![]() In multistage sampling, you always go from higher-level to lower-level clusters at each stage. Instead, you decide to use a multistage sampling method to collect a representative sample of participants. In addition, collecting data from a sample of individuals across the state would be very difficult, costly, and time-intensive. If you’re unable to access a complete sampling frame, you can’t use single-stage probability sampling from the whole population. Research exampleYour population is all students aged 13–19 registered at schools in your state. That’s why this method is useful for collecting data from large, dispersed populations. But in multistage sampling, you don’t need a sampling frame that lists every member of the population. Like in single-stage sampling, you start by defining your target population. At the last step, you only select some members of each cluster for your sample. ![]() At each subsequent stage, you further divide up those selected clusters into smaller clusters, and repeat the process until you get to the last step. In multistage sampling, you divide the population into clusters and select some clusters at the first stage. Multistage sampling is often considered an extended version of cluster sampling. ![]() Multistage sampling often involves a combination of cluster and stratified sampling. You use random selection to choose participants from each stratum separately to ensure that you have enough participants from each socioeconomic level in your sample. Single-stage stratified samplingYou divide the sampling frame up into three strata of different socioeconomic status. You select some members from each stratum so that all groups are represented in your sample. Every unit or member of the population is placed in one stratum. In stratified sampling, the population is divided into strata, which are often based on demographic characteristics such as race, gender or socioeconomic status. You select 15 clusters using random selection and include all members from those clusters into your sample. Single-stage cluster samplingYou divide the sampling frame up based on geography, and you end up with 98 area-based clusters of students. In single-stage cluster sampling, you randomly select some of the clusters for your sample and collect data from everyone within those clusters in one stage. In cluster sampling, the population is divided into clusters, which are usually based on geography (e.g., cities or states) or organization (e.g., schools or universities). In cluster sampling and stratified sampling, you divide up your population into groups that are mutually exclusive and exhaustive. You can use simple random, systematic, stratified, or cluster sampling methods to select a probability sample from your sampling frame. To obtain this list, you can reach out to the state education department or to each school individually to request a list of students. The sampling frame for your study is a list of all teenage students registered at schools within the state. Your target population is students aged between 13 and 19, and your ideal sample size is 7500 students. ![]() Sampling frameYou’re surveying students in your state in a large-scale study. It should be as complete as possible, so that your sample accurately reflects your population. In single-stage probability sampling, you start with a sampling frame, which is a list of every member in the entire population. But for external validity, or generalizability, it’s best to use probability sampling methods, which allow for stronger statistical inferences. You can use either probability or non-probability sampling methods in single-stage and multi-stage sampling. You can take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from. ![]() In multistage sampling, you divide the population into smaller and smaller groupings to create a sample using several steps. In single-stage sampling, you divide a population into units (e.g., households or individuals) and select a sample directly by collecting data from everyone in the selected units.
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