Strengths and weaknesses of various types of sampling

Full Answer Section

    Stratified sampling | * Ensures that all subgroups of the population are represented in the sample * More efficient than simple random sampling for large populations | * Can be difficult to identify and define subgroups of the population * Requires knowledge of the population | A researcher wants to survey all adults in the United States about their political views. The researcher stratifies the sample by age, gender, and race to ensure that all of these subgroups are represented. Cluster sampling | * More efficient than simple random sampling for large populations * Easier to implement than other types of sampling | * Less precise than other types of sampling * May be biased if the clusters are not representative of the population | A researcher wants to survey all adults in Los Angeles about their attitudes towards public transportation. The researcher divides Los Angeles into clusters of neighborhoods and randomly selects a sample of clusters. All adults in the selected clusters are invited to participate in the survey. Convenience sampling | * Easy to implement * Inexpensive | * Highly biased * Not representative of the population | A researcher wants to survey people about their opinions on a new product. The researcher stands outside of a grocery store and asks the first 100 people who enter the store to participate in the survey. Purposive sampling | * Can be used to target specific subgroups of the population * Can be used to collect data from difficult-to-reach populations | * Can be biased if the researcher is not careful to select a representative sample | A researcher wants to survey people who have experienced homelessness. The researcher works with a local homeless shelter to recruit participants for the survey. Snowball sampling | * Can be used to collect data from difficult-to-reach populations * Relatively inexpensive | * Can be biased if the initial sample is not representative of the population | A researcher wants to survey drug users. The researcher recruits a few drug users to participate in the survey and then asks them to refer other drug users to the study. Which type of sampling is best depends on the specific research question and the resources available. Researchers should carefully consider the strengths and weaknesses of each type of sampling before selecting a method. Here are two examples of how different types of sampling can be used in research: Example 1: A researcher wants to study the effects of a new drug on the treatment of depression. The researcher uses stratified sampling to ensure that the sample is representative of the population in terms of age, gender, and severity of depression. The researcher then randomly assigns participants to either receive the new drug or a placebo. Example 2: A researcher wants to study the attitudes of homeless people towards social services. The researcher uses purposive sampling to recruit participants from a local homeless shelter. The researcher then interviews the participants about their experiences with social services. In both of these examples, the researcher used a type of sampling that was appropriate for the research question and the resources available. By carefully selecting a sampling method, the researchers were able to collect data that was representative of the population of interest.  

Sample Answer

  Type of sampling | Strengths | Weaknesses | Examples ---|---|---|---|---| Simple random sampling | * Unbiased * Easy to implement | * Requires a complete list of all members of the population * May be inefficient for large populations | A researcher wants to survey all students at a university about their opinions on a new student center. The researcher creates a list of all students at the university and randomly selects 1,000 students to participate in the survey.