Stratified Sampling

Definition

Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, based on specific characteristics, and then randomly selecting samples from each stratum. This technique ensures that different segments of the population are represented in the sample, which can enhance the accuracy and reliability of research findings. By considering the various strata, researchers can obtain a more comprehensive understanding of the population being studied.

5 Must Know Facts For Your Next Test

1. Stratified sampling helps to ensure that all relevant subgroups within a population are represented in the sample, which can improve the generalizability of results.
2. This method can be particularly useful in human sexuality research where different demographics (like age, gender, or sexual orientation) may have varying experiences and perspectives.
3. Stratified sampling can be implemented in two ways: proportionate, where the sample size from each stratum reflects its proportion in the population, and disproportionate, where certain strata are oversampled to ensure adequate representation.
4. Using stratified sampling often leads to greater precision in estimating population parameters than simple random sampling, especially when there are significant differences between strata.
5. Researchers need to carefully define their strata based on relevant characteristics to ensure meaningful analysis and avoid introducing bias.

Review Questions

• How does stratified sampling improve the accuracy of research findings compared to simple random sampling?
  • Stratified sampling improves accuracy by ensuring that all relevant subgroups within a population are represented in the sample. This method addresses potential biases that might arise if certain groups were underrepresented or omitted entirely in simple random sampling. By reflecting the diversity within a population, researchers can obtain more reliable and valid conclusions about the relationships being studied.
• In what scenarios would disproportionate stratified sampling be advantageous, and what considerations should researchers keep in mind when using this method?
  • Disproportionate stratified sampling is advantageous when certain subgroups are small but critical for analysis, allowing researchers to oversample these groups for better representation. This is especially important in studies of human sexuality where minority sexual orientations or identities may not be adequately captured in proportionate samples. Researchers must ensure that their analysis accounts for the different probabilities of selection to avoid skewed results.
• Evaluate how stratified sampling could impact research outcomes in studies examining sexual behavior across various demographics.
  • Stratified sampling could significantly impact research outcomes by providing a clearer picture of how sexual behavior varies among different demographics. By ensuring that groups such as age, gender, or cultural background are adequately represented, researchers can identify trends and differences that might otherwise be overlooked. This method allows for more nuanced analyses and conclusions about how various factors influence sexual behavior, leading to better-informed policies and educational programs tailored to diverse populations.

Related terms

Population: The entire group of individuals or instances about whom a researcher wants to draw conclusions.

Random Sampling: A sampling method where each member of the population has an equal chance of being selected, minimizing bias in the selection process.

Sample Size: The number of observations or replicates included in a statistical sample, which can affect the precision and reliability of research results.