Sampling
Basic Terms
• Research units – subjects, participants
• Population of interest (all humans?)
• Accessible population – those you can
actually try to sample
• Intended sample – those you select for
participation
• Actual sample – those from whom you
actually obtain data
Proximal Similarity Model
• Donald T. Campbell
• To whom can you generalize your results?
• To the extent that the population is similar
to the sample, generalization should be
good.
• Typical Sample in Psychology is
– Students in Introductory Psychology
– Laboratory Animals
Simple Random Sampling
• Definition of a random sample
• How to obtain one
– Sampling frame – a list of all the members of
the target accessible population
– Each member assigned a random number
– Sort by those random numbers
– Select n units from the N members
• Sampling fraction = n / N
• Assumption that sampling fraction = 0
Stratified Random Sampling
• Divide population into strata
(nonoverlapping homogeneous
subgroups)
• Sample nj subjects from each stratum
• Proportionate stratified random sampling
• Disproportionate stratified random
sampling
Proportionate Stratified Random Sampling
• You sample the same proportion from
each stratum
• For example
– 10% of all freshmen at ECU
– 10% of all sophomores at ECU
– 10% of all juniors at ECU
– 10% of all seniors at ECU
– 10% of all graduate students at ECU
Disproportionate Stratified Random Sampling
• Some strata have relatively few members
• But you want to get a sufficient number of
subjects for each stratum
• So you sample a larger proportion of those
strata with fewer members
• For example, nondegree students or
doctoral students.
Cluster Random Sampling
• Sampling across a wide geographic
region.
• Divide the population in clusters – for
example, counties in North Carolina.
• Randomly sample clusters.
• Gather data on all target subjects within
each randomly sampled cluster.
• For example, all city managers in the
selected counties.
Multi-Stage Random Sampling
• Combine two or more techniques
• Example
– Randomly select 100 classes (clusters) at
ECU.
– From each class, randomly select 5 students.
Nonrandom Sampling
• Convenience Sampling – get what you can
without a lot of hassle
– Stand outside of Rawl and try to recruit
anybody who comes by
• Purposive Sampling – convenience
sampling but where you have
inclusion/exclusion criteria
– For example, subject must be AfricanAmerican and not live in North Carolina
Nonrandom Sampling
• Modal Instance Sampling – you define the
“typical” member of the population and
then recruit only such members
– ECU: 18 year old female resident of North
Carolina
• Expert Sampling – recruit only persons
who are known to expert in some domain
– Designing a survey on social aggression,
recruit experts to judge potential survey items.
Nonrandom Sampling
• Proportional Quota Sampling –
convenience sampling, except you want
subgroups represented in same
proportions they are in the target
population.
– ECU: 30% freshmen, 30% sophomores, 20%
juniors, 20% seniors.
Nonrandom Sampling
• Non-proportional Quota Sampling –
convenience sampling, except you have
specified (nonproportionally) how many
subjects you want in each subgroup
Nonrandom Sampling
• Heterogeneity Sampling – you want to
have adequate numbers of people in each
of two or more groups with disparate
opinions.
– For example, those who thought the world
would end this year, and those who did not
– There are a lot fewer of the former, so you
would need sample a larger proportion of
them.
Nonrandom Sampling
• Snowball Sampling
– Identify people who meet your inclusion
criteria (for example, lifeguards)
– Ask them not only to complete your survey,
– But also to send it on to other similar persons
they know and ask them to complete it.
– Birds of a feather flock together.