Stratified Random Sampling

Definition

Stratified random sampling is a probability sampling technique in which the population is first divided into mutually exclusive and exhaustive subgroups or strata, and then a random sample is drawn from each stratum. This method ensures that the final sample is representative of the overall population, allowing for more precise estimates and comparisons between subgroups.

5 Must Know Facts For Your Next Test

1. Stratified random sampling is particularly useful when the population is heterogeneous, and the researcher wants to ensure that specific subgroups are adequately represented in the sample.
2. The strata are created based on one or more characteristics of the population, such as age, gender, income level, or geographic location.
3. Stratified random sampling can lead to more precise estimates and reduce the standard error of the sample mean compared to simple random sampling.
4. Proportionate stratification ensures that the relative size of each stratum in the sample is the same as in the population, which can improve the representativeness of the sample.
5. Stratified random sampling can be more time-consuming and resource-intensive than simple random sampling, as it requires the researcher to identify and sample from each stratum.

Review Questions

• Explain the purpose and benefits of using stratified random sampling in a data collection experiment.
  • The primary purpose of stratified random sampling in a data collection experiment is to ensure that the sample is representative of the underlying population. By dividing the population into homogeneous subgroups (strata) and then randomly selecting participants from each stratum, the researcher can obtain a more precise and accurate estimate of the population parameters. This approach is particularly beneficial when the population is heterogeneous, as it helps to reduce the standard error of the sample mean and allows for more meaningful comparisons between different subgroups within the population.
• Describe the process of implementing stratified random sampling in a data collection experiment, including the steps involved in creating the strata and selecting the sample.
  • To implement stratified random sampling in a data collection experiment, the researcher must first identify the relevant characteristics or variables that will be used to define the strata. These strata should be mutually exclusive and exhaustive, meaning that each individual in the population belongs to one and only one stratum. Once the strata have been established, the researcher then selects a random sample from each stratum, typically using a simple random sampling method. The size of each stratum's sample can be proportional to the size of the stratum in the population (proportionate stratification) or can be based on other considerations, such as the desired level of precision or the need to ensure adequate representation of smaller subgroups.
• Analyze the potential advantages and disadvantages of using stratified random sampling compared to other sampling techniques, such as simple random sampling or cluster sampling, in the context of a data collection experiment.
  • Compared to simple random sampling, stratified random sampling can offer several advantages in a data collection experiment. By ensuring that the sample is representative of the population's key subgroups, stratified random sampling can lead to more precise estimates and allow for more meaningful comparisons between different segments of the population. This can be particularly useful when the population is heterogeneous, and the researcher wants to explore potential differences between subgroups. However, stratified random sampling can also be more time-consuming and resource-intensive, as it requires the researcher to identify and sample from each stratum. In contrast, cluster sampling may be more efficient when the population is geographically dispersed, but it may not provide the same level of control over the representativeness of the sample. Ultimately, the choice of sampling technique will depend on the specific goals and constraints of the data collection experiment, as well as the characteristics of the population being studied.

Related terms

Simple Random Sampling: A probability sampling method where each individual in the population has an equal chance of being selected for the sample.

Cluster Sampling: A probability sampling technique where the population is divided into mutually exclusive and exhaustive clusters, and a random sample of clusters is selected.

Proportionate Stratification: A stratified random sampling method where the size of each stratum's sample is proportional to the size of the stratum in the population.