Sampling Methods and Their Characteristics

The video tutorial explains the concept of sampling, a method used by researchers to collect data from a population. It covers four main sampling techniques: random sampling, stratified sampling, cluster sampling, and systematic sampling. Each method is explained with examples, highlighting their advantages and potential biases. The video also touches on sampling errors and convenience sampling, emphasizing the importance of choosing the right method to ensure unbiased results.

What is the primary goal of using samples in research?

• To gather information about a variable or variables from a larger population

• To ensure all members of a population are included

• To collect data from the entire population

• To save time and resources

Which method ensures every member of a population has an equal chance of being selected?

• Stratified sampling

• Random sampling

• Cluster sampling

• Systematic sampling

In stratified sampling, how is the population divided?

• Into equal parts

• Into subgroups with similar characteristics

• Into clusters

• Into random groups

What is a key characteristic of cluster sampling?

• Entire clusters are randomly selected

• All clusters have similar characteristics

• Clusters are always geographically based

• Only a few members from each cluster are selected

What is the main difference between cluster and stratified sampling?

• Stratified sampling is used for large populations

• Cluster sampling is always more accurate

• Stratified sampling is more cost-effective

• Cluster sampling uses entire clusters, while stratified sampling uses subgroups

Which sampling method is most suitable for a large geographic area?

• Stratified sampling

• Cluster sampling

• Random sampling

• Systematic sampling

In systematic sampling, how are members selected?

• Based on convenience

• Randomly from each subgroup

• At regular intervals from a random starting point

• By selecting entire clusters

What is a potential drawback of convenience sampling?

• It requires complex calculations

• It often leads to biased results

• It is time-consuming

• It is difficult to implement

What is a sampling error?

• A mistake in data collection

• The difference between sample results and population results

• An error in calculating sample size

• A bias introduced by the researcher

What is the interval number in systematic sampling?

• The total population size

• The number of clusters

• The number of subgroups

• The regular interval at which members are selected