Sampling. Basic Terms Research units – subjects, participants Population of interest (all humans?) Accessible population – those you can actually try.

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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 n j 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 African- American 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.

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