6.1 Sampling Methods and Techniques

Sampling methods are crucial in business research, allowing us to draw conclusions about large populations from smaller, manageable samples. From probability sampling techniques like simple random sampling to non-probability methods, each approach has its own strengths and applications in various business scenarios.

Choosing the right sampling method depends on factors like research goals, population characteristics, and resource constraints. Understanding these methods helps businesses make informed decisions, whether conducting market research, quality control, or employee satisfaction surveys. Let's explore the key sampling techniques and their real-world applications.

Sampling Methods

Probability vs non-probability sampling

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• Probability sampling assigns a known, non-zero chance of selection to every member of the population enabling statistical inference and generalization to the entire population
  • Examples: Surveying a random sample of customers to measure satisfaction (retail), selecting a random sample of products for quality control testing (manufacturing)
• Non-probability sampling selects samples based on subjective judgment or convenience, limiting the ability to make statistical inferences about the population due to unknown probabilities of selection
  • Examples: Conducting a focus group with a convenient sample of employees (human resources), surveying a self-selected sample of website visitors (e-commerce)

Applications of sampling techniques

• Simple random sampling (SRS) gives each member an equal probability of being chosen, requiring a complete sampling frame (list of population)
  • Example: Randomly selecting customer IDs from a database for a satisfaction survey (customer service)
• Systematic sampling selects every kth element from an ordered population list, using a sampling interval of $k = \frac{N}{n}$ (N: population size, n: sample size)
  • Example: Selecting every 10th customer who enters a store for a survey (retail)
• Stratified sampling divides the population into mutually exclusive and exhaustive subgroups (strata), then performs SRS within each stratum to ensure representation of important subgroups
  • Example: Sampling employees from different departments to assess job satisfaction (human resources)
• Cluster sampling divides the population into naturally occurring groups (clusters), randomly selects a subset of clusters, and samples all elements within selected clusters, making it cost-effective for geographically dispersed populations
  • Example: Surveying all households within randomly selected city blocks (market research)

Evaluating Sampling Methods

Pros and cons of sampling methods

• Simple random sampling
  • Pros: Unbiased, representative of the population
  • Cons: Requires a complete sampling frame, can be costly and time-consuming
• Systematic sampling
  • Pros: Simple to implement, ensures even coverage of the population
  • Cons: Potential for periodicity bias if sampling interval is related to a periodic pattern in the population
• Stratified sampling
  • Pros: Ensures representation of important subgroups, can improve precision
  • Cons: Requires knowledge of population characteristics, can be more complex to implement
• Cluster sampling
  • Pros: Cost-effective for geographically dispersed populations
  • Cons: Less precise than SRS, clusters may not be representative of the population

Selection of appropriate sampling

1. Consider the research objectives and required level of precision
   • SRS and stratified sampling provide the most precise estimates
   • Cluster sampling may be sufficient for exploratory research
2. Evaluate the availability and quality of the sampling frame
   • SRS and systematic sampling require a complete list of the population
   • Cluster sampling can be used when a complete list is not available
3. Assess the geographical distribution of the population
   • Cluster sampling is cost-effective for geographically dispersed populations
4. Consider the cost and time constraints
   • SRS and stratified sampling can be more costly and time-consuming
   • Systematic and cluster sampling are often more efficient