Cluster sampling

Definition

Cluster sampling is a statistical method used to select a sample from a population by dividing it into clusters and then randomly selecting entire clusters for the study. This technique is particularly useful when the population is large and spread out, making it more efficient and cost-effective to gather data by focusing on specific clusters rather than individuals. It simplifies the data collection process in hospitality and tourism research by allowing researchers to target certain geographic areas or demographic groups.

5 Must Know Facts For Your Next Test

1. Cluster sampling can reduce costs significantly, especially when data collection involves travel to different locations within a region.
2. This method works best when clusters are internally homogeneous but differ significantly from one another, which helps ensure a diverse representation of the entire population.
3. It is often used in survey research within hospitality and tourism to target specific geographic areas like hotels or tourist attractions.
4. Researchers must be careful about cluster selection as poorly chosen clusters can lead to biased results, affecting the reliability of conclusions drawn from the data.
5. While it simplifies the sampling process, cluster sampling may increase sampling error if clusters are not representative of the larger population.

Review Questions

• How does cluster sampling differ from other sampling methods like stratified sampling?
  • Cluster sampling differs from stratified sampling in that it involves dividing the population into separate groups or clusters, then randomly selecting entire clusters for study. In contrast, stratified sampling requires dividing the population into subgroups based on specific characteristics and drawing samples from each subgroup. This makes cluster sampling more efficient in large populations where reaching individual members can be logistically challenging, while stratified sampling aims to ensure representation across all defined subgroups.
• Discuss the advantages of using cluster sampling in hospitality and tourism research compared to random sampling.
  • Using cluster sampling in hospitality and tourism research offers several advantages over random sampling, especially in terms of cost-effectiveness and logistical convenience. By targeting specific clusters, such as regions with tourist attractions or hotel chains, researchers can gather data more efficiently without needing to randomly sample individuals across vast geographical areas. This method not only saves time but also reduces travel costs while still providing valuable insights into trends and customer preferences within defined segments of the industry.
• Evaluate the potential implications of poor cluster selection on research outcomes in hospitality and tourism studies.
  • Poor cluster selection can significantly skew research outcomes in hospitality and tourism studies by introducing bias into the sample. If selected clusters do not accurately represent the diversity of the overall population, this can lead to misleading conclusions about consumer behaviors or preferences. For instance, if a study only includes high-end hotels in affluent areas as clusters, it may overlook valuable insights from budget accommodations that cater to different demographics. Such biases can ultimately affect marketing strategies and decision-making processes in the industry.

Related terms

Stratified sampling: A method of sampling where the population is divided into subgroups, or strata, that share similar characteristics, and samples are drawn from each stratum.

Random sampling: A sampling technique where each member of the population has an equal chance of being selected, ensuring that the sample is representative of the entire population.

Sampling error: The difference between the results obtained from a sample and the actual values in the population, which can occur due to the sample not being perfectly representative.