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SAMPLING AND THE SOURCES OF BIAS (SOURCES OF SAMPLING BIAS (difference…
SAMPLING AND THE SOURCES OF BIAS
SAMPLING
explanatory analysis of the sample
generalize and conclude about sample
making an
INFERENCE
for inference to be valid
sample needs to be REPRESENTATIVE of entire population
SOURCES OF SAMPLING BIAS
CONVENIENCE SAMPLE
individuals who are easily accessible
they are more likely to be included in the sample
NON-RESPONSE
if only a (non-random) fraction of the random sample people respond to a survey such that the sample is no longer representative of the populatiom
e.g. certain groups of people with social status are less likely to respond to the survey
VOLUNTARY RESPONSE BIAS
when the sample consists of people who volunteer to respond because they have strong opinions on the issue
difference between voluntary response bias and non-response bias
non-response
there is a random sample that is surveyed, but people who choose to respond are not representative of the sample
voluntary response
there is no initial random sample
SAMPLING METHODS
STRATIFIED SAMPLING
first divide the population into homogenous groups
STRATA
then randomly sample from within each startum
want to make sure both genders are equally represented in a study
divide the population first into males and females, and then randomly sample from within each group
SIMPLE RANDOM SAMPLING (SRS)
randomly select cases from population, such that each case is equally likely to be selected
e.g. randomly drawing names from a hat
CLUSTER SAMPLING
divide the population into
CLUSTERS
, randomly sample a few clusters, and then sample all observation within these clusters
clusters are heterogeneous within themselves (x strata and stratified sampling)
each cluster is similar to another, such that we can get away with just sampling from a few of the clusters
MULTISTAGE SAMPLING
add another step to cluster sampling
we divide the population into clusters, randomly sample a few clusters, and then we randomly sample observations from within these clusters