Sampling Methods and Techniques

The video tutorial by Minisetti covers various methods of sampling, dividing them into probability and non-probability sampling. Probability sampling includes simple random, stratified, systematic, and cluster sampling, each explained with examples. Non-probability sampling methods discussed are convenience, quota, snowball, and judgment sampling. The video provides a comprehensive understanding of each method, highlighting their characteristics and differences.

What are the two main categories of sampling methods?

• Convenience and judgment sampling

• Cluster and stratified sampling

• Probability and non-probability sampling

• Random and systematic sampling

In simple random sampling, what is the key characteristic of the selection process?

• Members are selected based on judgment

• Members are selected in a fixed order

• Members are selected based on convenience

• Each member has an equal chance of being selected

How does stratified sampling differ from simple random sampling?

• It selects members based on convenience

• It uses a fixed interval for selection

• It involves dividing the population into homogeneous groups

• It selects entire clusters as samples

What is the main feature of systematic sampling?

• Selection based on judgment

• Selection based on convenience

• Selection of entire clusters

• Selection of members at regular intervals

In cluster sampling, how are samples selected?

• Members are selected at regular intervals

• Entire clusters are randomly selected

• Members are selected based on judgment

• Members are selected based on convenience

What is the primary characteristic of convenience sampling?

• Selection of members who are easiest to reach

• Selection of members at regular intervals

• Selection of entire clusters

• Selection based on judgment

In quota sampling, how is the sample size determined?

• By pre-deciding a fixed number for each group

• By selecting members based on convenience

• By selecting members at regular intervals

• By selecting entire clusters

What is a key feature of snowball sampling?

• Participants refer other participants

• Members are selected based on convenience

• Members are selected at regular intervals

• Entire clusters are selected

When is snowball sampling particularly useful?

• When a fixed interval is required

• When members are easily accessible

• When a large sample size is needed

• When participants are hard to find

How does judgment sampling differ from other methods?

• It selects members based on convenience

• It selects entire clusters

• It selects members at regular intervals

• It relies on the researcher's expertise to select samples