joyline yegon
more than 1 week ago
Describe the various sampling techniques with examples?
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ChatGPT
more than 1 week ago
1. Simple random sampling: In this technique, each member of the population has an equal chance of being selected. For example, if a researcher wants to conduct a survey on customer satisfaction at a shopping mall, they could assign each customer a number and use a random number generator to select a sample.
2. Stratified sampling: This technique involves dividing the population into subgroups or strata based on certain characteristics, and then randomly selecting samples from each stratum. For example, if a researcher wants to study the opinions of students at a university, they could divide the students into different departments (strata) and then randomly select samples from each department.
3. Cluster sampling: In this technique, the population is divided into clusters or groups, and then a random sample of clusters is selected. All members of the selected clusters are then included in the sample. For example, if a researcher wants to study the health outcomes of residents in a city, they could divide the city into neighborhoods (clusters) and randomly select a few neighborhoods to study.
4. Systematic sampling: In this technique, every nth member of the population is selected to be part of the sample. For example, if a researcher wants to study the opinions of employees in a company, they could select every 10th employee on a list of all employees.
5. Convenience sampling: This technique involves selecting individuals who are easily accessible or readily available to participate in the study. For example, if a researcher wants to study the opinions of shoppers at a mall, they could approach shoppers as they exit the mall and ask them to participate in the survey.
6. Snowball sampling: This technique is often used in studies where the population is difficult to reach or identify. The researcher starts with one or a few participants who meet the criteria and then asks them to refer other potential participants. This process continues until the desired sample size is reached. For example, if a researcher wants to study the experiences of undocumented immigrants, they could start by interviewing one immigrant and then ask them to refer other undocumented immigrants they know.
2. Stratified sampling: This technique involves dividing the population into subgroups or strata based on certain characteristics, and then randomly selecting samples from each stratum. For example, if a researcher wants to study the opinions of students at a university, they could divide the students into different departments (strata) and then randomly select samples from each department.
3. Cluster sampling: In this technique, the population is divided into clusters or groups, and then a random sample of clusters is selected. All members of the selected clusters are then included in the sample. For example, if a researcher wants to study the health outcomes of residents in a city, they could divide the city into neighborhoods (clusters) and randomly select a few neighborhoods to study.
4. Systematic sampling: In this technique, every nth member of the population is selected to be part of the sample. For example, if a researcher wants to study the opinions of employees in a company, they could select every 10th employee on a list of all employees.
5. Convenience sampling: This technique involves selecting individuals who are easily accessible or readily available to participate in the study. For example, if a researcher wants to study the opinions of shoppers at a mall, they could approach shoppers as they exit the mall and ask them to participate in the survey.
6. Snowball sampling: This technique is often used in studies where the population is difficult to reach or identify. The researcher starts with one or a few participants who meet the criteria and then asks them to refer other potential participants. This process continues until the desired sample size is reached. For example, if a researcher wants to study the experiences of undocumented immigrants, they could start by interviewing one immigrant and then ask them to refer other undocumented immigrants they know.
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Google Gemini AI
more than 1 week ago
Sampling Techniques:
1. Probability Sampling:
- Simple Random Sampling: Each subject has an equal chance of being selected.
- Example: Randomly selecting lottery winners from a pool of tickets.
- Systematic Random Sampling: Subjects are selected at regular intervals from a list or population.
- Example: Selecting every 10th student from a school register.
- Stratified Sampling: Population is divided into subgroups (strata) and subjects are randomly selected from each stratum.
- Example: Dividing a population by age and selecting a random sample from each age group.
- Cluster Sampling: Population is divided into groups (clusters) and a sample of clusters is randomly selected, with all subjects within those clusters being included.
- Example: Selecting a few schools from a district and including all students within those schools.
2. Non-Probability Sampling:
- Convenience Sampling: Selecting subjects who are readily available or easy to access.
- Example: Asking people in a grocery store to participate in a survey.
- Quota Sampling: Setting quotas for different subgroups within the population and selecting subjects to fill those quotas.
- Example: Ensuring that a survey sample reflects the gender and racial distribution of the target population.
- Judgmental Sampling: Selecting subjects based on expert opinion or prior knowledge.
- Example: Choosing a panel of experts in a field to provide insights on a topic.
- Snowball Sampling: Initial subjects recruit additional subjects who meet the target criteria.
- Example: Asking friends and family of drug users to participate in a study on addiction.
3. Mixed Sampling:
- Two-Stage Sampling: Combining probability sampling and non-probability sampling.
- Example: Randomly selecting a sample of households (probability) and then conveniently selecting individuals within those households (non-probability).
- Multi-Stage Sampling: Involves multiple stages of sampling.
- Example: Randomly selecting a sample of counties (stage 1), then randomly selecting a sample of cities within those counties (stage 2), and finally randomly selecting individuals within those cities (stage 3).
1. Probability Sampling:
- Simple Random Sampling: Each subject has an equal chance of being selected.
- Example: Randomly selecting lottery winners from a pool of tickets.
- Systematic Random Sampling: Subjects are selected at regular intervals from a list or population.
- Example: Selecting every 10th student from a school register.
- Stratified Sampling: Population is divided into subgroups (strata) and subjects are randomly selected from each stratum.
- Example: Dividing a population by age and selecting a random sample from each age group.
- Cluster Sampling: Population is divided into groups (clusters) and a sample of clusters is randomly selected, with all subjects within those clusters being included.
- Example: Selecting a few schools from a district and including all students within those schools.
2. Non-Probability Sampling:
- Convenience Sampling: Selecting subjects who are readily available or easy to access.
- Example: Asking people in a grocery store to participate in a survey.
- Quota Sampling: Setting quotas for different subgroups within the population and selecting subjects to fill those quotas.
- Example: Ensuring that a survey sample reflects the gender and racial distribution of the target population.
- Judgmental Sampling: Selecting subjects based on expert opinion or prior knowledge.
- Example: Choosing a panel of experts in a field to provide insights on a topic.
- Snowball Sampling: Initial subjects recruit additional subjects who meet the target criteria.
- Example: Asking friends and family of drug users to participate in a study on addiction.
3. Mixed Sampling:
- Two-Stage Sampling: Combining probability sampling and non-probability sampling.
- Example: Randomly selecting a sample of households (probability) and then conveniently selecting individuals within those households (non-probability).
- Multi-Stage Sampling: Involves multiple stages of sampling.
- Example: Randomly selecting a sample of counties (stage 1), then randomly selecting a sample of cities within those counties (stage 2), and finally randomly selecting individuals within those cities (stage 3).
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