1.

Library Assignment

25 marks

(Que. a) Explain the concept of sampling including types of sampling/methods of

sampling

Sampling
In statistics, quality assurance, and survey methodology, sampling is concerned
with the selection of a subset of individuals from within a statistical population to
estimate characteristics of the whole population. Each observation measures one
or more properties (such as weight, location, color) of observable bodies
distinguished as independent objects or individuals. In survey sampling, weights
can be applied to the data to adjust for the sample design, particularly stratified
sampling. Results from probability theory and statistical theory are employed to
guide the practice. In business and medical research, sampling is widely used for
gathering information about a population.

The sampling process comprises several stages:

Defining the population of concern

Specifying a sampling frame, a set of items or events possible to measure

Specifying a sampling method for selecting items or events from the frame

Determining the sample size

Implementing the sampling plan

Sampling and data collecting

Data which can be selected

A population can be defined as including all people or items with the

characteristic one wishes to understand. Because there is very rarely enough
time or money to gather information from everyone or everything in a
population, the goal becomes finding a representative sample (or subset) of that
population.

Sampling Methods can be classified into one of two categories:

1. Probability Sampling: Sample has a known probability of being selected
2. Non-probability Sampling: Sample does not have known probability of

being selected as in convenience or voluntary response surveys

Probability Sampling
A probability sampling scheme is one in which every unit in the population has a
chance (greater than zero) of being selected in the sample, and this probability
can be accurately determined.
When every element in the population does have the same probability of
selection, this is known as an 'equal probability of selection' (EPS) design. Such
designs are also referred to as 'self-weighting' because all sampled units are
given the same weight. The following sampling methods are examples
of probability sampling:
Simple Random Sampling (SRS)
Stratified Sampling
Cluster Sampling
Systematic Sampling
Multistage Sampling (in which some of the methods above are
combined in stages)
Simple Random Sampling:

Applicable when population is small, homogeneous & readily available

All subsets of the frame are given an equal probability. Each element of
the frame thus has an equal probability of selection.

It provides for greatest number of possible samples. This is done by

assigning a number to each unit in the sampling frame.

A table of random number or lottery system is used to determine which

units are to be selected.

Estimates are easy to calculate.

Simple random sampling is always an EPS design, but not all EPS
designs are simple random sampling.

Disadvantages

If sampling frame large, this method impracticable.

Minority subgroups of interest in population may not be present in

sample in sufficient numbers for study.

Systematic Sampling

Systematic sampling relies on arranging the target population

according to some ordering scheme and then selecting elements at
regular intervals through that ordered list.

Systematic sampling involves a random start and then proceeds with

the selection of every kth element from then onwards. In this case,
k=(population size/sample size).

It is important that the starting point is not automatically the first in the
list, but is instead randomly chosen from within the first to the kth
element in the list.

A simple example would be to select every 10th name from the

telephone directory (an 'every 10th' sample, also referred to as
'sampling with a skip of 10').

As described above, systematic sampling is an EPS method, because

all elements have the same probability of selection (in the example
given, one in ten). It is not 'simple random sampling' because different
subsets of the same size have different selection probabilities - e.g. the
set {4,14,24,...,994} has a one-in-ten probability of selection, but the
set {4,13,24,34,...} has zero probability of selection.

ADVANTAGES

Sample easy to select

Suitable sampling frame can be identified easily

Sample evenly spread over entire reference population

DISADVANTAGES

Sample may be biased if hidden periodicity in population coincides with

that of selection.

Difficult to assess precision of estimate from one survey.

Stratified Sampling

Where population embraces a number of distinct categories, the frame

can be organized into separate "strata." Each stratum is then sampled
as an independent sub-population, out of which individual elements
can be randomly selected.

Every unit in a stratum has same chance of being selected.

Using same sampling fraction for all strata ensures proportionate

representation in the sample.

Adequate representation of minority subgroups of interest can be

ensured by stratification & varying sampling fraction between strata as
required.

Finally, since each stratum is treated as an independent population,

different sampling approaches can be applied to different strata.

Drawbacks to using stratified sampling.

