Sampling Methods

Flashcards for key vocabulary and concepts related to sampling methods.

Simple Random Sampling

Chosen by chance, where each member has an equal chance of being selected.

Systematic Sampling

Individuals are selected at regular intervals from the sample frame.

Stratified Sampling

Population divided into subgroups/strata with similar characteristics, then equal samples are taken from each stratum.

Clustered Sampling

Subgroups of the population are used as a sampling unit, rather than individuals.

Convenience Sampling

Participants are selected based on availability and willingness to take part.

Quota Sampling

A quota of subjects of a specified type is recruited, ideally proportionally representing population characteristics.

Judgement Sampling

Relies on the researcher's judgment when choosing who to ask to participate.

Snowball Sampling

Existing subjects nominate further subjects known to them.

Voluntary Response Sample

Involves only those who want to participate in the sample.

Probability Sampling

Sampling methods where each member of the population has a known probability of being selected.

Non-probability Sampling

Sampling methods where the probability of selecting any particular member is unknown.

Bias in Sampling

Occurs when pre-agreed sampling rules are deviated from.

Bias in Sampling

Occurs when people in hard-to-reach groups are omitted.

Bias in Sampling

Occurs when selected individuals are replaced with others due to contact difficulties.

Bias in Sampling

Occurs when there are low response rates.

Bias in Sampling

Occurs when an out-of-date list is used as the sample frame.

Sample Frame

A list of all those within a population who can be sampled.

Sampling Error

The error caused by observing a sample instead of the whole population.

Selection Bias

Occurs when the sample selected is not representative of the population.

Response Rate

The percentage of individuals in the sample who participate in the study.

Sampling Unit

A single element or group of elements subject to selection in the sample.

Strata

Subgroups within a population that share similar characteristics.

Representativeness

Extent to which a sample accurately reflects the characteristics of the population.

Volunteer Bias

Systematic errors caused by differences between those who volunteer to participate and those who do not.

Bias

Systematic errors in sampling that can lead to inaccurate results.

MLO3

Design your own experiment and evaluate someone else's experimental design.

Experimental Design Evaluation

Assessing the strengths and weaknesses of an experiment's structure.

Appropriate Characteristics

Relevant traits useful for stratification in sampling.

Sampling Frame Knowledge

Understanding the available resources to accurately select a sample.

Increased Risk of Bias

Potential downside of cluster sampling due to non-representative subgroups.

Increased Sampling Error

Higher variability in results due to the sample not perfectly mirroring the population.

Significant Bias

Prone results from convenience sampling.

Proportional Representation

Ideal characteristic of quota sampling, reflecting demographics.

Effectiveness

Benefit of snowball sampling when a sampling frame is hard to identify.

Deviation

Departing from the established sampling process rules, which results in sampling errors.