Its academic presentation about Sampling. What-Why-How of sampling in research projects.

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
RESOURCES
Cost
Time
Human
Technical
Target Group
Sampling
Size
Frame
Techniques
Procedure
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
PROBABILITY
[Equal chance,
Estimation of chance]





Simple Random
Systematic Random
Stratified Random
PPS
Area/Cluster
NON-PROBABILITY
[All do not have chance,
No way to
estimate/specify chance]
Accidental/Incidental/
Convenience/Available
 Purposive/ Expert
choice/ Judgmental
 Quota
 Sequential
Snow ball

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