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Last updated on 2024年6月17日

How do you use stratified, cluster, and multistage sampling methods in your survey research?

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Survey research is a powerful way to collect data from a large population of interest, but it also involves some challenges and decisions. One of the most important decisions is how to select a sample of respondents that represents the population and minimizes sampling error. In this article, you will learn how to use three common sampling methods in your survey research: stratified, cluster, and multistage sampling.

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Ankesh Saxena

30x Top Voices | Podcaster @ The Collaborative Canvas | Founder - TRP Magazine | Learn and Grow | Market Research |…
Ding Wang

Program Developer | Helping innovators apply LLM in their survey and reporting projects

1 What is stratified sampling?

Stratified sampling is a sampling method that divides the population into smaller groups or strata based on some relevant characteristic, such as age, gender, income, or education. Then, a simple random sample is drawn from each stratum, proportionally or disproportionately, depending on the research objective. Stratified sampling can help you increase the precision and accuracy of your estimates, reduce the sampling error, and ensure the representation of different subgroups in your sample.

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Ankesh Saxena

30x Top Voices | Podcaster @ The Collaborative Canvas | Founder - TRP Magazine | Learn and Grow | Market Research | Personal Branding | Consumer Insights | Sales | ESOMAR member | MRSI Member |
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In survey research, use stratified sampling to ensure representation by dividing the population into homogeneous subgroups and sampling each. Use cluster sampling by dividing the population into clusters, then randomly selecting some clusters and surveying all members within them. Apply multistage sampling by combining these methods: first, selecting clusters, then stratifying within them, and finally sampling individuals from these strata, ensuring a comprehensive, representative sample efficiently.

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2 What is cluster sampling?

Cluster sampling is a sampling method that divides the population into larger groups or clusters that are geographically or administratively defined, such as regions, districts, schools, or households. Then, a simple random sample of clusters is selected, and all the units within the selected clusters are included in the sample. Cluster sampling can help you reduce the cost and complexity of your survey, especially when the population is widely dispersed or hard to access.

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3 What is multistage sampling?

Multistage sampling is a sampling method that combines cluster sampling and stratified sampling in two or more stages. For example, you can first select a random sample of regions, then a random sample of districts within each region, then a random sample of schools within each district, and finally a random sample of students within each school. Multistage sampling can help you balance the trade-offs between precision, cost, and feasibility of your survey.

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4 How to choose a sampling method?

The choice of a sampling method depends on several factors, such as the research question, the population size and structure, the availability of a sampling frame, the budget and time constraints, and the desired level of confidence and precision. Generally, stratified sampling is preferred when the population is heterogeneous and the strata are relevant for the analysis. Cluster sampling is preferred when the population is homogeneous and the clusters are convenient for the data collection. Multistage sampling is preferred when the population is complex and the sampling frame is incomplete or unavailable.

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Ding Wang

Program Developer | Helping innovators apply LLM in their survey and reporting projects
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This is a great summary of how to choose the right sampling method! I'd add that sometimes you might even use a combination of methods. For example, you could start with stratified sampling to make sure you represent different groups, and then use cluster sampling within each group to make your data collection easier. It all comes down to finding the best fit for your specific research goals and resources.

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5 How to calculate the sample size?

The sample size is the number of units or respondents that you need to include in your sample to achieve a certain level of confidence and precision in your estimates. The sample size depends on four factors: the margin of error, the confidence level, the population variance, and the design effect. The margin of error is the maximum difference between the sample estimate and the true population value. The confidence level is the probability that the sample estimate falls within the margin of error. The population variance is the degree of variability or diversity in the population. The design effect is the factor that adjusts the sample size for the effect of using a complex sampling method, such as stratified, cluster, or multistage sampling.

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6 How to analyze the survey data?

The analysis of the survey data involves applying appropriate statistical techniques to summarize, compare, and infer from the data. The analysis should take into account the sampling method and the sample weights that reflect the probability of selection and the nonresponse of each unit in the sample. The analysis should also account for the sampling error and the confidence intervals that indicate the range of possible values for the population parameters. The analysis should also consider the sources of nonsampling error, such as measurement error, coverage error, or processing error, that may affect the validity and reliability of the data.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you use stratified, cluster, and multistage sampling methods in your survey research?

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1 What is stratified sampling?

2 What is cluster sampling?

3 What is multistage sampling?

4 How to choose a sampling method?

5 How to calculate the sample size?

6 How to analyze the survey data?

7 Here’s what else to consider

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