New - Ch 7 (NoteLM) Flashcards
Population
The entire group of individuals or objects that a researcher is interested in studying
Sample
A subset of individuals or objects selected from the population to represent the whole
Census
A study that includes data from every member of the population.
Population of Interest
The specific population to which the researcher wants to generalize their findings.
Generalizability
The extent to which findings from a sample can be applied to the population of interest
Biased Sample
(Unrepresentative Sample)
A sample that does not accurately reflect the characteristics of the population of interest.
Unbiased Sample
(Representative Sample)
A sample that accurately reflects the characteristics of the population of interest
Probability Sampling
(Random Sampling)
A sampling method where every member of the population has an equal and known chance of being selected.
Nonprobability Sampling
(Non-random Sampling)
A sampling method where not every member of the population has an equal chance of being selected, leading to potential bias.
Simple Random Sampling
A probability sampling method where each individual is randomly selected from the population
Systematic Sampling
A probability sampling method where individuals are selected at regular intervals from a list of the population.
Cluster Sampling
A probability sampling method where the population is divided into clusters, and some clusters are randomly selected to represent the whole.
Multistage Sampling
A probability sampling method that involves selecting a sample in stages, with each stage involving random sampling from the previous stage.
Stratified Random Sampling
A probability sampling method where the population is divided into subgroups (strata) based on shared characteristics, and a random sample is drawn from each stratum.
Oversampling
A sampling technique where a specific subgroup is intentionally overrepresented in the sample to ensure adequate data is collected from that group.
Convenience Sampling
A nonprobability sampling method where individuals are selected based on their easy availability.
Purposive Sampling
A nonprobability sampling method where researchers select participants based on their specific knowledge or characteristics.
Snowball Sampling
A nonprobability sampling method where existing participants recruit future participants from their own networks.
Quota Sampling
A nonprobability sampling method where researchers set quotas for different subgroups in their sample to ensure representation.
Margin of Error
A statistic expressing the amount of random sampling error in the results of a survey.
Differentiate between a sample and a population.
A population refers to the entire group of interest in a study, while a sample is a smaller, representative subset selected from that population.
What is the key difference between probability sampling and nonprobability sampling?
Probability sampling uses random selection, giving every member of the population an equal chance of inclusion, while nonprobability sampling does not, potentially leading to biased samples.
Describe two advantages of using stratified random sampling.
Stratified random sampling ensures representation from key subgroups and allows for comparisons between these subgroups.
Why is convenience sampling generally considered a weaker sampling method than random sampling?
Convenience sampling often leads to biased samples because it does not use random selection and tends to overrepresent easily accessible individuals.
Explain how oversampling can be useful in research, even though it creates a non-representative sample.
Oversampling a small or underrepresented group helps to gather sufficient data from that group for analysis, even if it means the sample is not perfectly representative of the overall population.
What is the difference between cluster sampling and multistage sampling?
Cluster sampling involves randomly selecting entire groups (clusters), while multistage sampling involves random selection at multiple stages, potentially sampling both clusters and individuals.
Why is a large sample size not always a guarantee of generalizability?
A large sample size does not guarantee generalizability if the sample is not representative of the population of interest. A large, biased sample is still biased.
Describe a research scenario where purposive sampling would be an appropriate method.
Purposive sampling would be appropriate when studying a specific group with unique expertise or experiences, such as cancer survivors or expert chess players.
What is the relationship between sample size and margin of error?
As sample size increases, the margin of error decreases, meaning that larger samples tend to provide more precise estimates of population parameters.
A researcher wants to study the political attitudes of college students in the United States. Describe a sampling method that would be likely to produce a representative sample for this study.
A stratified random sampling approach could be used, where the researcher first stratifies by college type (e.g., 2-year, 4-year), then randomly selects students within each strata to ensure representation from different college types.
