Multistage sampling is a complex probability sampling method that involves dividing a population into multiple stages or levels of sampling, with each stage introducing a new sampling level. This method is often used when a complete list of the entire population is unavailable or impractical to obtain.
Multistage sampling is a way to balance the need for representativeness with logistical considerations, making it more feasible for large and diverse populations.
Advantages of multistage sampling include:
- Efficiency: Multistage sampling is often more efficient than single-stage sampling, especially for large and diverse populations.
- Cost reduction: It can be more cost-effective than attempting to sample the entire population, especially when a comprehensive list is unavailable.
- Logistical feasibility: Particularly useful when access to the entire population is difficult or impractical.
- Balanced representation: Multistage sampling aims to achieve a balanced and representative sample by incorporating different sampling levels.
Some challenges of multistage sampling are:
- Complexity: Multistage sampling can be more complex to plan and implement than more straightforward sampling methods.
- Potential for bias: If the clusters or stages are not selected randomly, there is a risk of introducing bias into the sample.
- Loss of precision: Depending on the specific design, multistage sampling may result in less precision compared to some single-stage sampling methods.
- Need for comprehensive sampling frame: Even though a complete list of the entire population is not required, a comprehensive sampling frame for each stage is still necessary.
List of recommended resources #
For a broad overview #
This article by Iris Shimizu illustrates how to apply multistage sampling in terms of two-stage samples. The article also discusses some procedures for determining optimum sample sizes and allocation between primary and secondary sampling stages along with methods for controlling the effects of variation in sizes of primary sampling units on sampling errors.
This blog on Scribbr by Pritha Bhandari gives an overview of multistage cluster sampling, simply known as multistage sampling. It differentiates between single-stage and multistage sampling as well as delineates the various stages of multistage sampling.
For in depth understanding #
This study module by eGyanKosh gives an in-depth overview of multistage sampling. It illustrates the theory by taking several examples for the easy understanding of the reader along with providing an explanation of the estimations to be done in such situations.
Chapter 10 of this book by Ravindra Singh and Naurang Singh Mangat gives an in-depth understanding of multistage sampling or two-stage sampling and how it provides an edge over cluster sampling procedures.
Case study #
This study aims to estimate the forest volume efficiently and accurately by sampling and analyzing the existing forest survey data. Multistage sampling is used. The study has selected certain samples from the collection of second-stage samples through stratified sampling to form the third-stage sampling units.
This case study by Farzaneh Yarahmadi aims to measure viewers’ perception toward TV commercials. It employs a multistage sampling technique, which divides large populations into smaller groups to make the sampling process more practical.