Network Sampling
- Selects individuals from a larger social network to collect data on their relationships and interactions.
- Useful for studying interaction patterns and reaching hard-to-access or hidden populations via chain referrals.
- Can introduce sample-selection bias and therefore requires careful data collection and analysis.
Definition
Section titled “Definition”Network sampling is a sampling method used in research to study the characteristics and behavior of a particular group or population within a social network. It involves selecting a sample of individuals from a larger network and collecting data on their relationships and interactions with other network members. The goal is to understand the patterns and dynamics of social interactions within the group or population and to identify factors that influence those interactions.
Explanation
Section titled “Explanation”Network sampling focuses on relationships and interaction patterns rather than treating individuals as independent units. Researchers select a subset of nodes (individuals) from a larger social network and gather information about connections among those nodes and between sampled nodes and others in the network. By mapping and analyzing these ties, network sampling aims to reveal how social interactions form, spread, and influence behavior within a population.
Chain-referral techniques are common in network sampling because they leverage existing ties to reach additional participants. This approach can make it possible to access networks that are difficult to sample through conventional random or population-based methods.
Examples
Section titled “Examples”Snowball sampling in social media research
Section titled “Snowball sampling in social media research”Snowball sampling starts with a small group of individuals and asks them to refer other network members who may participate. It is particularly useful for studying networks that are difficult to access or locate, such as online social networks or hidden populations. For example, a researcher studying the use of social media among teenagers may use snowball sampling to recruit participants from popular social media platforms like Instagram or Snapchat. By starting with a small group of teens who are active on these platforms, the researcher can then ask them to refer other teens who may be interested in participating in the study.
Respondent-driven sampling (RDS) for hidden populations
Section titled “Respondent-driven sampling (RDS) for hidden populations”Respondent-driven sampling (RDS) involves recruiting a small group of individuals from the target population and asking them to refer other members of the population to the study. This method allows researchers to access hard-to-reach populations that may not be easily identified through traditional sampling methods, and to study the social networks and behaviors of these groups. For example, a researcher studying the use of intravenous drugs among young adults in a city may use RDS to recruit participants from this hidden population. By starting with a small group of drug users, the researcher can then ask them to refer other users to the study, and use the data collected to understand the patterns and dynamics of drug use within this group.
Use cases
Section titled “Use cases”- Studying social interactions and relationship patterns within defined groups or communities.
- Accessing hard-to-reach or hidden populations that are not easily identified through traditional sampling methods.
Notes or pitfalls
Section titled “Notes or pitfalls”- Network sampling can introduce bias in the sample selection process.
- Ensuring accuracy and reliability requires careful data collection and analysis.
Related terms
Section titled “Related terms”- Snowball sampling
- Respondent-driven sampling (RDS)