Sampling process

Data Science, AI and ML
#1

  1. Define target population: Based on the objective of the study, clearly scope the target population. For instance, if we are studying a regional election, the target population would be all people who are domiciled in the region that are eligible to vote.

  2. Define Sampling Frame: The sampling frame is the approachable members from the overall population. In the above example, the sampling frame would consist of all the people from the population who are in the state and can participate in the study.

  3. Select Sampling Technique: Now that we have the sampling frame in place, we want to select an appropriate sampling technique. We will discuss this in detail in the next section.

  4. Determine Sample Size: To ensure that we have an unbiased sample, free from errors and that closely represents the whole population, our sample needs to be of an appropriate size. What is an appropriate size? Well, this is dependent on factors like the complexity of the population under study, the researcher’s resources and associated constraints. Also, it’s important to keep in mind that not all individuals we approach for the study will respond. Researchers like Bartlett et al. suggest that we should increase the number of individuals we approach initially, by as much as 50%, to factor in the non-response rate.

  5. Collect the Data: Data collection is critical to solving the business case. We should attempt to ensure that we don’t have too many empty fields in our data, and we document the reasons in cases where the data is missing. This helps in analysis, as this gives us perspective on how to treat the missing data when we perform analysis.

  6. Assess response rate: It is important to closely monitor the response rate to ensure you make timely changes to your sample collection approach and ensure you achieve your determined sample collection.