Sampling Techniques PDF

Understanding Sampling Techniques LEARNING OBJECTIVES: Explain various sampling techniques in research.  Appreciate the role of representative sampling through accurate data collection. Choose the best sampling technique for a given research scenario. Scenario: “A company wants to know if...

Understanding Sampling Techniques LEARNING OBJECTIVES: Explain various sampling techniques in research.  Appreciate the role of representative sampling through accurate data collection. Choose the best sampling technique for a given research scenario. Scenario: “A company wants to know if teenagers would buy a new snack product. How can they collect opinions?”  WHAT IS SAMPLING TECHNIQUES Sampling techniques are methods used to select a subset of individuals or data points from a larger population, to make inferences about the entire population. Here are some common sampling techniques:  # Probability Sampling Techniques 1. Simple Random Sampling: Every member of the population has an equal chance of being selected.. Simple Random Sampling Example: A university wants to survey 100 students out of 10,000 students. - Steps: 1. Create a list of all 10,000 students. 2. Use a random number generator to select 100 unique student IDs. 3. Contact the selected students to participate in the survey.  # Probability Sampling Techniques 2. Stratified Sampling: Divide the population into subgroups (strata) and randomly select samples from each stratum.  Stratified Sampling Example: A company wants to survey 500 out of 50,000 customers, stratified by age group (18-24, 25-34, 35-44, 45-54, 55+). – Steps: 1. Divide the 50,000 customers into the five age groups. 2. Calculate the proportion of customers in each age group. 3. Select a random sample of 500 customers, with the number of customers from each age group proportional to the population.  Systematic Sampling 3. Systematic Sampling: Select every nth member of the population, starting from a random point.  Systematic Sampling Example: A researcher wants to survey every 10th customer who enters a store over a period of one month. - Steps: 1. Determine the starting point (e.g., the first customer on the first day). 2. Select every 10th customer who enters the store. 3. Continue until the desired sample size is reached  # Probability Sampling Techniques 4. Cluster Sampling: Divide the population into clusters and randomly select clusters to sample.  Cluster Sampling Example: A company wants to survey 100 city households clustered by neighborhood. – Steps: 1. Divide the city into neighborhoods. 2. Randomly select a subset of neighborhoods. 3. Survey all households within the selected neighborhoods. # Non-Probability Sampling Techniques 1. Convenience Sampling: Select samples based on ease of access or convenience.. Convenience Sampling Example: A researcher wants to survey students at a university and decides to set up a survey booth in the student union. – Steps: 1. Set up the survey booth in a high-traffic area. 2. Approach students who pass by the booth and ask if they would like to participate. 3. Collect surveys from willing participants. # Non-Probability Sampling Techniques 2. Purposive Sampling: Select samples based on specific characteristics or expertise.. Purposive Sampling Example: A researcher wants to study the experiences of CEOs at Fortune 500 companies. - Steps: 1. Identify the CEOs of Fortune 500 companies. 2. Contact the CEOs and ask if they would be willing to participate in the study. 3. Conduct in-depth interviews with the participating CEOs. # Non-Probability Sampling Techniques 3. Snowball Sampling: Start with a small group of individuals and ask them to refer others who meet the sampling criteria. Snowball Sampling Example: A researcher wants to study the experiences of people living with a rare medical condition. - Steps: 1. Identify a few individuals who have the condition. 2. Ask them to refer others who also have the condition. 3. Continue to recruit participants through referrals.. # Non-Probability Sampling Techniques 4. Quota Sampling: Select samples to ensure that the sample represents the population's characteristics..  Quota Sampling Example: A researcher wants to survey 100 people, with quotas for age, gender, and income level. - Steps: 1. Determine the quotas for each demographic characteristic. 2. Recruit participants until the quotas are filled. 3. Ensure that the sample is representative of the population.. # Other Sampling Techniques 1. Panel Sampling: Select a group of individuals to participate in a study over time. Panel Sampling Example: A researcher wants to study the purchasing habits of a group of consumers over time. - Steps: 1. Recruit a group of consumers to participate in the study. 2. Collect data from the participants at regular intervals (e.g., every 6 months). 3. Analyze the data to identify trends and patterns.  # Other Sampling Techniques. Longitudinal 2 Sampling: Select a sample and study them over an extended period.. Longitudinal Sampling Example: A researcher wants to study the career development of a group of graduates over 10 years. - Steps: 1. Recruit a group of graduates to participate in the study. 2. Collect data from the participants at regular intervals (e.g., every 2 years). 3. Analyze the data to identify trends and patterns. # Other Sampling Techniques Cross-Sectional Sampling: Select a sample at a single point in time. Cross-Sectional Sampling Example: A researcher wants to study the attitudes of voters towards a particular policy issue. - Steps: 1. Recruit a sample of voters to participate in the study. 2. Collect data from the participants through a survey or interview. 3. Analyze the data to identify trends and patterns. When choosing a sampling technique, consider factors such as: 1.Population size and complexity 2. Research objectives and questions 3. Resource constraints (time, budget, etc.) 4. Desired level of accuracy and precision Day 1 Quiz  1. What is random sampling?  a) Asking only your friends  b) Selecting a subset randomly  c) Surveying everyone in the world  d) Ignoring the target market  2. What is stratified sampling?  a) Selecting specific groups for targeted analysis  b) Ignoring data from specific groups  c) Choosing only one demographic  d) Guessing results Day 1 Answer Key  1. b) Selecting a subset randomly  2. a) Selecting specific groups for targeted analysis