📊Sampling Surveys Unit 3 – Simple Random Sampling

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