Sampling Surveys

📊Sampling Surveys Unit 3 – Simple Random Sampling

Simple random sampling is a key technique in survey research, ensuring every member of a population has an equal chance of selection. This method provides representative samples, reduces bias, and allows for statistical analysis, making it valuable for various fields. Understanding simple random sampling involves knowing its implementation, advantages, and limitations. Key concepts include sampling frames, sample size determination, and avoiding common pitfalls. This knowledge is crucial for conducting accurate and reliable surveys across diverse applications.

What's Simple Random Sampling?

  • Probability sampling technique where each member of a population has an equal chance of being selected
  • Involves randomly selecting a sample from a population without replacement
  • Ensures the sample is representative of the population
  • Requires a complete list of all members of the population (sampling frame)
  • Sample size is determined based on desired level of precision and confidence
  • Enables researchers to make inferences about the population based on the sample
  • Minimizes bias in the selection process
    • Eliminates potential for researcher subjectivity
    • Prevents overrepresentation or underrepresentation of certain groups

Why Use It?

  • Provides a representative sample of the population
    • Allows for generalization of findings to the entire population
  • Reduces bias in the selection process
  • Enables the use of statistical techniques for analysis
  • Allows for the calculation of sampling error and confidence intervals
  • Cost-effective compared to surveying the entire population
  • Faster than collecting data from the entire population
  • Suitable when the population is homogeneous
    • Homogeneous populations have similar characteristics across members

How to Do It

  • Define the population of interest
  • Obtain a complete list of all members of the population (sampling frame)
  • Determine the desired sample size
    • Consider the level of precision and confidence required
    • Use sample size calculators or formulas (e.g., Cochran's formula)
  • Assign a unique identifier to each member of the population
  • Use a random number generator or random number table to select the sample
  • Contact the selected individuals and collect data
  • Analyze the data and make inferences about the population
  • Document the sampling process for transparency and replicability

Key Terms to Know

  • Population: The entire group of individuals or items of interest
  • Sampling frame: A complete list of all members of the population
  • Sample: A subset of the population selected for study
  • Sampling unit: The individual members of the population
  • Sampling error: The difference between the sample estimate and the true population value
  • Confidence interval: A range of values likely to contain the true population parameter
  • Bias: Systematic error that leads to inaccurate results
  • Homogeneity: The degree to which members of a population are similar

Common Mistakes to Avoid

  • Using an incomplete or outdated sampling frame
    • Leads to undercoverage bias
  • Selecting a sample size that is too small
    • Reduces the precision and reliability of the results
  • Failing to use true random selection methods
    • Introduces bias into the sample
  • Replacing non-respondents with other individuals
    • Alters the randomness of the sample
  • Generalizing findings to populations not represented in the sample
  • Failing to document the sampling process
    • Makes it difficult to assess the validity of the results
  • Ignoring sampling error when interpreting results
    • Leads to overconfidence in the findings

Real-World Examples

  • Political polls: Randomly selecting voters to predict election outcomes
  • Quality control: Randomly inspecting a sample of products to assess overall quality
  • Market research: Randomly selecting consumers to gather feedback on products or services
  • Public health: Randomly selecting individuals to estimate disease prevalence
  • Educational research: Randomly selecting students to evaluate the effectiveness of teaching methods
  • Environmental studies: Randomly selecting locations to monitor pollution levels
  • Social science research: Randomly selecting participants for surveys or experiments

Pros and Cons

  • Pros:
    • Provides a representative sample of the population
    • Reduces bias in the selection process
    • Allows for the use of statistical techniques
    • Cost-effective and faster than surveying the entire population
  • Cons:
    • Requires a complete and accurate sampling frame
    • May not capture rare or hard-to-reach populations
    • Sampling error can affect the precision of the results
    • Non-response bias can occur if selected individuals refuse to participate
    • May not be suitable for highly heterogeneous populations
    • Can be more expensive than non-probability sampling methods

When to Use Other Methods

  • When a complete sampling frame is not available, consider:
    • Cluster sampling
    • Stratified sampling
    • Systematic sampling
  • When the population is highly heterogeneous, consider:
    • Stratified sampling
    • Quota sampling
  • When the population is hard to reach or rare, consider:
    • Snowball sampling
    • Purposive sampling
  • When time and budget constraints are significant, consider:
    • Convenience sampling
    • Voluntary response sampling
  • When the research aims to explore a specific subgroup, consider:
    • Purposive sampling
    • Quota sampling


© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.