Sampling Techniques in Research

Sampling Techniques

• Sampling techniques are methods for selecting a smaller group from a larger population to learn about the whole population.
• Different sampling methods provide varied approaches to accurately represent the population.

Learning Objectives

• Understand various sampling techniques used in research.
• Recognize the importance of representative sampling for accurate data collection.
• Select the best sampling method for a specific research scenario.

Scenario

• A company wants to determine teen interest in a new snack. They need to collect opinions appropriately.

What is Sampling

• Sampling involves selecting a subset (sample) of individuals or data points from a larger group (population). This allows researchers to make conclusions about the larger group.
• Sampling techniques help select a subset that accurately represents the larger population.

Probability Sampling Techniques

• Simple Random Sampling: Every member of the population has an equal chance of being selected.
  • Example: A university surveys 100 students from 10,000 by creating a list, using a random number generator to pick IDs, and contacting those students.
• Stratified Sampling: Divide the population into meaningful subgroups (strata) and then randomly select samples from each stratum.
  • Example: A company surveys 500 customers out of 50,000 by dividing by age groups (18-24, 25-34, etc.), calculating proportions, and selecting a random sample proportional to each group's size in the total population.
• Systematic Sampling: Select every 'nth' member of the population, starting from a random point.
  • Example: A researcher surveys every 10th customer entering a store over a month, beginning with a randomly selected first customer.
• Cluster Sampling: Divide the population into clusters and randomly select clusters to sample.
  • Example: A company surveys 100 households by dividing the city into neighborhoods, randomly selecting some neighborhoods, and surveying all households within those selected neighborhoods.

Non-Probability Sampling Techniques

• Convenience Sampling: Select samples based on ease of access.
  • Example: A researcher surveys students by setting up a booth in a high-traffic area of a campus.
• Purposive Sampling: Select samples based on specific characteristics or expertise.
  • Example: A researcher studies CEOs of Fortune 500 companies by identifying them and contacting them for interviews.
• Snowball Sampling: Start with a small group of individuals and ask them to refer others who meet the criteria.
  • Example: A researcher studies people with a rare medical condition by identifying a few individuals and asking for referrals.
• Quota Sampling: Select samples to ensure the sample reflects the population's characteristics.
  • Example: A researcher surveys 100 people, ensuring equal representation of different age, gender, and income groups.

Other Sampling Techniques

• Panel Sampling: Select a group of individuals to participate in a study over time.
  • Example: A researcher studies consumer purchasing habits over time by recruiting consumers and gathering data at regular intervals (e.g., every 6 months).
• Longitudinal Sampling: Select a sample and study them over an extended period.
  • Example: A researcher follows graduates' career development for 10 years by recruiting them, collecting data every 2 years, and analyzing for trends.
• Cross-Sectional Sampling: Select a sample at a single point in time.
  • Example: A researcher studies voter attitudes toward a policy by recruiting voters and surveying them.

Factors to Consider When Choosing a Technique

• Population size and complexity
• Research objectives and questions
• Resource constraints (time, budget)
• Desired level of accuracy and precision

Day 1 Quiz Answers

• 1: b) Selecting a subset randomly
• 2: a) Selecting specific groups for targeted analysis