Simple random sampling

Full Answer Section

b) Systematic Sampling:

• Definition: You select every kth member of the population after a random starting point. For example, if you want to sample 100 out of 1000 people, you might randomly choose a number between 1 and 10 (say, 3). Then, you would select the 3rd person, the 13th person, the 23rd person, and so on.

• Appropriate Situations: Useful when the population is large and you have a list or ordered arrangement. For example, inspecting every 10th product coming off an assembly line for defects.

• Pros: Simpler to implement than simple random sampling, can be more efficient.

• Cons: Can be biased if there's a periodic pattern in the population that aligns with your sampling interval.

(c) Systematic Random Sampling (This is often used interchangeably with Systematic Sampling):

• Definition: This is essentially the same as systematic sampling. The emphasis on "random" usually refers to the random selection of the starting point.

• Appropriate Situations: Same as systematic sampling.

• Pros: Same as systematic sampling.

• Cons: Same as systematic sampling.

(d) Haphazard Sampling (or Convenience Sampling):

• Definition: Selecting individuals who are easily accessible or readily available. For example, surveying people you meet in a shopping mall.

• Appropriate Situations: Often used for exploratory research or when other sampling methods are not feasible due to time or resource constraints. For example, a professor doing a quick poll of students in their class.

• Pros: Easy and inexpensive.

• Cons: Highly biased, not representative of the population, results cannot be generalized. Should be used with extreme caution.

(e) Block Sampling (or Cluster Sampling):

• Definition: The population is divided into clusters (blocks), and then a random sample of clusters is selected. All members within the selected clusters are included in the sample. For example, if you want to survey opinions in a city, you might randomly select a few city blocks and interview everyone in those blocks.

• Appropriate Situations: Useful when the population is geographically dispersed or when it's more cost-effective to sample groups than individuals. For example, surveying farmers in a state by randomly selecting counties (blocks) and then surveying all farmers within those counties.

• Pros: Can be more efficient and less expensive than other methods, especially for large, dispersed populations.

• Cons: Can be biased if the clusters are not representative of the population.

Is it ever a good idea to use more than one of these?

Yes, combining sampling methods can sometimes be beneficial. Here are a couple of examples:

• Stratified Random Sampling with Simple Random Sampling: You could divide the population into strata (groups) based on some characteristic (e.g., age, gender) and then use simple random sampling to select individuals within each stratum. This ensures representation from all subgroups.

• Cluster Sampling with Simple Random Sampling: You could first use cluster sampling to select a few areas, and then use simple random sampling to select individuals within each of the chosen clusters. This helps manage large, geographically spread populations.

Using multiple methods can address the limitations of individual methods and provide a more robust and representative sample. However, it also adds complexity to the sampling process. The choice of which methods to combine depends on the specific research question, the characteristics of the population, and available resources.

Sample Answer

Let's define these sampling methods and discuss their appropriate uses:

(a) Simple Random Sampling:

• Definition: Every member of the population has an equal and independent chance of being selected. It's like drawing names out of a hat.

• Appropriate Situations: Best used when the population is relatively homogeneous (similar characteristics) and you have a complete list of all members. For example, randomly selecting 100 students from a university's student directory to survey about their satisfaction with campus dining.

• Pros: Unbiased, easy to understand.

• Cons: Can be difficult to implement if the population is large or dispersed. May not be representative if there are subgroups within the population.