SAMPLING DESIGN
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CENSUS VERSUS SURVEY
Census collects information about
every member of the population
Survey collects information from
sample of the population
Census is more detailed and
accurate.
Survey is not accurate or reliable as
a census
Census takes long time to complete
Survey can be done in a shorter
period of time compared to census.
Census is generally conducted by
the government
Census are not conducted
frequently
Surveys can be conducted by anyone.
Surveys can be conducted more
frequently .
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SAMPLE
A sample is “a smaller (but
hopefully representative) collection
of units from a population used to
determine truths about that
population” (Field, 2005)
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SAMPLING
…….
STUDY POPULATION
SAMPLE
TARGET POPULATION
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WHY SAMPLING?
Get information about large populations with
 Less costs
 Less field time and budget constraint (Maximize information with
minimum resources)
 More accuracy i.e. Can Do A Better Job of Data Collection
 When it’s impossible to study the whole population, for
example Topographic limitations and when sometimes the sampling process
is destructive.
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POPULATION FRAME
A list, map, directory, or other source used to represent the population
Overregistration -- the frame contains all members of the target
population and some additional elements
Example: using the chamber of commerce membership directory as the
frame for a target population of member businesses owned by women.
Underregistration -- the frame does not contain all members of the
target population.
Example: using the chamber of commerce membership directory as the
frame for a target population of all businesses.
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THE SAMPLING DESIGN PROCESS
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
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FACTORS TO CONSIDER IN SAMPLING DESIGN
Work objectives
Degree of accuracy
Resources
Time frame
Knowledge on population
Scope
Statistical analysis needs
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Classification of Sampling Techniques
In which the chances
(probability) of
selecting members from
the population are
unknown
Nonprobability
Sampling
Techniques
Sampling
Techniques
In which members of
the population have a
known chance
(probability) of being
selected
Probability
Sampling
Techniques
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Nonprobability Sampling Techniques
Nonprobability Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
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NON-PROBABILITY SAMPLING
Convenience sampling: selection is based on
easiness (the right place at the right time).
Judgmental (purposive)sampling: selection based
on the judgment of the researcher.
Snowball sampling, an initial respondent is selected
and subsequent respondents are selected based on
the referrals.
Quota sampling/stratified: is a two-stage restricted
judgmental sampling ( develop quotas of population
elements and then select)
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SNOWBALL SAMPLING
Person 1
RESEARCHER
Friend/contact 1 contacts
his/her own
friends/contacts/
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RESEARCHER HAS 3 CONTACTS
Friend/contact 2 contacts
his/her own
friends/contacts/
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7
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Friend/contact 3 contacts
his/her own
friends/contacts/
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10
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THE 3 CONTACTS EACH HAVE 3 CONTACTS
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Strengths and Weaknesses of Basic Sampling Techniques
________________________________________________________________
Technique
Strengths
Weaknesses
________________________________________________________________
Nonprobability Sampling
Convenience
sampling
Least expensive;
least time
consuming;
most convenient
Selection bias;
sample not
representative;
not recommended
for descriptive or
causal research
Judgmental
sampling
Low cost;
convenient;
not time
consuming
Does not
generalization;
subjective
Quota
sampling
Sample can be
controlled for
certain characteristics
Selection bias;
no assurance of
representativeness
Snowball
Can estimate
Time consuming
rare characteristics
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Probability Sampling Techniques
Probability Sampling Techniques
Simple Random
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
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PROBABILITY SAMPLING
Simple Random Sampling: Each element has a known and equal
probability of selection.
Systematic Sampling: A random starting point and then picking
every ith element .
Stratified Sampling: A two-step process. Population is partitioned
into subpopulations and then elements are selected from each
stratum by a random procedure.
Cluster Sampling: First clusters are formed and then a cluster is
randomly selected. For each selected cluster, either all the
elements are included in the sample (one-stage) or a sample of
elements is drawn probabilistically (two-stage).
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Simple Random Sampling
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Every unit has an equal non-zero chance of being selected
Advantages:
Known and equal chance of selection (most representative)
Easy method when there is an electronic database
Disadvantages:
Complete accounting of population needed (Difficult to
identify every member of a population)
This method is the purest form of probability sampling
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Systematic Sampling
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The defined target
population is ordered and
the sample is selected
according to position using a
skip interval ((e.g., every 5th
item in alphabetized list,
every 10th name in phone
book)
Systematic sampling is frequently used to select a specified
number of records from a computer file.
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Advantages:
Known and equal chance of selected interval
Less expensive…faster than Radom methods
Disadvantages:
Loss in sampling precision (each item does not have
equal chance to be selected, System for selecting
subjects may introduce systematic error, Cannot
generalize beyond population actually sampled)
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SYSTEMATIC SAMPLING
Every nth person (e.g. every 4th person).
To find the frequency use the formula:
N
f 
sn
where
f = frequency interval;
N = the total number of the wider population;
sn = the required number in the sample.
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
In a company of 1,500 employees a sample
size of 306 is required (from tables of sample
size for random samples). The formula is:
This rounds to 5, i.e. every 5th person.
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Stratified Random Sampling
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The population is separated into homogeneous
strata and a sample is taken from each
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Stage 1: Divide the wider population into mutually
exclusive homogeneous groups.
Stage 2: Randomly sample within these groups, the
size of each group being determined by judgement or
tables of sample size.
