The document discusses stratified random sampling, which is a statistical sampling technique where the population is first divided into homogeneous subgroups or strata, then a random sample is drawn from each stratum. The key steps are to 1) identify and define the population, 2) determine sample size, 3) identify variables and subgroups for representation, 4) classify population members into subgroups, and 5) randomly select an appropriate number of individuals from each subgroup. Stratified random sampling can reduce bias and variability compared to simple random sampling. However, it requires knowing the names of all population members and may be difficult if some selected cannot be reached.

1. Slide 12- 1
Stratified Sampling
 Simple random sampling is not the only fair
way to sample.
 More complicated designs may save time or
money or help avoid sampling problems.
 All statistical sampling designs have in
common the idea that chance, rather than
human choice, is used to select the sample.

2. Slide 12- 2
Stratified Sampling (cont.)
 Designs used to sample from large populations
are often more complicated than simple
random samples.
 Sometimes the population is first sliced into
homogeneous groups, called strata, before the
sample is selected.
 Then simple random sampling is used within
each stratum before the results are combined.
 This common sampling design is called
stratified random sampling.

3. STEPS IN STRATIFIED
SAMPLING:
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify the variable and subgroups (strata)
for which you want to guarantee appropriate,
equal representation.

4. STEPS IN STRATIFIED RANDOM
SAMPLING
4. Classify all members of the population as
members of the one identified subgroup.
5. Randomly select, using a table of random
numbers; an “appropriate” number of
individuals from each of the subgroups,
appropriate meaning an equal number of
individuals.

5. ADVANTAGES OF STRATIFIED
RANDOM SAMPLING:
 More precise sample.
 Can be used both proportions and
stratification sampling.
 Sample represents the desired strta.

6. Slide 12- 6
Stratified Sampling (cont.)
 Stratified random sampling can reduce bias.
 Stratifying can also reduce the variability of
our results.
 When we restrict by strata, additional samples are
more like one another, so statistics calculated for
the sampled values will vary less from one sample
to another.

7. DISADVANTAGES OF
STRATIFIED RANDOM
SAMPLING:
 Need names of all population members.
 There is difficulty in reaching all selected
in the sample.
 Researcher must have names of all
populations.