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Sampling
CHAPTER 5
Meaning
 Selection of some part of an aggregate or total on the
basis of which a judgment about the aggregate or
total is made
 Process of obtaining information about an entire
population by examining only a part of it
SAMPLING…….
STUDY POPULATION
SAMPLE
TARGET POPULATION
Essentials or characteristics of a good sample
 Representativeness
 Sample must be representative of the entire population
 Accuracy
 Must result in very small error
 Economy
 Viable in the context of funds available
 Size
Factors affecting Sample size
 Nature of Universe
 Whether universe is homogenous or heterogeneous
 Dispersion factor
 Size of Population
 Nature of study
 For intensive and continuous studies, small size is appropriate
 Type of Sampling
 With random sample, small size also may be allowed
 Standard of Accuracy and acceptable confidence
level
 Availability of finance
Sampling Methods
Probability
Sampling
Non
Probability
Sampling
Probability Sampling
Each unit should have a known chance of being
selected in the population
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Simple Random Sampling
 Applicable when population is small, homogeneous
& readily available.
 Each element has an equal probability of selection.
 A table of random numbers or computer generated
random numbers can be used.
 Method impractical with large samples
Systematic Sampling
 An alternative to random sampling
 Selection of first unit is on random basis
 Remaining units selected at fixed intervals
 Steps
 Number of units in the population-N
 Sample size –n
 Interval size –k =N/n
 Select the first unit randomly
 Select every kth unit
Stratified Sampling
 Involves dividing the population into homogenous
sub groups and then taking simple random sample in
each subgroup
 Stratification improves sample’s representativeness
Cluster Sampling
 Divide the population into a number of non-
overlapping areas (clusters)
 Randomly select a number of these clusters
 Measure all units within selected clusters
 Two types


Area Sampling- Clusters happen to be geographic subdivisions
Multi stage Sampling-Clusters formed at in different stages,
applicable to inquiries extending to large geographical areas
Non Probability Sampling Methods
 Method does not adopt any basis for estimating the
probability that each item in the population has to be
included in the sample
 Methods




Convenience Sampling
Purposive or Judgment Sampling
Quota Sampling
Snowball Sampling
Convenience Sampling
 Sometimes known as grab or opportunity sampling
or accidental or haphazard sampling
 Sample drawn from that part of the population
which is close to hand
 Most useful for pilot testing
Purposive or Judgment Sampling
 Deliberate selection of sample units that conform to
some pre determined criteria
 Selection of those samples that are considered most
appropriate ones for the study
 Based on the judgment of the researcher
Quota Sampling
 Selecting people non randomly based on some fixed
quota
 Two types


Proportional
Non proportional
 Not aimed at precision but need results quickly
Snowball Sampling
 Identify those who meet the criteria of study
 Ask them to recommend others who meet the criteria
 Method suitable when population is difficult to find