First, sampling frame of entire population has to be prepared

separately for each stratum
Second, when examining multiple criteria, stratifying variables may be
related to some, but not to others, further complicating the design, and
potentially reducing the utility of the strata.
Finally, in some cases (such as designs with a large number of strata,
or those with a specified minimum sample size per group), stratified
sampling can potentially require a larger sample than would other
methods

Cluster Sampling

Cluster sampling is an example of 'two-stage sampling .

First stage a sample of areas is chosen;

Second stage a sample of respondents within those areas is selected.
Population divided into clusters of homogeneous units, usually based
on geographical contiguity.
Sampling units are groups rather than individuals.
A sample of such clusters is then selected.
All units from the selected clusters are studied.

Advantages :

Cuts down on the cost of preparing a sampling frame.

This can reduce travel and other administrative costs.

Disadvantages:

Sampling error is higher for a simple random sample of same size.

Often used to evaluate vaccination coverage in EPI

Identification of clusters

List all cities, towns, villages & wards of cities with their population
falling in target area under study.
Calculate cumulative population & divide by 30, this gives sampling
interval.
Select a random no. less than or equal to sampling interval having
same no. of digits. This forms 1st cluster.
Random no.+ sampling interval = population of 2nd cluster.
Second cluster + sampling interval = 4th cluster.
Last or 30th cluster = 29th cluster + sampling interval

Two types of cluster sampling methods

One-stage sampling. All of the elements within selected clusters are

included in the sample.
Two-stage sampling. A subset of elements within selected clusters are
randomly selected for inclusion in the sample

Difference between Strata and Clusters

Although strata and clusters are both non-overlapping subsets of the
population, they differ in several ways.
All strata are represented in the sample; but only a subset of clusters are
in the sample
With stratified sampling, the best survey results occur when elements within
strata are internally homogeneous. However, with cluster sampling, the best
results occur when elements within clusters are internally heterogeneous
Multistages sampling

Complex form of cluster sampling in which two or more levels of units are
embedded one in the other
First stage, random number of districts chosen in all states
Followed by random number of talukas, villages
Then third stage units will be houses.
All ultimate units (houses, for instance) selected at last step are surveyed.

Non-Probability Sampling
Any sampling method where some elements of population have no chance of
selection (these are sometimes referred to as 'out of coverage'/'undercovered'),
or where the probability of selection can't be accurately determined. It involves
the selection of elements based on assumptions regarding the population of
interest, which forms the criteria for selection. Hence, because the selection of
elements is non-random, non-probability sampling not allows the estimation of
sampling errors.
Example: We visit every household in a given street, and interview the first
person to answer the door. In any household with more than one occupant, this is
a non-probability sample, because some people are more likely to answer the
door (e.g. an unemployed person who spends most of their time at home is more
likely to answer than an employed housemate who might be at work when the
interviewer calls) and it's not practical to calculate these probabilities.
Non-probability Sampling includes: Accidental Sampling, Quota Sampling and
Panel Sampling. In addition, non response effects may turn any probability
design into a non-probability design if the characteristics of non response are not
well understood, since non response effectively modifies each element's
probability of being sampled.
Quota Sampling

The population is first segmented into mutually exclusive sub-groups,

just as in stratified sampling.
Then judgment used to select 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.
It is this second step which makes the technique one of non-probability
sampling.
In quota sampling the selection of the sample is non-random.
For example interviewers might be tempted to interview those who
look most helpful. The problem is that these samples may be biased
because not everyone gets a chance of selection. This random element
is its greatest weakness and quota versus probability has been a
matter of controversy for many years

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 using such a sample cannot scientifically make
generalizations about the total population from this sample because it
would not be representative enough.
For example, if the interviewer was to conduct 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 be conducted at different times of day and
several times per week.
This type of sampling is most useful for pilot testing.
Panel Sampling
Method of first selecting a group of participants through a random
sampling method and then asking that group for the same information
again several times over a period of time.
Therefore, each participant is given same survey or interview at two or
more time points; each period of data collection called a "wave".
This sampling methodology often chosen for large scale or nation-wide
studies in order to gauge changes in the population with regard to any
number of variables from chronic illness to job stress to weekly food
expenditures.
Panel sampling can also be used to inform researchers about withinperson health changes due to age or help explain changes in continuous
dependent variables such as spousal interaction.
There have been several proposed methods of analyzing panel sample
data, including growth curves.

