}],"$L39","$L3a"]