Sampling - PowerPoint Presentation

Slide1

SamplingSlide2

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 Slide3

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 AnimalsSlide4

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 = 0Slide5

Stratified Random Sampling

Divide population into

strata (

nonoverlapping

homogeneous subgroups)

Sample

n

j

subjects from each stratum

Proportionate stratified random sampling

Disproportionate stratified random samplingSlide6

Proportionate

S

tratified Random

S

ampling

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 ECUSlide7

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.Slide8

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.Slide9

Multi-Stage Random Sampling

Combine two or more techniques

Example

Randomly select 100 classes (clusters) at ECU.

From each class, randomly select 5 students.Slide10

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 Slide11

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.Slide12

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.Slide13

Nonrandom Sampling

Non-proportional Quota Sampling – convenience sampling, except you have specified (

nonproportionally

) how many subjects you want in each subgroupSlide14

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.Slide15

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.