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Sampling a Population
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Sampling
The process ofselecting a number of individuals for a
study in such a way that the individuals represent the
larger group from which they wereselected
SampleSample
the representativesselected for a study whosecharacteristicsexemplify the larger group from which
they wereselected
PopulationPopulation
the larger group from which individuals areselected to
participate in a study
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Thepurpo
sefor
sampling
To gather data about the population in
orde
r to make
an infe
renc
ethat can b
egeneralized to the population
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Thesampling proc
ess
POPULATION (N)
SAMPLE (n)
INFERENCE
IS THE SAMPLE
REPRESENTATIVE?
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Sampling Process...
2. Determine sample size (n)
3. Select sample
1. Define population (N) to be sampled
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1. Define population to be sampled...
(Identifying a suitable sampling frame)
Identify the group of interest and itscharacteristics to which the findings of
thestudy will be generalized
called the targettarget population
oftentimes the accessibleaccessible or
availableavailable population must be
used
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2.Deciding on a suitable sample size...
Thesize of thesample influences boththe representativeness of thesample
and thestatistical analysis of the data
larger samples are more likely
to detect a difference betweendifferent groups
smaller samples are more likely
not to be representative
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Rules of thumb for determining the sample size...
2. For smaller samples (N 100), there is little point in
sampling. Survey the entire population.
1. The larger the population size, the smaller the percentage
of the population required to get a representative sample
4. If the population size is around 1500, 20% should be
sampled.
3. If the population size is around 500 (give or take 100),
50% should be sampled.
5. Beyond a certain point (N = 5000), the population size is
almost irrelevant and a sample size of 400 may be
adequate.
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3. Select the sample...
(Estimating response rate and actual sample size)
(Response rate will be higher if sample is good representative )
Response Rate =Total number of responses
Total no.
of sample-(ineligible + unreachable)
Sample size= n 100re%
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Approaches to quantitative sampling...
2.2. NonrandomNonrandom (non-probability): does not have
random sampling at any state of the sample
selection; increases probability of sampling bias
(Research purpose is directed towards evaluating how a small
group is doing for the purpose of explanation or case study,
like: Which attributes attract people to join job)
1.1. RandomRandom (Probability)(Probability): allows a procedure governed
by chance to select the sample; controls for sampling
bias(to arrive at conclusions and to make predictions affecting the
population)
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Random sampling methods...
(Probability Sampling)
2. Stratified sampling
3. Cluster sampling
4. Systematic sampling
1. Simple random sampling
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Simple random samplingSimple random sampling: the process of selecting a
sample that allows individual in the defined population
to have an equal and independent chance of being
selected for the sample
Steps in random sampling...
1. Number each of the cases in your sampling frame
with a unique number. The first case is numbered 0,
the second 1 and so on.
2. Select cases using random numbers until your actual
sample size is reached.
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advantagesadvantages
easy to conductstrategy requires minimum knowledge of the
population to be sampled
disadvantagesdisadvantages
need names of all population members
may over- represent or under- estimate sample
members
there is difficulty in reaching all selected in the sample
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Systematic samplingSystematic sampling: the process of selecting
individuals within the defined population from a
list by taking every KKth name.
Steps in systematic sampling...
1. Obtain a list of the population.
2. Determine what
KKis equal to by dividing the size ofthe population by the desired sample size.
4. Starting at that point, take every KKth name on the
list until the desired sample size is reached.
3. Start at some random place in the population list.
5. If the end of the list is reached before the desired
sample is reached, go back to the top of the list.
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advantagesadvantages
sample selection is simple
disadvantagesdisadvantages
all members of the population do not have an equalchance of being selected
the KKth person may be related to a periodical order in
the population list, producing unrepresentativeness
in the sample
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Stratified samplingStratified sampling: It is a modification of random
sampling in which you divide the population into two or
more relevant and significant strata based on one or anumber of attributes.
Steps in stratified sampling...
1. Choose the stratification variable or variables.
2. Divide the sampling frame into the discrete strata.
3. Number each of the cases within each stratum with
a unique number.
4. Select your sample using either simple random or
systematic sampling.
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It can be used.
when population consist of a number of
heterogeneoussub-population and elements within a given subpopulation
are relatively homogenous.
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advantagesadvantages
more precise sample
can be used for both proportions and stratification
sampling
sample represents the desired strata
disadvantagesdisadvantages
need names of all population members
there is difficulty in reaching all selected in the sample
researcher must have names of all populations
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Cluster samplingCluster sampling: method in which the population is divided
into groups, any of which can be considered a representative
SampleSteps in cluster sampling...
1. Identify and define a logical cluster.
2. List all clusters (or obtain a list) that make up the
population of clusters.3. Estimate the average number of population members per
cluster.
6. Randomly select the needed number of clusters
5. Determine the number of clusters needed by dividing the
sample size by the estimated size of a cluster.
7. Include in your study all population members in each
selected cluster.
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advantagesadvantages
efficient
researcher doesnt need names of all population
members
reduces travel to site
useful for educational research
disadvantagesdisadvantagesfewer sampling points make it less like that the sample
is representative
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Nonrandom
sampling m
ethod
s...
2. Purposive sampling3. Quota sampling
1. Convenience sampling
4. Snowball sampling
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1. Convenience samplingConvenience sampling: the process
of including whoever happens to beavailable at the time
called accidental or haphazard
sampling
disadvantagesdisadvantages
difficulty in determining how much of
the effect (dependent variable) resultsfrom the cause (independent variable)
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2. Purposive samplingPurposive sampling: the process
whereby the researcher selects asample based on experience or
knowledge of the group to be sampled
called judgment sampling
disadvantagesdisadvantages
potential for inaccuracy in the
researchers criteria and resultingsample selections
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3. Quota samplingQuota sampling: the process whereby
a researcher gathers data fromindividuals possessing identified
characteristics and quotas
disadvantagesdisadvantages
people who are less accessible (more
difficult to contact, more reluctant to
participate) are under-represented
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4. Snowball sampling
It is commonly used when it is difficult to identifymembers of the desired population, for example
people who are working while claiming
unemployment benefit.
Make contact with one or two cases in thepopulation.
Ask these cases to identify further cases.
Ask these new cases to identify further new cases(and so on).
Stop when either no new cases are given or the
sample is as large as is manageable.
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Mistak
esto b
econ
sciou
sof...
2. Sampling bias
which threaten to render a studys
findings invalid
1. Sampling error
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Sampling errorSampling error
the chance and random variation invariables that occurs when any sample
is selected from the population
sampling error is to be expected
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to avoid sampling error, a censuscensus of
the entire population must be taken
to control for sampling error,
researchers use various sampling
methods
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Sampling biasSampling bias
nonrandom differences, generally thefault of the researcher, which cause the
sample is over-represent individuals or
groups within the population andwhich lead to invalid findings
sources of sampling bias include the
use of volunteers and available groups