Sampling Methods and Techniques

The video tutorial explains different sampling techniques used in research to collect data from a population. It covers random sampling, where each member has an equal chance of selection; stratified sampling, which involves dividing the population into subgroups; cluster sampling, used for large populations or geographic areas; and systematic sampling, which selects members at regular intervals. The video also discusses sampling errors and convenience sampling, which can lead to biased results.

What is the main goal of using samples in research?

• To gather information about a larger population

• To save time and resources

• To collect data from the entire population

• To make assumptions about a variable

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

• Systematic sampling

• Cluster sampling

• Random sampling

• Stratified sampling

How can a researcher achieve random sampling?

• By selecting every nth member

• By using a random number table

• By choosing the most convenient members

• By dividing the population into clusters

In stratified sampling, how is the population divided?

• Into clusters

• Into random groups

• Into convenient groups

• Into subgroups with similar characteristics

What is a key characteristic of cluster sampling?

• All clusters have similar characteristics

• Only one cluster is used for sampling

• Entire clusters are randomly selected

• Clusters are selected based on convenience

When is cluster sampling particularly useful?

• When the population is small

• When a quick sample is needed

• When subgroups have similar characteristics

• When the population is large or geographically spread out

What is the main difference between cluster and stratified sampling?

• Stratified sampling is used for large populations

• Cluster sampling is always unbiased

• Stratified sampling is more cost-effective

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

In systematic sampling, how is the interval determined?

• By selecting the most convenient members

• By dividing the population size by the sample size

• By choosing every member

• By randomly selecting a starting point

What is a potential drawback of convenience sampling?

• It is time-consuming

• It is difficult to implement

• It often leads to biased results

• It requires complex calculations

What is a sampling error?

• A bias introduced by convenience sampling

• An error in calculating intervals

• The difference between sample results and population results

• A mistake in data collection