This document discusses stratified sampling, which involves dividing a population into subgroups or strata based on characteristics. Samples are then randomly selected from each stratum. Some key points: - Stratification allows for greater precision than simple random sampling of the same size. It can reduce variation within strata. - Common variables to stratify on include demographics, locations, weights. - Advantages include greater precision, smaller sample sizes, and ability to focus on important subgroups. Disadvantages include more work and difficulty selecting variables. - The size of each stratum sample is usually proportional to the stratum's size in the overall population. This is called proportional allocation.

1. STRATIFIED
SAMPLING
presented by
Waiton sherekete
and
Tafara mapetese
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2. STRATIFIED SAMPLING
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1. Stratification: The elements in the
population are divided into layers/groups/
strata based on their values on one/several
auxiliary variables. The strata must be non-
overlapping and together constitute the whole
population.
2. Sampling within strata: Samples are
selected independently from each stratum.
Different selection methods can be used in
different strata.

3. 3
Eg. Stratification of individuals animals by live
Weight.
Stratum Live weight
(KG)
1 170 or less
2 180-240
3 250-340
4 350-440
5 450-540
6 550-640
7 650 or more

4. 4
Stratum 1:
Northern
Sweden
Eg. Regional
stratification
Stratum 2: Mid-
Sweden
Stratum 3:
Southern
Sweden

5. 5
Eg. Stratification of individuals animals by live
weight and region
Stratum Live weight Region
1 170 or less Northern
2 170 or younger Mid
3 170 or younger Southern
4 180-240 Northern
5 180-240 Mid
6 180-240 Southern
etc. etc. etc.

6. Advantages
 Provides greater precision than a SRS (simple random
sample) of the same size
 Often requires a smaller sample, which saves money
 Can guard against an "unrepresentative" sample
 Focuses on important subpopulations but ignores
irrelevant ones
 If measurements within strata have lower standard
deviation, stratification gives smaller error in
estimation

7. Disadvantages
 Can be difficult to select relevant stratification
variables
 Often requires more administrative work than an SRS
 Not useful when there are no homogeneous subgroups
 Can be expensive

8. WHY STRATIFY?
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• Gain in precision. If the strata are more
homogenous with respect to the study
variable(s) than the population as a whole,
the precision of the estimates will improve.
• Strata = domains of study. Precision
requirements of estimates for certain
subpopulations/domains can be assured by
using domains as strata.

9. WHY STRATIFY?, cont’d
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• Practical reasons. For instance nonresponse
rates, method of measurement and the quality of
auxiliary information may differ between
subpopulations, and can be efficiently handled by
stratification.
• Administrative reasons. The survey organization
may be divided into geographical districts that
makes it natural to let each district be a stratum.

10. Strata size calculation
 In general the size of the sample in each stratum is taken
in proportion to the size of the stratum. This is called
proportional allocation.
 Supporse there are 120 cattle in northen region ,180 in
mid region and 140 cattle in southern region.Total =440
 we are asked to take a sample of 40 cattle in each
strata.
 The first step is to calculate the percentage of each group
of the total.
 southern region - 140/440*100=31,8%
 northen region-12O/440*100=27,2%
 Mid region =180/440*100=41%
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11. cont
 Then we will calculate the stratum using the
percentages provided.
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12. Thank You
 For more info,
 READ FOR YOURSELF
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