Chapter 9: Sampling in Quantitative Research Flashcards
(14 cards)
What is sampling?
The process of selecting a subset of individuals from a population to represent the whole.
Example: Surveying 200 employees from a company with 1,000 workers.
What is a population in research?
The entire group that the researcher is interested in studying.
Example: All marketing professionals in the UK.
What is a sample?
A smaller group selected from the population for study.
Example: 150 marketing professionals randomly selected for a survey.
What is probability sampling?
A sampling method where every member of the population has a known chance of being selected.
Example: Using a random number generator to select employees.
What is simple random sampling?
A probability method where every individual has an equal chance of selection.
Example: Drawing names from a hat to choose 10 participants.
What is systematic sampling?
Selecting every nth person from a list after a random starting point.
Example: Choosing every 5th name from an employee list.
What is stratified random sampling?
Dividing the population into subgroups and randomly sampling within each group.
Example: Selecting participants from each department proportionally.
What is cluster sampling?
Randomly selecting groups (clusters) and then studying everyone within them.
Example: Randomly selecting five branches of a company and surveying all staff there.
What is non-probability sampling?
Sampling where not all individuals have a known or equal chance of being selected.
Example: Surveying volunteers who respond to an email invitation.
What is convenience sampling?
Selecting participants who are easy to access.
Example: Surveying classmates because they’re nearby.
What is quota sampling?
Ensuring specific subgroups are included in set proportions.
Example: Surveying 50% men and 50% women to match company demographics.
What is snowball sampling?
Asking participants to refer others to the study.
Example: A manager introduces you to other managers for interviews.
What is sampling error?
The difference between sample results and what the full population would show.
Example: Your sample shows 60% satisfaction, but the true population value is 65%.
Why is sampling important in quantitative research?
It affects the accuracy, generalizability, and validity of findings.
Example: A biased sample may lead to incorrect conclusions about employee morale.
