SAMPLING

COMPILED BY:
MALVIKA – MBA BIOTECH – 11
RASHMI – MBA BIOTECH - 12

OUTLINE
 SAMPLING.
 SAMPLING PROCESS.
 SAMPLING TYPES.
 SAMPLING ERROR.
SAMPLE SIZE.

 The process of obtaining
information from a sample of
a larger group (population).
 A sample is “a smaller (but
hopefully representative)
collection of units from a
population used to determine
truths about that population”.

Characteristics of Good Samples :
Representative
Accessible
Low cost

1. Define the population :
The Element ...... individuals
families
seminar groups
Sampling Unit…. individuals over 20
families with 2 kids
seminar groups at ”new”
university
Extent ............ individuals who have bought
“one”
families who eat fast food
seminar groups doing MR
Timing ......... bought over the last seven

2. Identify the sampling frame :
Select “sample units”
 Individuals
 Household
 Streets
 Telephone numbers
 Companies

3. Select a sampling design or procedure :
 PROBABILITY
 NON- PROBABILITY
4. Determine the sample size.
5. Draw the sample.

Probability sampling - equal chance of being
included in the sample (random)
-simple random sampling
-systematic sampling
-stratified sampling
-cluster sampling
Non-probability sampling - unequal chance of
being included in the sample (non-random)
-convenience sampling
-judgment sampling
-snowball sampling
-quota sampling

SIMPLE RANDOM SAMPLING
A sampling procedure in which every element in
the population has a known and equal chance of
being selected as a subject (e.g., drawing names
out of a hat).

SYSTEMATIC SAMPLING
If a sample size of n is desired from a population
containing N elements, we might sample one
element for every n/N elements in the population.

STRATIFIED SAMPLING
Population is divided on the basis of
characteristic of interest in the population e.g.
male and female may have different
consumption patterns.

Cluster or Area Random Sampling
Clusters of population
units are selected at
random by dividing
the population into
clusters (usually
along geographic
boundaries) and then
all or some randomly
chosen units in the
selected clusters are
studied.

CONVENIENCE SAMPLING
Sometimes known as grab or opportunity
sampling or accidental or haphazard sampling.
A type of non probability sampling which
involves the sample being drawn from that part of
the population which is close to hand. That is,
readily available and convenient.

 The researcher chooses the sample
based on who they think would be
appropriate for the study.
 This is used primarily when there is a
limited number of people that have
expertise in the area being researched.
JUDGMENTAL SAMPLING

SNOWBALL SAMPLING
 Selection of additional respondents is based
on referrals from the initial respondents.
- friends of friends
 Used to sample from low incidence or rare
populations.

Quota sampling is a method for selecting survey
participants.
In quota sampling, a population is first segmented into
mutually exclusive sub-groups, just as in stratified
sampling.
Then judgment is used to select the subjects or units
from each segment based on a specified proportion.
For example, an interviewer may be told to sample 200
females and 300 males between the age of 45 and 60.
This means that individuals can put a demand on who
they want to sample (targeting).
QUOTA SAMPLING

Random Sampling Error
 Random error- the sample
selected is not representative of
the population due to chance.
 The level of it is controlled by
sample size.
 A larger sample size leads to a
smaller sampling error.

Non-sampling Error
 Systematic Error
 The level of it is not
controlled by sample size.

A response or data error is any systematic bias that
occurs during data collection, analysis or
interpretation, like:
Respondent error (e.g., lying, forgetting, etc.).
Interviewer bias.
Recording errors.
Poorly designed questionnaires.
The basic types of non-sampling error :
A non-response error occurs when units selected as
part of the sampling procedure do not respond in
whole or in part .

Non-probability sampling is less time
consuming and less expensive.
The probability of selecting one element
over another is not known and therefore
the estimates cannot be projected to the
population with any specified level of
confidence.
.

Size of sample should be determined by a
researcher keeping in view:
1. Nature of universe: homo (small sample)
 hetero (large sample).
2. No. of classes proposed: directly proportional to
the sample size .
3. Nature of study: general (large)
 intensive (small).
4.Type of sampling: small random sample is better
than a large but bad one.

5. Standard of accuracy: high level of precision
large sample.
6. Availability of finance: sample size =amount of
money available.
7. Other considerations: size of population,
 size of questionnaire,
 nature of units,
 conditions.

www.socialresearchmethods.net/kb/sampling.php
en.wikipedia.org/wiki/Sampling_(statistics)
psychology.ucdavis.edu/sommerb/.../sampling/types.
htm
www.investopedia.com/terms/s/samplingerror.asp
www.slideshare.net/dfmoore/sampling-size
Research Methodology - C.R.Kothari.

SAMPLING

SAMPLING

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