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SAMPLING
• Sampling: is a process used to study a
response to an intervention in a small
population that can be applied to a larger population
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What is the purpose of
sampling?
• To draw conclusions about populations
from parameters such as
mean,total,variance standard deviationetc
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The sampling process
• Defining the target population .
• Specifying a sampling frame a set of items or
events possible to measure• Specifying a sampling method for selecting items
or events from the frame
• Determining the sample size
• Implementing the sampling plan• Sampling and data collecting
• Reviewing the sampling process
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Types of Sampling
• Non probability Sampling /Non random
Sampling
• Probability/Random Sampling’
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Non probability Sampling /Non
random SamplingCONVENIENCE SAMPLING
Uses the most readily available subjects
and is the easy method to obtain subjects
Problem: risk of bias is very great
and sample tends to be self selecting:
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Purposive Sampling
Quota sampling
• The population is first segmented into
mutually exclusive sub-groups
• Each stratum of the population is
represented proportionally
• selection of the sample is non-random
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Judgement Sampling
• When researcher select sample member to
conform to some criteria
• It involves the assembling of a sample of
persons with known or demonstrable
experience and expertise in some area.
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Snowball Sampling
• Snowball sampling is especially useful
when you are trying to reach populations
that are inaccessible or hard to find
• Identify someone who meets the criteria
for inclusion in your study. You then ask
them to recommend others who they mayknow who also meet the criteria
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Probability Sampling
• Simple Random Sampling
• Systematic Random Sampling
• Cluster Random Sampling
• Stratified Random Sampling
• Multi phase Sampling
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Simple Random Sampling
• Objective: To select n units out of N such that each NCn has an equal chance of being selected.
• N = the number of cases in the sampling frame
• n = the number of cases in the sample• NCn = the number of combinations (subsets) of n from N
• f = n/N = the sampling
• Procedure: Use a table of random numbers, a computer random number generator, or a mechanical device to select
the sample.• Probability of selection=
Sample size/population size
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Systematic Random Sampling
• Number the units in the population from 1
to N
• Decide on the n (sample size) that you want
or need
• k = N/n = the interval size
• Randomly select an integer between 1 to k
• Take every kth unit
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Stratified Random Sampling
Determine the variables to use for stratification
and there proportion in the population.
Select proportionate or disproportinatestratification
Divide the population into non-overlapping
groups (i.e., strata) N1, N2, N3, ... Ni, such that
N1 + N2 + N3 + ... + Ni = N. Then do a simple
random sample of f = n/N in each strata .
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Cluster (Area) Random Sampling
• Divide population into clusters (usually
along geographic boundaries)
• Randomly sample clusters
• Measure all units within sampled clusters
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Multi-Stage Sampling
• When we combine sampling methods, we
call this Multi-stage sampling.
• Also known as sequential sampling and
Double Sampling
• Mostly used with stratified or cluster
design.