Non Random sampling (Final)

8/7/2019 Non Random sampling (Final)

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NON-RANDOM SAMPLING



Non Random Sampling Non Random sampling is method where The population

elements (Samples) are selected on the basis of Availability.

(People out of population volunteer themselves for the

research)

Some elements of the population have no chance of selection Or

where the probability of selection can not be accurately

determined.

It involves the selection of elements based on assumptions,

population of interest or selection criteria.



Example:

Interviewing 25 people with AIDS about there

experiences with HIV

could provide valuable insightsabout the stress and coping, rather than interviewing

people who do not suffer from AIDS Virus.



Non-random sampling is useful when descriptive

comments about the sample is desired.

Non-random sampling is biased sampling, as it is

certainly introduced.

The Samples are also selected on the researchers

personal judgment.



TYPES OF NON RANDOM SAMPLING

1. Convenience Sampling

2. Quota Sampling

3. Volunteer Sampling

4. Purposive Sampling

5. Snowball Sampling



Convenience Sampling Convenience sampling sometimes known as grab or

opportunity sampling.

It is a type of non random sampling which involves the sample

being drawn from the part of population which is easily

available.

A sample population selected because it is readily available andconvenient.

The researcher using such a sample cannot significantly make

generalizations about the total population from sample

because it would not be representative enough.



Example:

If the interviewer has to conduct such a survey at a

shopping center early in the morning on a given day,the people that he/she could interview would be

limited to those given there at that given time, which

would not represent the views of other members of

society in such an area, if the survey was to beconducted at different times of day and several times

per week.



Some more examples:-

the first ten cars to enter a car park

the first ten people to walk through a turnstile at a

sporting event, or

females in the first row of a concert.





Example:

Out of population of 5500 Men and 4500 Women,

quota samples or 100 individual ensure that 55 men

and 45 women are selected. The researcher goes out

the population and picks up people until the quotas

are filled up.



QUOTA V/S STRATIFIED SAMPLING

In Q uota Sampling, interviewer selects

first available subject who meets

criteria: is a convenience sample.

In Stratified Sampling, selection of

subject is random. Call-backs are used

to get that particular subject.

Highly controlled quota sampling uses

Probability sampling down to the last

block or telephone exchange

Stratified sampling without call-backs

may not, in practice, be much

different

fromQ uota sampling.





Example:

A television station may ask viewers to phone in to give their preferred

opinion on whether Australia should become a Republic. The station

would give two numbers to ring: one for Yes voters and the other forNo voters.

They would possibly give voters three hours to call after which the lines

would be closed and a conclusion formed. If 200 people called in, and

114 voted Yes and 86 voted No, then the television station would

report that 57% of callers voted Yes and 43% voted No. However, this

may or may not represent the opinion of the whole population.

However, the chance that the sample will be biased is very high because

only those with a telephone can vote, and only those watching television

or listening to radio at the time would be aware of the survey.



ADVANTAGE

The main advantages of phone-in sampling are that it is cheapin terms of time and money, and very easy to monitor and

control.



PURPOSIVE SAMPLING In this the researcher specifies the characteristics of the

population of interest and the locate/find the individuals who

match the characteristics.

Subjects selected for a good reason tied to purposes of

research

Small samples < 30, not large enough for power of probability

sampling

- Nature of research requires small sample



Example:

Market research firm decides to only include the boys

who are in 7th grade and have been diagnosed withH1N1 Virus. Then in this case the researcher will try

and find at least the 50 students who meet the

Inclusion criteria.

Some More Examples:-

Overweight children, First time mothers etc.





Example:

Study of the group of people who have power in the

area of educational policy making,In addition to thealready known positions of power like school board,

school superintendent, collage board etc.



Initi al Sample-2 F inal Sample-9



PROBLEMS WITH NON RANDOM SAMPLING

The problem with non-random samples is that there exist no

convincing method to generalize to the population.

Legitimate uses can include: controlled experiments, testing

questionnaires, exploring theoretical issues.

It may also be true (and it is often claimed) that changes can bemonitored adequately in volunteer samples. This is dubious.



CONTD..

Most users do not understand the legitimate uses and non uses

of non-probability samples. In particular we see a lot of over-interpretation of volunteer samples.

Another common misunderstanding is that users do not take

into account that non-probability samples (including purposive

samples) are still subject to random sampling error.



PGD

M Evening,2009-2012PRESENTED BY:

Abhishek Kumar(13/2009)

Chaitansi Sharma(16/2009)

Abhishek Kohli(17/2009)

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Non Random sampling (Final)