Sampling is the process of selecting a subset of individuals from a larger group (population) to study. Here's a breakdown of the various types:
Comprehensive Sampling:
Explanation: This involves studying every single member of the population. It's used when the population is small and you need a complete understanding.
Example: Studying the performance of all 20 employees in a small startup company.
Critical Case Sampling:
Explanation: Selecting cases that are crucial or vital to the research question. If it's true for this case, it's likely true for others.
Example: Studying a successful turnaround of a company on the brink of bankruptcy to understand factors of success.
Maximum Variation Sampling:
Explanation: Selecting cases that represent a wide range of variations on the characteristics of interest.
Example: Studying student opinions on online learning by including students from different majors, grades, and levels of tech proficiency.
Extreme, Deviant or Unique Case Sampling:
Explanation: Focusing on cases that are unusual or significantly different from the norm.
Example: Studying individuals who have achieved exceptionally high levels of success in a field despite facing extreme adversity.
Typical Case Sampling:
Explanation: Selecting cases that represent the average or typical situation.
Example: Studying the average customer experience at a restaurant by observing customers during a typical weekday lunch.
Negative or Discrepant Case Sampling:
Explanation: Selecting cases that contradict or challenge the emerging patterns or theories from the data.
Example: If you are finding that most people love a new product, you would interview the few people that strongly dislike it, to understand why.
Homogenous Sampling:
Explanation: Selecting cases that are very similar in terms of specific characteristics.
Example: Studying the experiences of first-generation college students from low-income backgrounds.
Snowball, Chain or Network Sampling:
Explanation: Starting with a few participants and then asking them to refer other potential participants.
Example: Studying a hidden population, like undocumented immigrants, by asking initial participants to connect you with others.
Intensity Sampling:
Explanation: Selecting cases that exhibit the phenomenon of interest intensely, but not extremely.
Example: studying students that are known to have a strong interest in environmentalism, but that are not considered extreme activists.
Stratified Purposeful Sampling:
Explanation: Dividing the population into subgroups (strata) and then selecting cases from each subgroup to ensure representation.
Example: Studying the impact of a new teaching method by selecting students from different grade levels and academic performance groups.
Random Purposeful Sampling:
Explanation: Randomly selecting cases from a larger pool of potential participants, but still within a defined purpose or criteria.
Example: from a list of all teachers in a district that have taught the new math curriculum, randomly selecting 10 to be interviewed.
Theoretical or Theory-Based Sampling:
Explanation: Selecting cases based on their potential to contribute to the development or testing of a theory.
Example: Studying different types of leadership styles in organizations to refine a theory of organizational leadership.
Criterion Sampling:
Explanation: Selecting cases that meet a specific set of predetermined criteria.
Example: Studying individuals who have successfully completed a specific training program.
Opportunistic Sampling:
Explanation: Taking advantage of unexpected opportunities to gather data during the research process.
Example: While conducting a study on community health, encountering a local support group and deciding to include their perspectives.
Convenience Sampling:
Explanation: Selecting participants who are easily accessible to the researcher.
Example: Surveying students in your own class because they are readily available. This is often the weakest sampling method.