![]() Although larger samples provide more statistical certainty, they also cost more and require far more work. Next, you need to decide how large your sample size will be. Example: PopulationIn the American Community Survey, the population is all 128 million households who live in the United States (including households made up of citizens and non-citizens alike). It’s important to ensure that you have access to every individual member of the population, so that you can collect data from all those who are selected for the sample. ![]() Start by deciding on the population that you want to study. There are 4 key steps to select a simple random sample. You divide the sample into clusters that approximately reflect the whole population, and then choose your sample from a random selection of these clusters. Cluster sampling is appropriate when you are unable to sample from the entire population.You split your population into strata (for example, divided by gender or race), and then randomly select from each of these subgroups. Stratified sampling is appropriate when you want to ensure that specific characteristics are proportionally represented in the sample.It can also be used when you don’t have a complete list of the population. Systematic samplinginvolves choosing your sample based on a regular interval, rather than a fully random selection.In some cases, it might be more appropriate to use a different type of probability sampling: Simple random sampling works best if you have a lot of time and resources to conduct your study, or if you are studying a limited population that can easily be sampled. You have the time and resources to collect data from the necessary sample size.You can contact or access each member of the population if they are selected. ![]()
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