Types of Sampling and Qualitative Sampling Methods: 18 MCQs with answers

Selecting a subset from a larger population for study

Sampling is the process of selecting a subset (sample) from a larger population so conclusions can be drawn about the whole.

Cluster sampling

Cluster sampling randomly selects whole clusters and studies everyone inside the selected clusters.

Convenience sampling

Convenience sampling relies on ease of access, so selection probabilities are unknown.

Every $k$-th unit from the list

Systematic sampling takes a random start and then every $k$-th unit.

Simple random sampling

Simple random sampling gives every member an equal chance, like a lottery draw.

True

**Probability sampling** allows researchers to calculate how far a sample estimate may deviate from the true population value, which is why it underpins statistical generalisation.

Snowball sampling

Snowball sampling relies on referrals from existing participants, useful for hidden populations.

Stratified samples within every group; cluster samples whole groups

Stratified sampling samples inside every stratum; cluster sampling selects whole clusters and studies everyone inside them.

Selection probabilities are known, so deviation can be estimated

Known selection probabilities allow researchers to calculate how far a sample estimate may deviate from the true population value.

Convenience sampling

Participants are chosen because they are easy to reach, which is convenience sampling regardless of crowd variety.

Theoretical sampling

Theoretical sampling is specific to grounded theory, where sampling, data collection and analysis loop together.

New interviews stop producing new themes

Saturation is reached when new data no longer generate new themes, guiding when to stop sampling.

Extreme case sampling

Extreme case sampling picks unusual cases to reveal the normal by contrast.

Maximum variation sampling

Maximum variation sampling selects a wide spread of cases to capture shared patterns across diversity.

Homogeneous sampling

Homogeneous sampling picks similar cases to describe a subgroup in depth.

Snowball sampling cannot support statistical generalisation to a population

Non-probability methods like snowball sampling do not allow calculation of selection probabilities, so population-level claims are not legitimate.

Snowball sampling does not allow for statistical generalization.

The primary flaw is that snowball sampling is a non-probability method, which means it does not provide a basis for generalizing findings to the entire population. The researcher cannot claim their results reflect the views of all undocumented workers.

To test and develop concepts based on data collected.

The rationale behind theoretical sampling is to select participants based on their potential to contribute to the development and refinement of an emerging theory, rather than to ensure diversity or representativeness.