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Sample Designs and Sampling
Procedures
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Sampling The process ofsampling involves using a
small number of items or parts of the
population to make conclusions regarding
the whole population. The purpose of
sampling is to estimate some unknown
characteristic of the population.
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Sampling Terminology Sample
Population or universe Population element
Census
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Sample Subset of a larger population
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Population Any complete group that share some
common characteristics
People
Sales territories
Stores
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Census Investigation of all individual elements that
make up a population
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WHY SAMPLE? PRAGMATIC REASONS: Applied
business research projects usually havebudget and time constraints.
It would not be possible to contact the
whole population within a short period oftime.
Sampling cuts costs, reduces manpower
requirements, and gathers vital information
quickly.
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ACCURATE AND RELIABLE RESULTS:properly selected samples are sufficiently accurate in most
cases. Even when the populations have considerable
heterogeneity, large samples provide data of sufficient
precision to make most decisions.
A sample may be more accurate than a census. In a censusof a large population, there is greater likelihood of non-
sampling errors. In a field survey a small, well-trained,
closely supervised group may do a more careful and
accurate job of collecting information than a large group of
nonprofessional interviewers trying to contact everyone.
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DESTRUCTION OF TEST UNITS: Many
research projects, especially those in quality
control testing, require the destruction of the
items being tested.
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Define the target population
Select a sampling frame
Conduct fieldwork
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure
for selecting sampling units
Determine sample size
Select actual sampling units
Stages in the
Selection
of a Sample
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Target Population TOWHOM DOWEWANT TO TALK
It is the complete group of specific
population elements relevant to the research
project. E.g. in Industrial Buyer Behaviour
Relevant population
Operationally define
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Sampling Frame A list of elements from which the sample
may be drawn
Also called theWorking population Sampling frame error occurs when certain
elements are excluded or when the entire
population is not accurately represented in
the sample frame. It is possible for elements
to be either over- or underrepresented in a
sampling frame.
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Sampling Units
The sampling unit is a singles element or group of
elements subject to selection in the sample. For example, if
an airline wishes to sample passengers, every 25th name
on a complete list of passengers may be taken. In this case
the sampling unit would be the same as the element.
Primary Sampling Units (PSU)
Secondary Sampling Units
Tertiary Sampling Units
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Two Major Categories of Sampling
Probability sampling Known, nonzero probability for every element eachmember of the population has an equal probability
of being selected.
Non-probability sampling
Probability of selecting any particular member is
unknown
no appropriate statistical techniques for measuringrandom sampling error from a nonprobability
sample. Thus, projecting the data beyond the sample
is inappropriate.
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Nonprobability Sampling Convenience
Judgment
Quota
Snowball
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Probability Sampling Simple random sample
Systematic sample
Stratified sample
Cluster sample
Multistage area sample
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Convenience Sampling
Also called haphazard or accidental
sampling
The sampling procedure of obtaining the
people or units that are most conveniently
available
Best suited for exploratory research
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Judgment Sampling
Also called purposive sampling An experienced individual selects the
sample based on his or her judgment about
some appropriate characteristics required ofthe sample member
Especially used to forecast election results
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Quota Sampling Ensures that the various subgroups in a
population are represented on pertinent sample
characteristics to the exact extent that the
investigators desire
The interviewer has a quota to achieve Respondents are selected according to a
convenience sampling procedure rather than on a
probability basis, the haphazard selection of
subjects may introduce bias. Quota samples havethe tendency to include people who are easily
found, willing to be interviewed, and middle class.
It should not be confused with stratified sampling.
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Snowball Sampling A variety of procedures
Initial respondents are selected by
probability methods
Additional respondents are obtained from
information provided by the initial
respondents
Used to locate members of rare populations
by referrals.
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Simple Random Sampling A sampling procedure that ensures that each
element in the population will have an equal
chance of being included in the sample
Random numbers table can be used in large
population
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Systematic Sampling A simple process
Every nth name from the list will be drawn
Sampling Interval = population size/sample
size
The problem ofperiodicity occurs if a list
has a systematic pattern, that is, if it is not
random in character.
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Stratified Sampling
Probability sample Subsamples are drawn within different strata
Each stratum is more or less equal on some characteristic
Do not confuse with quota sample
First a variable is identified for basis of stratification. The variable chosen should increase the homogeneity
within each stratum and increase the heterogeneity
between strata. The stratification variable is usually a
categorical variable or one easily converted into categories,
that is, subgroups.
Next, for each separate subgroup or strata, a list of
population elements must be obtained
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Cluster Sampling
A population is divided into clusters usingnaturally occurring geographic or other
boundaries. The clusters are randomly selected
and a sample is collected by randomly selecting
from each cluster. The purpose of cluster sampling is to sample
economically while retaining the characteristics of
a probability sample.
The primary sampling unit is no longer theindividual element in the population
The primary sampling unit is a larger cluster of
elements located in proximity to one another
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Cluster
Population divided into manysubgroups
Each subgroup has fewelements in it
Subgroups are selectedaccording to some criterion ofease
Heterogeneity within subgroups
Homogeneity betweensubgroups
Random choice of subgroups
Stratified
Population divided into few subgroups
Each group has many elements in
it.
Subgroups are selected according
to some criterion that is related to
the variables under study
Homogeneity within subgroups
Heterogeneity between subgroups
Choice of elements from withineach subgroup
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What is the
Appropriate Sample Design? Degree of accuracy
Resources
Time
Advanced knowledge of the population
National versus local Need for statistical analysis