Sampling Procedure Quiz

What is the primary purpose of sampling in research?

• To select every member of the population

• To choose a representative subgroup from the population

• To select only a convenient group of individuals

• To ensure all groups are excluded from the study

Answer explanation

The primary purpose of sampling in research is to choose a representative subgroup from the population. This allows researchers to make inferences about the entire population without needing to study every individual.

Which sampling technique involves dividing a population into subgroups based on specific characteristics?

• Systematic Sampling

• Convenience Sampling

• Stratified Sampling

• Simple Random Sampling

Answer explanation

Stratified Sampling involves dividing a population into subgroups, or strata, based on specific characteristics. This technique ensures that each subgroup is adequately represented in the sample, making it the correct choice.

In systematic sampling, what is the 'nth' element?

• The first element chosen randomly

• The total population divided by the sample size

• The last element in the list

• The entire population

Answer explanation

In systematic sampling, the 'nth' element refers to the interval calculated by dividing the total population by the sample size. This helps in selecting elements at regular intervals from the population.

What does the term 'cluster' refer to in cluster sampling?

• Small, representative groups

• Homogeneous subgroups

• Randomly selected individuals only

• Geographically organized groups

Answer explanation

In cluster sampling, 'cluster' refers to geographically organized groups. This method involves dividing the population into clusters, often based on location, and then randomly selecting entire clusters for the sample.

Which type of sampling method uses a 'referral' system for hard-to-reach populations?

• Convenience Sampling

• Quota Sampling

• Snowball Sampling

• Cluster Sampling

Answer explanation

Snowball Sampling is a method where existing study subjects recruit future subjects from among their acquaintances, making it effective for reaching hard-to-reach populations.

Why is random selection important in probability sampling?

• It ensures the sample is easily accessible.

• It gives each member of the population an equal chance of being chosen.

• It allows the researcher to pick preferred participants.

• It reduces the need for data collection tools.

Answer explanation

Random selection is crucial in probability sampling because it ensures that every member of the population has an equal chance of being chosen, which helps to eliminate bias and allows for more accurate generalizations from the sample.

Which of the following describes a key feature of stratified sampling?

• Selecting participants only based on availability

• Dividing a population into subgroups with shared characteristics

• Choosing every individual in the population

• Randomly picking clusters in a particular area

Answer explanation

Stratified sampling involves dividing a population into subgroups based on shared characteristics, ensuring that each subgroup is represented in the sample. This method enhances the accuracy and reliability of the results.

In cluster sampling, why are clusters used instead of individual selections?

• Clusters represent heterogeneous groups.

• Clusters are easier to access and represent larger groups.

• Clusters are randomly selected to provide targeted analysis.

• All of the above.

Answer explanation

Clusters are used in sampling because they represent heterogeneous groups, are easier to access, and allow for larger group representation. Therefore, all the provided reasons are valid, making 'All of the above' the correct choice.

What differentiates quota sampling from stratified random sampling?

• Quota sampling selects participants based on convenience.

• Quota sampling requires dividing groups by geographic location.

• Quota sampling does not use random selection.

• Quota sampling only uses online surveys.

Answer explanation

Quota sampling does not use random selection, as it selects participants based on specific characteristics until a predetermined quota is met, unlike stratified random sampling which involves random selection from each subgroup.

When might a researcher choose snowball sampling?

• When working with a large, diverse population

• When targeting a specific subgroup with easy access

• When studying a hard-to-reach or hidden population

• When requiring random selection across subgroups

Answer explanation

Snowball sampling is ideal for studying hard-to-reach or hidden populations because it relies on existing subjects to recruit new participants, making it easier to access groups that are difficult to identify or reach.