1. Sampling Meaning, Types, Procedure
Dr Mira K Desai
University Department of Extension Education
S.N.D.T. Women’s University

2. When do we do sampling?
Covering entire population is practically impossible and the population is infinite.
When the results are required in a short time.
When the area of study is wide.
When resources are limited particularly in respect of money and trained persons.
When the item or unit is destroyed under investigation.

3. Why Sampling? Scientific approach - Inductive reasoning
Economy - time, money, resources
Quick- procedure is faster
Accurate- results can be accurate
Quality- can be improved
Estimation- adequate and tentative measure
Reliable- error and accuracy
Absence of researcher bias

4. Steps in Sampling Deciding universe/population
Is population under study finite or infinite?
Decision about sample Size, Frame
Deciding sampling design (Type & Procedure)
Calculating sampling error
Statistical generalization…replication

5. What is Universe/Population-Sample?
UNIVERSE: All the individuals/things/events/ documents etc. having designated set of specifications which a study intends to cover.
POPULATION: All the individuals/things/events/ documents etc. confirming to the designated set of specifications which the study in particular covers.
SAMPLE: In relation to population, representative population, miniature or aggregate of population.

6. Here is the example…. UNIVERSE: Children in Mumbai.
POPULATION: Children in the age group of 5 to 10 years, from GMUA, who stay with their families, and who attend private schools.
SAMPLE: Children residing in the Suburban areas of Mumbai and attending to Podar, Jamanabai, Manekji Kooper and Uttpal Sanghavi Schools.

7. Population versus Sample
Population = Parameter (N-size, μ-mean, s-SD)
Sample = Statistics (n-size, x- Mean, SD-SD)
Statistics gives estimates about parameter.
A finite subset of statistical individuals defined in a population is called a sample.
The number of units in a sample is called the sample size.
The list of the units of sample is sample frame.

8. Model for Sampling Objectives Target Group Sampling TYPE OF STUDY
Size
Frame
Techniques
Procedure
RESOURCES
Cost
Time
Human
Technical
TYPE OF STUDY
Survey
Historical
Experiment
Ethnographic
Case study

9. Types of Sampling
Probability sampling
Non-probability sampling
Mixed methods or Multi-stage sampling

10. Types of Sampling NON-PROBABILITY PROBABILITY
[All do not have chance, No way to estimate/specify chance]
Accidental/Incidental/ Convenience/Available
Purposive/ Expert choice/ Judgmental
Quota
Sequential
Snow ball
PROBABILITY
[Equal chance, Estimation of chance]
Simple Random
Systematic Random
Stratified Random
PPS
Area/Cluster

11. Pre-conditions for Probability Sampling
Population is finite
Listing of all the units of the population
Possibility of selection of units at random
Each unit having equal chance of getting selected
Estimation of chance of selection
Estimation of error in case of non-selection

12. Simple Random Method: Chits Random number tables Blind folded pointers
Limitations:
Time consuming
Impractical and deviant
Expensive

13. Systematic Random Method: Size = Total Number/Required Number
Random beginning at a particular interval
Limitations:
Time consuming
Difficult if high variance in population
At times the cost of data collection is high

14. Stratified Random Sampling
Method:
Formation of strata
Variance among stratum not within stratum
Random subgroups/strata/correlated categories
Limitations:
Base is the strata, need to know the units
Bigger strata may lead to over representation

15. PPS- Proportionate to Population Sampling/ Probability Proportional to Size
Method:
Simple random in stratum
Proportionate to the population in the stratum
Limitations:
Time consuming and expensive
Needs estimates of exact population to decide proportions

16. Area/Cluster Sampling
Method:
Assumption of homogeneity in the cluster
Usually part of multi-stage design
Limitations:
Deviance or variance within the cluster
Cluster need to be carefully defined

17. Multi-stage Sampling Example
1st: Administrative Ward (Lottery Method)
2nd:Election Ward (Lottery Method)
3rd: Geographic Location for first unit (Purposively)
4th: Identifying Housing society/ Chawl /Flats/Slums (Random)
5th: Locating household having sample characteristics (Purposive)
6th: Male and female equal ratio through quota (Snow Ball)

18. Keep in mind…. Higher population variance = Higher S. error
Higher Sampling error = Lower sample reliability
Higher sample size = Lower Sampling error
Higher sample size = Lesser sample reliability

19. Decision about Sample size
Degree of accuracy
Extent of variation in population with reference to key characteristics
Size of the population
Tolerable limits of sampling error
Degree of stratification

20. Calculation of sample size
For a survey design based on a simple random sample,
Formula:
n= t² x p(1-p) m²
Where,
n = required sample size t = confidence level at 95% (standard value of 1.96) p = estimated prevalence of measure m = margin of error at 5% (standard value of 0.05)

21. Good sampling design Adequate (larger the size better it is)
Accurate & Reliable (least sampling errors)
Representative (contains all the properties of the population)
Maximum information about population at minimum cost, time and human power