exam 3 chapter 7 Flashcards
population
an entire set of people or products in which you are interested
sample
smaller set taken from the population
census
a set of observations that contains all members of the population at interest
if a sample can generalize to an entire population, it has…
good external validity and is representative of the population of interest
if a sample comes from a population, does that mean it generalizes to that population
no samples can be biased or unbiased
biased sample
also called an unrepresentative sample, it is where some members of the population of interest have a much higher probability than other members
unbiased sample
also called a representative sample, all members of the population have an equal chance of being included in the sample. Only this kind of sample allows us to make inferences about the population of interest
why might a sample be biased
researchers may only study those they can conveniently contact (convenience sampling) or only those who volunteer to respond (self selection) this can threaten external validity
random sampling
also called probability sampling. every member of the population of interest has an equal chance of being selected for the sample, regardless of whether they are convenient or motivated to volunteer
simple random sampling
the most basic form of probability sampling, where the sample is chosen completely at random from the population of interest (ex: drawing names out of a hat)
systematic sampling
a probability sampling technique in which the researcher uses a randomly chosen number N, and counrs off every Nth member of a population to achieve a sample
what is the downside to simple random sampling and systematic sampling
it can be difficult and time consuming, and nearly impossible to find every member of the population of interest
cluster sampling
people are already divided into arbitrary groups. clusters of participants within a population of interest are randomly selected and then everyone in the cluster is used
multistage sampling
a probability sampling technique involving at least two stagesa random sample of clusters followed by a random sample of people within the selected clusters
stratified random sampling
A form of probability sampling a random sampling technique in which the researcher identifies particular demographic categories or strata and then randomly selects individuals within each category.
how is cluster sampling different from stratified random sampling
Strata are meaningful categories; clusters are more arbitrary.
the final sample sizes of strata reflect their proportion in the population; clusters are not selected with such proportions in mind
oversampling
a variation of stratified random sampling where the researcher intentionally overrepresents one or more groups. Still a probability sample
random assignment
only in experimental designs. When researchers want to place participants into two different groups (treatment and comparison groups), this enhances internal validity (treatment and comparison groups have the same kinds of people in them)
purposive sampling
A biased sampling technique in which only certain kinds of people are included in a sample
snowball sampling
a variation on purposive sampling, a biased sampling technique in which participants are asked to recommend acquaintances for the study
quota sampling
a biased sampling technique in which a researcher identifies subsets of the population of interest, sets a target number for each category in the sample and nonrandomly selects individuals within each category until the quotas are filled
what kind of claim needs a probability sample and what kind of claims can use a nonprobability sample
probability: frequency
nonprobability: association and causal
should you always not trust unrepresentative samples?
you should always be weary in certain cases, its reasonable to trust the reports of unrepresentative samples
for external validity, is a bigger sample always better?
no but when a phenomenon is rare you do need a large random sample to locate enough instances of the phenomenon for valid statistical analysis
