How is a sample selected?

A sample is selected from a larger population using various methods, each designed to ensure the sample is representative and suitable for the research question. The choice of method depends on factors like the population size, desired accuracy, and available resources.

Here's a breakdown of common sampling techniques:

1. Simple Random Sampling

• Definition: Every member of the population has an equal chance of being selected.
• Process:
  1. Assign a unique number to each individual in the population.
  2. Use a random number generator or a table of random numbers to select individuals for the sample.
• Example: Imagine you have a list of 100 students and want to select a random sample of 20. You would assign each student a number from 1 to 100, then use a random number generator to pick 20 unique numbers. The students corresponding to those numbers would be your sample.
• Advantage: Simple and unbiased.
• Disadvantage: Can be time-consuming and may not be representative if the population has subgroups.

2. Systematic Sampling

• Definition: Selecting every kth member of the population after a random start.
• Process:
  1. Calculate the sampling interval (k) by dividing the population size by the desired sample size.
  2. Randomly select a starting point between 1 and k.
  3. Select every kth individual from that starting point.
• Example: If you have a population of 1000 and want a sample of 100, k would be 10. If your random start is 3, you would select individuals numbered 3, 13, 23, 33, and so on.
• Advantage: Easy to implement.
• Disadvantage: Can be biased if there is a pattern in the population data.

3. Stratified Sampling

• Definition: Dividing the population into subgroups (strata) based on shared characteristics and then randomly sampling from each stratum.
• Process:
  1. Divide the population into strata (e.g., by age, gender, income).
  2. Determine the sample size for each stratum, either proportionally or disproportionately to the stratum size.
  3. Use simple random sampling within each stratum to select the sample.
• Example: If you want to survey students at a university and want to ensure representation from each major, you would divide the student population into strata based on major and then randomly sample from each major.
• Advantage: Ensures representation of all subgroups.
• Disadvantage: Requires knowledge of the population's composition.

4. Cluster Sampling

• Definition: Dividing the population into clusters (groups) and then randomly selecting entire clusters to include in the sample.
• Process:
  1. Divide the population into clusters (e.g., geographic areas, schools).
  2. Randomly select a number of clusters.
  3. Include all individuals within the selected clusters in the sample.
• Example: If you want to survey households in a city, you could divide the city into blocks (clusters), randomly select a few blocks, and then survey every household within those selected blocks.
• Advantage: Cost-effective when the population is geographically dispersed.
• Disadvantage: Can have higher sampling error than other methods if clusters are not homogeneous.

5. Convenience Sampling

• Definition: Selecting individuals who are easily accessible to the researcher.
• Process: Selecting participants based on their availability and willingness to participate.
• Example: Surveying students in a classroom or people walking by in a shopping mall.
• Advantage: Easy and inexpensive.
• Disadvantage: Highly susceptible to bias and may not be representative of the population. This method should be avoided when possible.

Summary Table:

In conclusion, the method of selecting a sample varies greatly depending on the goals of the study, resources available, and characteristics of the population being studied. Researchers must carefully consider these factors to choose the most appropriate method for obtaining a representative and unbiased sample.