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Collection of Data

Census Sampling

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Essentials of Sampling

Representative ness (spokespersons)

Adequacy (sufficient)

Independence

Homogeneity

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Principles of sampling

Principle (code) of statistical regularity

Principle of Inertia (inactive) of largenumbers

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METHODS OF SAMPLING

RANDOM SAMPLING NON RANDOM SAMPLING

SIMPLE OR RESRTICTED DELIBERATEUNRESTRICTED SAMPLING (no purpose) CONVENIENCESAMPLING QUOTA

SELFSELECTED

SNOW-BALLING

STRATIFIED SYSTEMATIC CLUSTER MULTI-STAGE

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SAMPLING

Random sampling also referred to asprobability sampling

It does not mean haphazard or hit ormiss method

Every item has equal chance of being

included

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Random Sampling

Methods:

1) Lottery method2) Tables of random numbers:a) Tippets random tablesb) Fishers and Yates tables

c) Kendall and Smithd) Rand Corporation

3) Software packages

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Random Sampling

A- Simple or Unrestricted sampling

Each and every item has equal and

independent chance of being in thesample

No personal bias

No discretion or preference

Replacement of sampling unit

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Simple or Unrestricted sampling

Each unit is returned to the population, beforedrawing the next sample

Probability of every item is -1 / N

Otherwise, Population is reduced for successivestages-

next item will have probability of 1 / N-1,

next will be 1/N-2,

1 / N-3

When the slip is returned to the drum beforedrawing the next slip, the size of population is same

1000- 0 =10001000-1 =9991000-2 =9981000-3 =9971000-4 =9961000-5 =9951000-6 =994

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B) Restricted Sampling

1) Stratified random sampling

a) Proportionate stratified sampling

b) Disproportionate stratifiedsampling

2) Systematic sampling

3) Multi-stage sampling

4) Cluster sampling

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Stratified random sampling

N is divided into groups according tohomogeneity

Adopted when there are heterogeneousfeatures

For example, to study the consumptionpattern of Belgaum, the city is divided into anumber of groups / wards

Samples are taken from each ward

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Types of stratified sampling

Proportionate stratified randomsampling:

sample is in proportion to the sizeof sub-population

Disproportionate stratified random

sampling:

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2) Systematic sampling ( Equal intervalsampling or Quasi random sampling or k )

Sample is formed by selecting one unitat random and then selecting

additional units at evenly spacedintervals.

Used when a complete list is available

The list is prepared in alphabetical ornumerical order and serially numbered

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Systematic sampling- continued

The first item is selected at random

Then the remaining items are selected at k

interval k is the sample interval, which is selected as

follows:

k = Size of Universe = N

Size of Sample n

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Systematic sampling-continued

Also known as equal interval samplingor quasi random sampling as the

subsequent units are pre-determined Merits: 1) Simple and convenient to

adopt

2) More efficient than simple random

3) Time, cost and work- become smaller

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Systematic sampling-continued

Demerits : 1) not truly a random methodas there are pre determined intervals

2) Problem of representative ness asthere may be a hidden periodicities

( every k th worker may be a well paid)

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Multi-stage sampling

Sampling is carried out at severalstages

Sample is taken from previous stagesample

Sample of one stage becomespopulation for next stage

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Multi-stage sampling

Merits :1) Flexibility

2) No bias

3) Cost and efforts are less as Nbecomes smaller in successive stages

Demerits: 1)Less accurate

2) More work

3) Not representative as stratified

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Cluster sampling

Primarily selection of groups rather thanindividuals

Population is divided into groups

Groups are mutually exclusive andcollectively exhaustive

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Cluster sampling - continued

Merits :1) Easier and Practical

Demerits : 1) Difficulty in clustering

2) Unequal

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Non random sampling

1) Purposive or Judgment samplingalso known as Deliberate (no purpose)sampling It is a conscious selection by an

investigator on his own judgment.It requires deep and thorough knowledge and

experience. Interviewer requiresawareness of the characteristics of the

population.Samples vary from investigator toinvestigator

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Purposive or Judgment sampling

Merits : 1) used in solving economic andbusiness problems

2) No missing characteristics

3) Properties of population are known

4) Appropriate for pilot surveys

5) Motivation to the investigator

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Purposive or Judgment sampling

Demerits :1) not scientific

2) Personal prejudice (injustice)

3) Difficult to calculate sampling errors

4) Inclination or convenience , but notjudgment

5) Comparison of work is difficult

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Convenience samplingalso known as chunk

Chunk is a fraction of population whichis selected neither by probability nor

by judgment, but by convenience.

Samples are drawn from readily

available lists such as automobilesregistration, telephone directory, etc

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Convenience sampling

Convenience sample is not random,although samples are drawn at

random from the lists.

Good for Pilot studies

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Quota sampling

It is a type of judgment sampling

Quotas are set up on characteristics

such as income group, age, politicalaffiliation, religious affiliation ,etc

The interviewer is asked to interview acertain number of persons in thequota. He is free to chose any personin the given quota.

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Quota sampling- continued

For example:

In a T V survey, the interviewer is told to interviewany 500 persons and out of every 100 following

should be the composition:20 - Students

10 Housewives

25 Office goers

15 Children20 - Farmers

10 - Businessmen

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Self selected or presenting sampling

Sample is not selected by theinvestigator, but they themselves

propose to be included in the sample

These persons have vital interest and

can spare Ex: Questionnaire in newspaper

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Snowball sample

Researcher asks the respondent fornames of other individuals who are

also to be surveyed The difficulty is close friends or

colleagues tend to behave in the same

way

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STEPS IN SAMPLING PROCESS

DEFINE POPULATION

SPECIFY SAMPLING FRAME

SPECIFY SAMPLING METHOD

DETERMINE SAMPLE SIZE

SPECIFY SAMPLE PLAN

SELECT SAMPLE & COLLECT INFORMATION

ANALYZE DATA & REPORT RESULTS

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Size of sample

No hard and fast rule

Depends on subject, time, cost and

accuracy

Considerations

1) Size of population

2) Accuracy desired

3) Homogeneity / heterogeneity

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Size of sample-continued

4) Nature of study- intensive,continuous, technical, general

5) Practical considerations- time,finance, personnel

6) Type of sampling- stratified, etc

7) Size of questionnaire

8) Question on questionnaire

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Size of sample-continued

Sampling

Errors

Sample Size

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Marketing conditions affecting sampling

Some characteristics of marketing population:

a) Population is nether uniform nor

concentratedb) Characteristics of people are not simple as

there are so many factors

c) Data on desired characteristics are non-existent, or inaccurate

d) Identity of specific population is difficult