Podcast
Questions and Answers
Which sampling design involves dividing the population into smaller groups known as clusters?
Which sampling design involves dividing the population into smaller groups known as clusters?
Why is simple random sampling considered time-consuming and expensive for large populations?
Why is simple random sampling considered time-consuming and expensive for large populations?
In which type of sampling do individuals who are easily accessible provide the data?
In which type of sampling do individuals who are easily accessible provide the data?
Which sampling design aims to ensure that the sample represents various subgroups within the population?
Which sampling design aims to ensure that the sample represents various subgroups within the population?
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What is a common disadvantage of cluster sampling compared to simple random sampling?
What is a common disadvantage of cluster sampling compared to simple random sampling?
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Which sampling method involves selecting every nth member of the population?
Which sampling method involves selecting every nth member of the population?
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In which sampling method are individuals selected based on chance encounters?
In which sampling method are individuals selected based on chance encounters?
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Which sampling method involves recruiting additional individuals through their connections?
Which sampling method involves recruiting additional individuals through their connections?
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Which sampling method is used when the researcher wants to gather in-depth information from a small sample?
Which sampling method is used when the researcher wants to gather in-depth information from a small sample?
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Which sampling method involves dividing the population into homogeneous subgroups based on certain characteristics?
Which sampling method involves dividing the population into homogeneous subgroups based on certain characteristics?
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Study Notes
Statistical Sampling Designs
Statistical sampling designs are essential in ensuring that the data collected is representative of the population being studied. There are several types of sampling designs, each with its own advantages and disadvantages. In this article, we will discuss the following types of sampling designs: simple random sampling, cluster sampling, convenience sampling, stratified sampling, systematic sampling, quota sampling, accidental sampling, purposive sampling, and the snowball technique.
Simple Random Sampling
Simple random sampling is a method where every member of the population has an equal chance of being selected for the sample. This approach ensures that the sample is representative of the population. However, it can be time-consuming and expensive, especially when the population is large.
Cluster Sampling
Cluster sampling involves dividing the population into smaller groups, known as clusters. A random sample of these clusters is then chosen, and all members of the selected clusters are included in the sample. This method can be more efficient than simple random sampling, as it reduces the number of individuals who need to be surveyed.
Convenience Sampling
Convenience sampling is a non-random method where data is collected from individuals who are easily accessible. This approach is often used in pilot testing or in situations where resources are limited.
Stratified Sampling
Stratified sampling involves dividing the population into homogeneous subgroups based on certain characteristics. A sample is then drawn from each stratum. This method can help ensure that the sample is representative of the population and can reduce sampling errors.
Systematic Sampling
Systematic sampling involves selecting every nth member of the population. This method is efficient and simple, but it can lead to systematic errors if the interval between the selected individuals is not random.
Quota Sampling
Quota sampling is a non-random method where a fixed number of individuals are selected to fit specific quotas based on certain characteristics. This approach can be useful in situations where a large sample is needed but resources are limited.
Accidental Sampling
Accidental sampling involves selecting individuals based on chance encounters. This method is not recommended, as it can lead to a non-representative sample and is prone to sampling errors.
Purposive Sampling
Purposive sampling involves selecting individuals who are experts in a particular field or who have specific characteristics relevant to the study. This method is useful when the researcher wants to gather in-depth information from a small sample.
Snowball Technique
The snowball technique involves starting with a small group of individuals and then recruiting others through their connections. This method is useful in studying hard-to-reach populations, such as those involved in illegal activities.
In conclusion, various statistical sampling designs are used to collect data from a sample that represents the population. Each design has its advantages and disadvantages, and the choice of design depends on the research question, resources, and the characteristics of the population being studied.
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Description
Explore different types of statistical sampling designs including simple random sampling, cluster sampling, convenience sampling, stratified sampling, systematic sampling, quota sampling, accidental sampling, purposive sampling, and the snowball technique. Learn about the advantages and disadvantages of each design and their applications in research.
