Stratified Sampling

Definition

Stratified sampling is a sampling technique that involves dividing a population into distinct subgroups, or strata, based on shared characteristics, and then randomly selecting samples from each stratum. This method ensures that every subgroup is represented in the final sample, enhancing the precision of estimates and allowing for better comparisons across different segments of the population.

5 Must Know Facts For Your Next Test

1. Stratified sampling can lead to increased statistical efficiency by reducing the overall variability of the sample estimates compared to simple random sampling.
2. This method requires prior knowledge of the population structure to properly form strata, which can include demographics like age, gender, or income level.
3. Samples can be selected proportionately (in line with the size of each stratum) or equally (an equal number from each stratum) depending on the research goals.
4. Stratified sampling helps mitigate sampling bias by ensuring all relevant subgroups are included, making it particularly useful in surveys and studies involving diverse populations.
5. In terms of power analysis, stratified sampling can allow researchers to achieve sufficient power with smaller overall sample sizes by focusing on the most relevant strata.

Review Questions

• How does stratified sampling improve the accuracy of estimates in a study compared to simple random sampling?
  • Stratified sampling improves accuracy by ensuring that all relevant subgroups within a population are represented. By dividing the population into strata based on specific characteristics and then randomly sampling from each stratum, researchers minimize the chances of overlooking important segments. This approach typically results in lower variability in estimates and more reliable data, as it accounts for differences across groups that might skew results if using only simple random sampling.
• What steps must be taken to effectively implement stratified sampling in a research study?
  • To implement stratified sampling effectively, researchers must first identify the relevant strata within the population based on key characteristics. Next, they need to determine how many samples will be taken from each stratum—this can be done proportionately based on stratum size or equally across strata. After defining these parameters, researchers can randomly select samples from each subgroup. Proper planning and understanding of the population's structure are essential for this technique to yield valid results.
• Evaluate how stratified sampling can influence the design and outcome of a survey aimed at understanding consumer behavior across different demographics.
  • Stratified sampling allows researchers to gain deeper insights into consumer behavior by ensuring that diverse demographic groups are adequately represented in the survey results. For instance, if a survey targets purchasing patterns among various age groups, using stratified sampling helps capture variations in preferences or behaviors that may exist between those groups. This approach not only enhances the reliability of findings but also enables targeted marketing strategies and tailored recommendations, ultimately leading to more effective decision-making based on comprehensive data.

Related terms

Population:

The entire group of individuals or instances about which we are trying to gather information.

Sampling Error: The error that arises from using a sample to estimate the characteristics of a population, which can occur due to variability between the sample and the population.

Quota Sampling:

A non-probability sampling technique where researchers ensure equal representation of certain characteristics in the sample based on pre-set quotas.