2 Sampling & Sample Size

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Chapter

Sample Designs

and

Sampling Procedures

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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

2 Sampling & Sample Size

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