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Chapter 11: Problem 1

Describe the difference between a stratifi ed sample and a cluster sample.

Short Answer

Expert verified
The main difference between a stratified sample and a cluster sample is the criteria for dividing the population. In stratified sampling, the population is divided into separate groups (or 'strata') based on a shared characteristic. A simple random sample is then drawn from each stratum. Conversely, in cluster sampling, the population is divided into clusters based on geographical or area-wise grouping, and these clusters are likely diverse. Either a simple random sample is drawn from each selected cluster (One-stage), or a sample of clusters is selected, and then a simple random sample is drawn from each of these chosen clusters (Two-stage).

Step by step solution

01

Defining Stratified Sampling

Stratified Sampling is a sampling technique in which the population under study is divided into distinct units (or 'strata') based on similar characteristics. The divisions are created in such a way that each unit in one division shares a common characteristic that distinguishes it from the units in other divisions. For example, in a school population, students can be grouped strata-wise based on their classes or grades. Then, a simple random sample is drawn from each stratum.
02

Defining Cluster Sampling

Cluster Sampling, on the other hand, also involves dividing the population into groupings or 'clusters'. However, the distinguishing factor here is that the clusters are defined geographically or area-wise, not based on similarities. The clusters are often naturally occurring, grouped based on geographical or area proximity, and each cluster is a true representation of the population. E.g., divide a city into different clusters based on postcode areas. Then, either take a simple random sample from each selected cluster (One-stage) or take a sample of clusters and then a simple random sample from each of these selected clusters (Two-stage).
03

Highlighting the Differences

While both stratified and cluster sampling involve dividing the population into subsets, the primary difference lies in the characteristics of these subsets or divisions. In stratified sampling, the subsets share similar characteristics, while in cluster sampling, the subsets are geographically grouped but can contain diverse members.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Stratified Sampling
Stratified sampling is a technique used to ensure that different segments of a population are accurately represented within your sample. It involves dividing the population into subgroups, or "strata," that share common characteristics. These characteristics can be anything that researchers believe is relevant to the study, like age, income level, or education.
  • For instance, if you were sampling from a high school, you might create strata based on grade level, such as freshmen, sophomores, juniors, and seniors.
  • Once the strata are defined, researchers then take a sample from each stratum. This could be through a simple random sample or another method.
The goal of this technique is to ensure that all relevant groups are included and proportionately represented in the sample. It helps to increase the precision and reliability of results as insights can be drawn from each stratum specifically and then generalized to the broader population.
Cluster Sampling
Cluster sampling is a different approach where the population is divided into clusters that often represent natural groupings or specific geographic areas. Unlike stratified sampling, clusters are not created based on similar characteristics.
The goal here is to simplify the data collection process, especially in large populations, by sampling entire clusters. Here's how it typically works:
  • The population is divided into clusters that are often situated geographically, like regions, neighborhoods, or districts.
  • Researchers then randomly select a certain number of these clusters to be part of the overall sample.
  • Within those selected clusters, researchers may perform additional sampling or use every member of the cluster.
This method can be cost-effective and logistically simpler because it allows data collection from grouped individuals. However, it may introduce greater variability if the selected clusters are not representative of the entire population.
Population Subsets
Population subsets are essential elements when discussing sampling techniques like stratified or cluster sampling. These subsets or groups represent different sections or segments of the population.
  • In stratified sampling, subsets (strata) are defined by specific characteristics or criteria relevant to the research. Each stratum is essentially a "mini-population" with shared traits.
  • In cluster sampling, subsets (clusters) are usually formed based on geographic or practical considerations rather than shared characteristics.
When deciding on population subsets, researchers must understand their study's objectives to choose the most relevant criteria for dividing their population. The formation of these subsets enables sampling methods to be more targeted, efficient, and informative, heightening the ability to make generalized conclusions about the whole population.

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