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STRATIFIED SAMPLING
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STRATIFIED RANDOM SAMPLING
List of clients
South
North
East
Strata
Random subsamples of n/N
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Advantages:
More accurate overall sample of skewed
population
Disadvantages:
More complex sampling plan requiring different
sample sizes for each stratum
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Advantage
Better in achieving representativeness on control variable
Disadvantage
Difficult to pick appropriate strata
Difficult to Identify every member in population
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Cluster Sampling
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The population is divided into groups (clusters),
any of which can be considered a
representative sample.
Useful when it is difficult or costly to develop a
complete list of the population members or when
the population elements are widely dispersed
geographically.
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Types of Cluster Sampling
Divide Population into Cluster
Randomly Sample Clusters
One Stage
Include All Elements
from Each Selected
Cluster
Two-Stage
Randomly
Sample Elements
from Each Selected
Cluster
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Advantages:
Economic efficiency … faster and less expensive
Does not require a list of all members of the
population
Disadvantages:
Cluster specification error (the more homogeneous
the cluster chosen, the more imprecise the sample
results)
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PLANNING A
SAMPLING
STRATEGY
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Stage One: Decide whether you need a sample, or whether it is
possible to have the whole population.
Stage Two: Identify the population, its important features (the
sampling frame) and its size.
Stage Three: Identify the kind of sampling strategy you require (e.g.
which variant of probability, non-probability, or mixed methods sample
you require).
Stage Four: Ensure that access to the sample is guaranteed. If not,
be prepared to modify the sampling strategy.
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Stage Five: For probability sampling, identify the confidence level and
confidence intervals that you require. For non-probability sampling,
identify the people whom you require in the sample.
Stage Six: Calculate the numbers required in the sample, allowing for
non-response, incomplete or spoiled responses, attrition and sample
mortality.
Stage Seven: Decide how to gain and manage access and contact.
Stage Eight: Be prepared to weight (adjust) the data, once collected.
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HOW TO DETERMINE SAMPLE SIZE?
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HOW LARGE MUST MY SAMPLE BE?
The number of strata required;
The number of variables included in the study;
The variability of the factor under study;
The kind(s) of sample;
The representativeness of the sample;
The allowances to be made for attrition and non-response;
The need to keep proportionality in a proportionate
sample;
The kind of research that is being undertaken
(qualitative/quantitative/mixed methods).
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PROPORTION OF SAMPLE SIZE TO POPULATION
6000
5000
4000
SA M PLE
3000
POPU LA T ION
2000
1000
0
Note: As the population increases, the proportion of the
population in the sample decreases.
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SAMPLE SIZE
Ensure a sufficiently large sample for each
variable.
Samples in qualitative research must be large
enough to generate ‘thick descriptions’.
A large sample does not guarantee
representativeness; representativeness
depends on the sampling strategy.
Sample size also depends on the heterogeneity
or homogeneity of the population: if it is highly
homogeneous then a smaller sample may be
possible.
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SAMPLE SIZE
Large samples are preferable when:
there are many variables;
only small differences or small relationships are expected or
predicted;
the sample will be broken down into subgroups;
the sample is heterogeneous in terms of the variables under study;
reliable measures of the dependent variable are unavailable.
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HOW TO DETERMINE SAMPLE SIZE? FACTORS TO
CONSIDER
The variability of elements in the target population
The type of sample required
Time available
Budget
Required estimation precision
Whether the findings are to be generalized and, if so,
with what degree of confidence
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HOW TO DETERMINE SAMPLE SIZE? STATISTICAL
METHODS
When statistical methods are used to determined the sample, three
decisions must be made:
1) the degree of confidence (the level of risks involved, often
95%),
2) the specified level of precision (amount of acceptable error, the
maximum acceptable difference between the estimated sample
value and the true population value), and
3) the amount of variability (population homogeneity, measured by
standard deviation). The true SD is usually unknown and mostly
based on previous similar studies or a pilot study
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SAMPLING SIZE FOR LARGE POPULATION
Sample size (SS) = (DC X TV/DP)2
Where
DC is the number of standard errors for the degree of confidence specified for the
research result
TV is true variability, the standard deviation of the population
DP is desired precision, the acceptable difference between the sample estimate and
the population value
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We wish to estimate the average monthly expenditure on eating
out. Although the true standard deviation is unknown, a pilot test
study of 30 customer provides an estimate of the unknown
standard deviation of $14. We desire to be 95 percent
confidence that our estimate of the mean monthly expenditure on
eating out is within $2 of the true population mean. Assuming that
the distribution f expenditure follows a normal distribution, then the
sample size is
Sample size (SS) = (DC X TV/DP)2 = (1.96X14/2)2 =
196
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IN SMALL POPULATION
When working with small population, use of the above formula may lead to
unnecessarily large sample size. If the sample size is larger than 5 Percent of the
population, then the calculated sample size should be multiplied by the following
correction factor:
N/{N+(n-1)}
Where N= population size
n=the calculated sample size determined by the original formula
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FOR SMALL SIZE
Suppose a bank has 5,000 ATMS installed in AA. The bank wishes
to establish the customer‘s view of this service. A researcher
commission estimates the required sample size given their agreed
criteria is 750. This sample size is 13 percent of the population
and a larger than is necessary for an efficient sample size. In this
case the sample size correction factor needs to be applied, as
illustrated below.
Adjusted sample size = 750 (5000/{5000+750-1})= 653
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