Transcript SAMPLING

SAMPLING
FACTORS AFFECTING SAMPLE SIZE
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OBJECTIVE OF RESEARCH
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DESCRIPTION
INFERENCE
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HOMOGENEITY OF POPULATION
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SIZE OF POPULATION
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MARGIN OF ERROR
SAMPLING TERMS
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SAMPLE – some part of a “whole”
ELEMENT – that unit about which information is
collected and which provides the basis for
analysis
POPULATION – the theoretically specified
aggregate of elements
REPRESENTATIVENESS – the extent to which
the sample “mirrors” the population
EPSEM – Equal Probability of Selection Method
Sampling Terms (cont)
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SAMPLING UNIT – that element or set of
elements considered for selection in some stage
of sampling
SAMPLING FRAME – the actual list of sampling
units from which the sample, or some stage of the
sample, is selected
OBSERVATION UNIT – (unit of data collection) is
an element or aggregation of elements from which
information is collected
SAMPLE SIZE – the number of elements selected
TYPES OF SAMPLES
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NON-PROBABILITY
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PROBABILITY
NON-PROBABILITY SAMPLES
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CONVENIENCE - procedure of obtaining those
sampling units/elements most conveniently available
Judgment – an experienced researcher selects the
sample based on appropriate characteristics of the
sample
Quota – ensures that various subgroups of a population
SNOBALL – initial respondents are selected by some
method and then additional respondents are obtained
from information provided by the initial respondents
Why Probability Samples?
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Typically more representative than other
types of samples – bias
Permit the researcher to estimate the
accuracy or representativeness of the
sample
Saves time/money
Sampling Error
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Biased Selection – misses and/or over
represents categories of elements
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Chance Variability – a sample deviates
from the population value as a result of
chance – increasingly problematic as
sample size decreases
Stages in Selection of a Sample
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Define the Target
Population
Select a Sampling
Frame
Determine Sampling
Method
Plan Procedure for
selecting elements
Estimate Sampling
Size *
 Draw Sample
 Conduct Field Word
 Check Sample against
the Population or
Sampling Frame *
* If probability sample
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Probability Sampling
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Simple Random – technique which assures that
each selected element in the population has an
equal chance of being included in the sample
Systematic – an initial starting point is selected by
a random process and then every nth numbered
element in the frame is selected
Stratified – random subsamples are drawn from
within each stratum. The sub samples may be
proportional or disproportional to the number of
elements in each stratum
Systematic Sample
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Distance between elements = SAMPLING INTERVAL = K
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e.g. we want a sample of 144 = n, where N = 1300
N/n or 1300/144 = 9.02 this then is the
Sampling Interval K = 9
Using a random start, every 9th element would be selected
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Sampling Ratio = proportion of population to be selected
(N/n) where n = the desired sample size
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N/n
e.g. N = 1000 and n = 100
 Sampling Ratio = 1000/100
 Sampling Ratio = 1/10th or as per above 1/9th
A random sample of 100 or 144 elements would be selected
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Probability Sampling (cont)
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Cluster – large clusters of elements, not
individual elements, are selected in the first
stage of sampling
Area – Cluster sampling when the cluster
consist of a geographical area
Multistage Area – Cluster sampling that
involves a combination of two or more
probability sampling techniques