Reasons for Taking a Sample and Basic Sampling Methods

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Questions and Answers

What is a key characteristic of simple random sampling?

Guarantees an equal chance of selection for all population members (correct)

Unequal probability of selection for population members

Random digit dialing only

Requires a complete listing of the population

Which method can be used in simple random sampling to select population members?

Purposive sampling

Random spot-checking

Random digit dialing (correct)

Convenience sampling

What is a disadvantage of simple random sampling?

Can lead to biased samples

Does not require unique identification of population members

Needs complete listings of the population (correct)

Does not guarantee equal probability of selection for all members

In simple random sampling, what ensures that each member of the population has an identical chance of being selected into the sample?

Random selection procedure Signup and view all the answers

Which type of sampling requires uniquely identifying and labeling each and every population member?

Simple random sampling Signup and view all the answers

What is the primary advantage of simple random sampling?

Results in a representative sample Signup and view all the answers

What is the main characteristic of nonprobability sampling methods?

Selection is not based on fairness, equity, or equal chance Signup and view all the answers

In which type of sampling is selection based on convenience?

Convenience sampling Signup and view all the answers

What is a possible error that can occur in convenience sampling due to the sample frame?

Sample frame error Signup and view all the answers

Which type of sampling method requires respondents to provide the names of other potential respondents?

Chain referral sampling Signup and view all the answers

In chain referral sampling, what is the likelihood of less well-known or disliked individuals being selected?

Low probability Signup and view all the answers

Which type of sampling method is based on selecting samples by dividing the population into clusters and then choosing clusters at random?

Cluster sampling Signup and view all the answers

What is the main characteristic of purposive sampling?

Requires a judgment or an 'educated guess' to select sample members Signup and view all the answers

How are quota samples different from purposive samples?

Purposive samples use quotas for each class of respondent Signup and view all the answers

In quota sampling, how are the sizes of quotas determined?

By the researcher's judgment of the relative size of each class Signup and view all the answers

What is a key drawback of purposive sampling?

Subjectivity and convenience affect sample selection Signup and view all the answers

Which sampling method involves setting quotas based on specific characteristics?

Quota sampling Signup and view all the answers

What is a common purpose of quota sampling in research studies?

To guarantee specific proportions of different respondent classes in the sample Signup and view all the answers

What is the greatest danger in cluster sampling?

Cluster specification error Signup and view all the answers

In which type of sampling method is the geographic area divided into clusters?

Cluster sampling Signup and view all the answers

What is the main difference between one-step area sample and two-step area sample?

The method of selecting individuals within clusters Signup and view all the answers

Which sampling method separates the population into different subgroups before sampling?

Stratified sampling Signup and view all the answers

What is the purpose of stratified sampling?

To ensure representation of all subgroups Signup and view all the answers

When using cluster sampling, what might happen if the clusters are not homogeneous?

Cluster specification error will arise Signup and view all the answers

What is the primary reason for using systematic sampling?

To ensure every member of the population has an equal chance of being included in the sample Signup and view all the answers

In cluster sampling, how are the clusters formed?

By dividing the population into groups based on similar characteristics Signup and view all the answers

What distinguishes simple random sampling from other probability sampling methods?

It ensures an equal probability of selection for each member of the population Signup and view all the answers

Why is it important to have a sample frame in sampling?

To ensure that every individual in the population has a known chance of being selected Signup and view all the answers

Which type of sampling is more prone to human error and biases?

Non-probability sampling Signup and view all the answers

What is a characteristic of stratified sampling?

Including only specific strata within the population in the sample Signup and view all the answers

Study Notes

Simple Random Sampling

A key characteristic of simple random sampling is that every member of the population has an equal chance of being selected.
Random number tables or random number generators can be used to select population members.
A disadvantage of simple random sampling is that it can be time-consuming and expensive.
The randomization process ensures that each member of the population has an identical chance of being selected into the sample.
Simple random sampling is the type of sampling that requires uniquely identifying and labeling each and every population member.
The primary advantage of simple random sampling is that it provides a representative sample of the population.

Nonprobability Sampling

The main characteristic of nonprobability sampling methods is that the selection of respondents is not based on randomization.
Convenience sampling is a type of nonprobability sampling where selection is based on convenience.
A possible error that can occur in convenience sampling due to the sample frame is that the sample may not be representative of the population.
Chain referral sampling is a type of nonprobability sampling where respondents provide the names of other potential respondents.
In chain referral sampling, the likelihood of less well-known or disliked individuals being selected is low.

Cluster Sampling

Cluster sampling is a type of sampling method that involves selecting samples by dividing the population into clusters and then choosing clusters at random.
The main characteristic of cluster sampling is that the population is divided into clusters, and then a random sample of clusters is selected.
The greatest danger in cluster sampling is that the clusters may not be homogeneous, leading to an unrepresentative sample.

Purposive Sampling

The main characteristic of purposive sampling is that the researcher selects the sample based on their judgment and expertise.
Quota samples are different from purposive samples in that they involve setting quotas based on specific characteristics.
The sizes of quotas are determined by the researcher's judgment and expertise.
A key drawback of purposive sampling is that it may not be representative of the population.

Quota Sampling

Quota sampling involves setting quotas based on specific characteristics, and then selecting a sample that meets those quotas.
A common purpose of quota sampling in research studies is to ensure that the sample is representative of the population.
Quota sampling is a type of nonprobability sampling where the sample is selected based on the researcher's judgment and expertise.

Stratified Sampling

Stratified sampling involves separating the population into different subgroups before sampling.
The purpose of stratified sampling is to ensure that the sample is representative of the population.
A characteristic of stratified sampling is that the population is divided into subgroups or strata, and then a random sample is selected from each stratum.

Systematic Sampling

The primary reason for using systematic sampling is to ensure that the sample is selected at regular intervals from the population.
Systematic sampling involves selecting every nth member of the population, where n is the sampling interval.
A characteristic of systematic sampling is that it is a type of probability sampling where every member of the population has an equal chance of being selected.

Cluster and Area Sampling

Cluster sampling is a type of sampling method that involves dividing the population into clusters and then choosing clusters at random.
In cluster sampling, the clusters are formed by dividing the population into smaller groups or clusters.
Area sampling is a type of cluster sampling where the geographic area is divided into clusters.
One-step area sampling involves selecting a sample from the entire population, whereas two-step area sampling involves selecting a sample from a subset of the population.

Importance of Sampling

It is important to have a sample frame in sampling because it ensures that the sample is representative of the population.
Nonprobability sampling is more prone to human error and biases.
Simple random sampling is distinguished from other probability sampling methods by the fact that every member of the population has an equal chance of being selected.

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Description

Learn about the reasons behind taking a sample, such as practical considerations and the inability to analyze large amounts of data from a census. Explore basic sampling methods, including probability samples where members of the population have a known chance of being selected.