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SAMPLING METHODS
Senjuti
WHAT IS ‘SAMPLING’?
Procedure by which some members
of a population are selected as
representative of the entire
population.
The information gathered from the
small group will allow judgements to
be made about the larger groups.
SAMPLE
A sample is the segment of the population we
wish to study
A great deal of care is needed in selecting the
sample so we can say that the data we
obtain from it is meaningful .
For these reasons we use sampling methods.
RIMS
PGDM
METHODS OF SAMPLING
Several methods are used to ascertain a
particular aspect of the population, through
an unbiased sample drawn from the
population.
Probability Sampling
Non-probability Sampling
PROBABILITY
A probability sampling scheme is one in
which every unit in the population has a chance
(greater than zero) of being selected in the
sample, and this probability can be accurately
determined.
Ensures the sample is representative.
Can calculate sample error.
Can project results to whole population, with
allowance made for sampling error.
WHAT IS…………
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
TYPES OF PROBABILITY SAMPLING
SIMPLE RANDOM SAMPLING(SRS)
A sample is selected such that each possible sample combination
has equal probability of being chosen.
Applicable when population is small, homogeneous & available.
It provides for greatest number of possible samples. This is
done by assigning a number to each unit in the sampling
frame.
A table of random number or lottery system is used to
determine which units are to be selected.
METHODS OF SELECTION OF A SIMPLE
RANDOM SAMPLING: Lottery Method
Table of Random numbers
Random number selections using
calculators or computers
EXAMPLEIn a lottery all the members of the
population have the equal chance of
being selected.
STRENGTHSEasily understood, result projectable.
Estimates are easy to calculate.
WEAKNESSESDifficult to construct sampling frame,
expensive, lower precision, no assurance of
repetitiveness.
SYSTEMATIC SAMPLING
It relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.
Entire population is numbered and elements are selected using a skip interval-e.g Every 3rd person in the list of Phone Directory.
Requires complete list of population of interest.
STRENGTHSCan increase representativeness.
Quick, efficient, saves time and cost.
WEAKNESSESNot entirely bias free, each item doesn’t
have equal chance to get selected.
Can decrease repetitiveness if there are
cyclical patterns
STRATIFIED SAMPLING Where population embraces a number of distinct
categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected.
Stratification means division into groups.
In this method the population is divided into a
number of subgroups or strata
From each stratum a simple random sample is
selected and combined together to form the required
sample from the population
FIGURE .1
STRENGTHS Stratifying our sample enable us to ensure that they are
included.
We divide the sample into groups in which we are interested
and then sample from the sub groups
WEAKNESSES First, sampling frame of entire population has to be
prepared separately for each stratum.
CLUSTER SAMPLING
It is an example of 'two-stage sampling' .
First stage a sample of areas is chosen;
Second stage a sample of respondents within those
areas is selected.
Population divided into clusters of homogeneous units,
usually based on geographical contiguity.
Sampling units are groups rather than individuals.
A sample of such clusters is then selected.
All units from the selected clusters are studied
STRENGTHSCuts down on the cost of preparing a sampling frame.
This can reduce travel and other administrative costs.
Disadvantages: sampling error is higher for a simple random sample of same size.
WEAKNESSES Imprecise.
Difficult to compute and interpret the result.