Stratified random sampling

Definition

Stratified random sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, and then randomly selecting samples from each of these groups. This technique ensures that each subgroup is adequately represented in the overall sample, making the findings more reflective of the entire population. It's particularly useful in survey methods where researchers want to ensure representation across various demographics, characteristics, or other important variables.

5 Must Know Facts For Your Next Test

1. Stratified random sampling helps reduce sampling bias by ensuring that all relevant subgroups are included in the sample.
2. The strata can be based on various characteristics such as age, gender, income level, or education, depending on what is relevant to the research question.
3. After identifying strata, researchers typically use proportional allocation to determine how many individuals to sample from each group, ensuring a balanced representation.
4. This method increases statistical efficiency and improves the precision of survey estimates compared to simple random sampling.
5. Stratified random sampling can be either proportionate, where samples from each stratum are based on their size relative to the total population, or disproportionate, where different sizes are chosen for specific analysis.

Review Questions

• How does stratified random sampling improve the representativeness of survey results?
  • Stratified random sampling enhances representativeness by dividing the population into distinct subgroups that reflect key characteristics relevant to the research. By randomly selecting samples from each subgroup, researchers ensure that all important demographics are included in the final sample. This reduces the risk of bias and makes it more likely that the survey results accurately mirror the views or behaviors of the entire population.
• Discuss the advantages of using stratified random sampling over simple random sampling in survey research.
  • Using stratified random sampling offers several advantages over simple random sampling. First, it allows for better representation of specific subgroups within a population, leading to more accurate and reliable results. Second, it can enhance statistical efficiency by providing more precise estimates with a smaller sample size. Lastly, stratified random sampling facilitates targeted analysis of particular subgroups, which can be crucial for understanding differences across demographics or other categories.
• Evaluate how stratified random sampling might influence the interpretation of survey data when analyzing different demographic groups.
  • Stratified random sampling significantly influences how survey data is interpreted by ensuring that different demographic groups are appropriately represented. This means that analysts can draw more nuanced conclusions about trends and behaviors specific to each stratum. When researchers understand how various groups responded within their contexts, it allows for richer analysis and insights, leading to informed decisions based on comprehensive data rather than generalized assumptions about the entire population.

Related terms

Population: The entire group of individuals or instances that researchers are interested in studying.

Sampling Frame: A list or database from which a sample is drawn, representing the population being studied.

Cluster Sampling:

A sampling method where the population is divided into clusters, usually geographically, and entire clusters are randomly selected for analysis.