Types of Probability Sampling: Simple, Systematic, Stratified, Cluster, and Multi-stage, Study notes of Introduction to Sociology

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Types of Probability Samples

Simple RandomSystematic RandomStratified RandomRandom ClusterStratified ClusterComplex Multi-stage Random (various kinds)

Simple Random Sampling

• Each element in the population

has an equal probability ofselection AND each combinationof elements has an equalprobability of selection

• Names drawn out of a hat• Random numbers to select

elements from an ordered list

Stratified Random Sampling-

•^

For a given sample size, reduces errorcompared to simple random sampling IF thegroups are different from each other

-^

Tradeoff between the cost of doing thestratification and smaller sample size neededfor same error

-^

Probabilities of selection may be different fordifferent groups, as long as they are known

-^

Oversampling small groups improves inter-group comparisons

Systematic Random Sampling-

•^

Each element has an equal probabilityof selection, but combinations ofelements have different probabilities.

-^

Population size N, desired sample sizen, sampling interval k=N/n.

-^

Randomly select a number j between 1and k, sample element j and then everyk

th

element thereafter, j+k, j+2k, etc.

•^

Example: N=64, n=8, k=64/8=8.Random j=3.

Systematic Random Sampling-

•^

Runs the risk of error ifperiodicity in the list matchesthe sampling interval

-^

This is rare.

-^

In this example, every 4

th

element is red, and red nevergets sampled. If j had been 4or 8, ONLY reds would besampled.

Random Cluster Sampling - 1

•^

Done correctly, this is a form of random sampling

-^

Population is divided into groups, usuallygeographic or organizational

-^

Some of the groups are randomly chosen

-^

In pure cluster sampling, whole cluster is sampled.

-^

In simple multistage cluster, there is randomsampling within each randomly chosen cluster

Random Cluster Samplng - 3

•^

Cluster sampling has veryhigh error if the clusters aredifferent from each other

-^

Cluster sampling is NOTdesirable if the clusters aredifferent

-^

It IS random sampling: yourandomly choose the clusters

-^

But you will tend to omitsome kinds of subjects

Stratification vs. Clustering

Stratification •^

Divide population intogroups different from eachother: sexes, races, ages

-^

Sample randomly fromeach group

-^

Less error compared tosimple random

-^

More expensive to obtainstratification informationbefore sampling

Clustering •^

Divide population intocomparable groups:schools, cities

-^

Randomly sample some ofthe groups

-^

More error compared tosimple random

-^

Reduces costs to sampleonly some areas ororganizations

Stratified Cluster Sampling

•^

Combines elements of stratification and clustering

-^

First you define the clusters

-^

Then you group the clusters into strata of clusters,putting similar clusters together in a stratum

-^

Then you randomly pick one (or more) clusterfrom each of the strata of clusters

-^

Then you sample the subjects within the sampledclusters (either all the subjects, or a simple randomsample of them)