(Que. b) Explain the importance and relevance of research in business. Take

illustrative examples if necessary.

Successful businesses worldwide periodically conduct market research in order to

stay tuned to changing market trends and to retain their competitive edge.
Whether your business is in a start-up stage or in an expansion phase, market
research is vital for understanding the critical characteristics of your target
market to increase sales revenue, profit, ROI and overall business success.
The importance of market research can be best perceived by understanding the
various factors that impact your business.
Market Information
This includes factors such as market size (in terms of number of customers),
sales revenues (for existing products), market segmentation (geographic,
gender, personality, etc.), the demand and supply scenario, and other factors.
Insight into Existing Customers
This requires understanding important customer decision-making triggers such
as:

Why do customers select your products or those of your competitors?

What value do they perceive in your products?

What is the key decision factor? Is it service, product quality or prestige?

What factors influence buying decisions?

Which magazines and websites do they browse?

Identifying Potential Customers

New customers can be identified by understanding:

Who are likely to use your products?

What is their age group?

What is their gender?

What is their marital status (single, married or divorced)?

How many children do they have?

Where do they reside?

Customer Needs
Evaluate and understand the precise needs of potential customers and develop
your products and services accordingly. Even existing products and services can
be suitably modified based on the results of such market research.
Customer Behavior Patterns
Potential customers may exhibit behavior patterns and preferences, such as a
preference for only one brand or switching brands frequently; trying out new
products; a preference for products of a particular type, size, color, price range,
or other such parameters.
Competitor Analysis
Research can provide you with valuable information about existing competitors,
their adopted strategies, impact on target consumers (and their reactions), and
other such details.
Identify Business Opportunities
Market research helps identify existing gaps and new business opportunities such
as untapped or underserviced markets, as well as changing market trends such
as population shifts, higher education levels, increased leisure spends and more.
Resolving Business Problems
A market research company can identify the cause of your business problems, if
any, and suggest possible remedies. For example, decreased sales could result
from the entry of a new competitor, a substitute product, reduced brand
awareness or negative brand image.
Accurate Business Decisions
The information collected through research can enable you to set realistic
expectations and make appropriate estimations about sales forecasting, market
share, growth rate and other such critical factors.
Develop Business Strategies

Research data can help you in developing strategies for product or service
pricing, distribution and logistics, advertising media usage (radio, TV,
newspapers etc.), making decisions for new products and services, and timing
the market. It can guide you towards better decisions on starting, consolidating,
diversifying or reducing business activity.
The significance of market research is seen in the following benefits accrued by
businesses:

Improved communications between the company and its target customers

through accurate understanding of customer needs and meeting them
effectively, leading to enhanced customer satisfaction

Minimized risk through precise analysis of the customer's exact demands

and developing perfectly matching products and services, thus reducing
errors and failures

Measuring business success through analysis of the research data and

evaluation of business progress and growth. Even possible pitfalls and
solutions can be gauged

Example of two successful businesses using research

Apple
Apple has been the largest name in technology for years. This is not necessarily
because they are the most innovative. Instead, it is because they use market
research to find out exactly what their customers want from their devices; they
then figure out how to make those wants a reality.
Their Apple Customer Pulse research group is a prime example. Because these
are online surveys, the company is able to compile and analyze the data faster,
and the surveys are easy to administer, without much effort. This makes the
market research more appealing to those that participate, as well as to the
company.
These surveys have led to different designs and modifications of Apple products.
Such modifications include having bigger screens to view videos and games
more clearly.

McDonalds
McDonalds is one of the largest fast food chains in the world. In order to
continue this trend, McDonalds uses ongoing market research.
In their market research, they have narrowed their focus onto four different
questions. 1.) Which products are well received? 2.) What prices are consumers
willing to pay? 3.) What TV programs, newspapers and advertising consumers
read and view? 4.) Which restaurants are most visited?
By answering these questions, McDonalds is able to determine whether the pool
of their target customers is growing or not.

One of the problems addressed by this research was if McDonalds was serving
healthy or organic food. As a result, the company has launched a campaign to
prove that their meat is real. They also have changed part of their menu to
include healthier alternatives, such as apple slices.