Exploring Random Sampling Methods

The video tutorial covers various sampling methods, including simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multistage sampling. Each method is explained with examples, highlighting their differences and applications. The tutorial emphasizes the importance of choosing the right sampling method based on the research context and objectives.

What is the main characteristic of a simple random sample?

• The sample is taken from specific locations.

• The population is divided into subgroups.

• Every individual has an equally likely chance of being selected.

• A pattern is used to select the sample.

In a stratified random sample, how is the population divided?

• By specific characteristics that make subgroups different.

• By random selection.

• By a systematic pattern.

• By geographical location.

Why might seniors and freshmen be placed in different strata in a high school survey?

• Because they are different ages.

• Because they have different schedules.

• Because they might have different opinions about student parking.

• Because they are in different locations.

What is a key difference between stratified and cluster sampling?

• Stratified sampling selects individuals based on location.

• Stratified sampling uses a pattern to select individuals.

• Cluster sampling selects entire subgroups.

• Cluster sampling divides the population by specific characteristics.

In cluster sampling, why are groups formed?

• To ensure every individual has an equal chance of being selected.

• To follow a systematic pattern.

• To make the sampling process easier.

• To identify different characteristics within the population.

What is the main feature of a systematic random sample?

• Individuals are selected based on specific characteristics.

• The population is divided into subgroups.

• Entire subgroups are selected.

• A random starting point and a pattern are used to select the sample.

How is the starting point chosen in a systematic random sample?

• By following a specific pattern.

• By selecting entire subgroups.

• By dividing the population into subgroups.

• By using a random number generator.

What is multistage sampling?

• Dividing the population into subgroups.

• Using a random number generator to select the sample.

• Selecting entire subgroups.

• Combining different sampling methods.

Which of the following is an example of multistage sampling?

• Selecting entire subgroups based on location.

• Breaking the population into hallways and then selecting every third homeroom.

• Dividing the population into subgroups and selecting individuals from each.

• Selecting every fourth person after a random starting point.

What is the purpose of combining different sampling methods?

• To make the sampling process easier.

• To ensure every individual has an equal chance of being selected.

• To follow a systematic pattern.

• To improve the accuracy and efficiency of the sample.