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8/2/2019 2 Sampling & Sample Size
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Chapter
Sample Designs
and
Sampling Procedures
PROF. REKHA SARAF
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Sampling may be defined as theselection of some part of aggregrate ortotality on basis of which a judgement ismade.
It is a simple process of learning abt thepopulation on basis of sample drawn
from it.
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Sampling Terminology
Sample
Population or universe
Sampling Frame
Census
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Sample
Subset of a larger population Sampling element Is Usually a human respondent
who may be customer, dealer or a person exposed toan advertisement. Others can be stores/ companies irfamilies or households so on.
PopulationPredefined set of potential respondents in a geog area
Any complete group
People eg: all teenagers who watch MTV
Sales territories eg: rural areas/income classificaton/ user classification
Stores
Census Investigation of all individual elements
that make up a populationPROF. REKHA SARAF
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Define the target population
Select a sampling frame
Conduct fieldwork
Determine if a probability or
nonprobabilitysampling method will be chosen
Plan procedurefor selecting sampling units
Determine sample size
Select actual sampling units
Stages in the
Selection
of a Sample
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Target Population
Relevant population
Operationally define
Comic book reader?
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Sampling Frame
Easy to define TG population Difficult to identify each member of such
a TG population
From a sampling frame we select a
sample for our research. Eg: use a TT directory to define all the
adult population in mumbai
A list of elements from which thesample may be drawn
Working population
Mailing lists - data base marketers
Sampling frame error
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Two Major Categories ofSampling
Probability sampling
Known, nonzero probability for every element
Chance of every unit in population beingincluded is known
Unbiased and scheme for selection of units fromTG is specified .
Non-probability sampling
Probability of selecting anyparticular member is unknown
Although PS is accurate n morescientific, NPS is preferred cuz ofconvinience and economy.
Actaul sample selection is left to theinterviewer and rsample is randomlyselected
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Non-probability
SamplingConvenience
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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NPS_ConvenienceSampling
Also called haphazard oraccidental sampling
The sampling procedure ofobtaining the people or units
that are most convenientlyavailable
Eg: TV reporters catch anyperson passing by forinterview
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NPS-Judgment
Sampling Also called purposive
sampling
An experienced individualselects the sample based on
his or her judgment aboutsome appropriatecharacteristics required of thesample member
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NPS-Quota Sampling
Ensures that the varioussubgroups in a population are
represented on pertinentsample characteristics
To the exact extent that theinvestigators desire
It should not be confused
with stratified sampling.
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NPS- Snowball
Sampling A variety of procedures
Initial respondents areselected by probabilitymethods
Additional respondents areobtained from information
provided by the initialrespondents
Eg: go to Benz owner and ask whether he knowssomeone who owns the Benz too.
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PS-Simple Random
SamplingA sampling procedure that
ensures that each element
in the population willhave an equal chance ofbeing included in the
sampleAlso known as chance
sampling
Like conducting a lottery.
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PS-Systematic
Sampling A simple process
Every nth name from the listwill be drawn
Every 10th house on the street.
Sampling frame is availableand hence this kind of
sampling becomes possible. Eg. Directory or roll numbers.
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Ps- Stratified Sampling
Probability sample
Sub-samples are drawnwithin different strata
Each stratum is more or lessequal on some characteristic
Do not confuse with quotasample
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Ps- Cluster Sampling
The purpose of clustersampling is to sampleeconomically while retainingthe characteristics of aprobability sample.
The primary sampling unit isno longer the individual
element in the population The primary sampling unit is
a larger cluster of elements
located in proximity to oneanother
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Population Element Possible Clusters
Indianpopulation States
Region
DistrictTahasilBlocksVillageHouseholds
Examples of Clusters
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What is theAppropriate Sample
Design?Degree of accuracy
Resources
Time
Advanced knowledge of
the populationNational versus local
Need for statistical
analysis
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Size of the sample one which fulfills the requirements of efficiency,
representativeness, reliability and flexibility.
Small enough to avoid unncess expenses andlarge enough to avoid sampling errors.
FACTORS TO BE CONSIDERED IN SAMPLE SIZE:
1. The Size of the Universe: larger d universe size-largerthe sample
2. The resources available: if Rrcs r vast- large samplecan b taken
3. The degree of accuracy or precision required:
4. Greater precision requires larger sample selection
5. Homogeneity or heterogeneity: homo-small/ hetero-
large
6. Nature of study: studies which r not repititve- larhgersample
7. Method of sampling adopted : simple randomsample/ stratified=small 21PROF. REKHA SARAF
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Sample Size Formula
2
E
zsn
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Sample Size Formula
- Example
Suppose a survey researcher,studying expenditures on
lipstick, wishes to have a 95
percent confident level (Z) and
a range of error (E) of less
than $2.00. The estimate of the
standard deviation is $29.00.
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Sample Size Formula -
Example
2
Ezsn
( )( )2
00.2
00.2996.1
2
00.2
84.56
( )2
42.28
808
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2
E
pqZn
Sample Size for a
Proportion