COMPLEX RANDOM SAMPLING DESIGNS

1. COMPLEX RANDOM
SAMPLING DESIGNS
GROUP- 3
Shubham Kundu(62)
Sayantan Bhattacharya(63)
Puja Kumari(64)

2. Content
 Sampling method & sample size
for survey
 What is complex sampling
method
 Sampling weight

3. About sampling
 Not feasible to selectALL population
 Best sampling should be able to represent population
 Sampling error occurs when statistics ≠ parameters
 Sampling error is not sampling bias
 Sampling error is random, sampling
bias is predictable(systematic)
 Sampling design affects sampling error
 Standard error measures sampling error

4. The aim of any sampling
plan should be to reduce
sampling error,and to
avoid sampling bias

5. Describe the sample
 Target population – inferred population
 Study population – representative of the target
population
 Sampling frame – list of sampling unit
 Sampling unit – unit to be sampled
 Observation unit – unit to be observed/measured

6. Sampling method
 Random vs. non-random
 Random ensures representativeness
 Simple vs. complex
 SRS = all samples have equal chance to be selected i.e.
equal probability of selection
 Anything not SRS is complex sampling

7. Simple Random
Sampling
Systematic
Random Sampling
Stratified Random
Sampling

8. Stratified versus cluster sampling
 Stratified for heterogeneous groups
e.g. male-female, age groups
 Cluster for homogenous groups – rarely homogenous,
only in ideal situation e.g. schools, districts

9. Cluster Stratified

10. Design Effect (deff)
 Design Effect = Variance estimate (complex)
Variance estimate (SRS)
 How much the sample differ from population
 Different value for different variable
 Usually, deff for complex survey >> 1
 If > 1.5, meaning effective loss 50% of sample if
designed using SRS

11. Sampling Weight
aka Probability Weight
N/n (inverse of sampling fraction)
Two stage = (N1/n1)*(N2/n2)
The sum of PW = population
Weighting can increase standard error

12. Sampling weight…
Why? There is always imperfection in sampling
Weighting will try to correct
1. Unequal probability of selection – base/design
weight
2. Non-response bias
3. Stratification in population – trying to
represent true characteristics of population
e.g. by sex, ethnic etc. – poststratification

13. THANK YOU