Measurement Scales
 Measurement : The assignment of Numbers or other
symbols to characteristics of objects according to certain
pre-specified rules.
 Scaling: The generation of a continuum upon which
measured objects are located.
Primary Scales of Measurement
There are 4 kinds of scales namely:
 Nominal scale
 Ordinal scale
 Interval scale
 Ratio scale
Nominal scale
 In this scale numbers are used to identify objects. For
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example University Registration numbers assigned to
students.
Have you visited Bangalore?
Yes-1, No-2
Yes is coded as one and No is coded as Two. The numeric
attached to the answers has no meaning and is a mere
identification.
If the numbers are interchanged it wont affect the answer.
Example for nominal scale
 The telephone number is a example of nominal scale where
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one number is assigned to one subscriber.
Similarly bus route numbers are examples of nominal scale.
“How old are you? This is an example of nominal scale.
“What is your PAN Card Number?
Arranging the books in the library subject wise, author
wise
Limitations
 There is no rank ordering.
 No mathematical operation is possible.
 Statistical implication- calculation of standard deviation
and the mean is not possible
Ordinal scale (ranking scale)
 The ordinal scale is used for ranking in most of market
research studies.
 Ordinal scales are used to ascertain the consumer
perceptions, preferences etc.
 This is also known as ranking scale
Example of ordinal scale
 The respondents may be given a list of brands which may
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be suitable and were asked to rank on the basis of ordinal
scale.
Lux
Liril
Cinthol
Lifebuoy
Hamam
Example for ordinal scale
Rank
item
No of
Respondents
I
Cinthol
150
II
Liril
300
III
Hamam
250
IV
Lux
200
V
Lifebuoy
100
Total
1000
Nominal scale- contd
 In the previous example II is the mode and III is the
median.
 In market research the researchers often ask the
respondents to rank the items like for example “A soft drink
based upon flavor or Color”.
 In such cases the ordinal scale is used
Interval scale
 Interval scale is more powerful than the nominal and
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ordinal scale.
The distance given on the scale represents equal distance
on the property being measured.
Interval scale may tell us “How far object are apart with
respect to an attribute?”
This means that the difference can be compared.
The difference between 1 and 2 is equal to the difference
between 2 and 3.
Eg for interval scale
 Eg 1: Suppose we want to measure the rating of a
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refrigerator using interval scale it will appear as follows:
1 Brand name
Poor------------Good
2 Price
High-------------Low
3
Service after sales Poor-----------Good
4 Utility
Poor----------Good
Interval scale-contd
 The researcher cannot conclude that the respondent who
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gives rating of 6 is 3 times more favorable towards the
product under study than the respondent who awards the
rating of 2.
Eg 2: How many hours you spend to do class assignment
every day?
<3o min
3o min- 1 hr
1 hr- 1.5 hr
1.5 hr
Difference between nominal and ordinal scale
 In nominal scale numbers can be interchanged because it
serves only for the purpose of counting. Numbers in
ordinal scale have meaning and it won’t allow
interchangeability.
Difference between interval and ordinal scale
 Ordinal scale gives only the ranking of the alternatives, one
is greater than the other, but won’t give the
differences/distances between one and other. Interval
scales provide information about the difference between
one and other.
Ratio scale
 Ratio scale is a special kind of interval scale that has a
meaningful zero point. With this scale, length, weight, or
distance can be measured. In this scale it is possible to say,
how many times greater or smaller one object is being
compared to the other.
 Example: sales this year for product A are twice the sales for
the same product last year.
 Statistical Implications: All statistical operations can be
performed on this
Difference b/w 4 Scaling Techniques
Scale
Characteristics
Common Egs
Marketing Egs
Possible Statistics
Des
Inf
Nominal
Nos identify & Class objects
SSN, Numbers of
football players
Brand Nos, Store Types
%, mode
Chi
squar
e
Ordinal
Numbers indicate the relative
position of objects but not the
magnitude of differences
between them.
Quality rankings,
Ranking of teams in
tournament.
Preference Ranking, Market
Position, Social Class.
Percentile,
median
Rank
order
corrn
,
ANO
VA
Interval
Difference between objects
can be compared to zero point
is arbitrary.
Temperature
(Fahrenheit,
Centigrade).
Attitude opinions
Range,
mean, S.D
Prod
uct
mom
ent
corrn
,t
test,
ANO
VA
Ratio
Zero point is fixed. Ratios of
scale values can be computed.
Length, weight.
Age, Income, cost, sales,
Market Share.
Geometric
Mean, H.M
Coeff
of
Varia
Classification of Scaling Techniques
 Comparative Scales: One of two types of scaling
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techniques in which there is direct comparison of
stimulus objects with one another.
There are 4 types of comparative scaling namely:
Paired comparison
Rank order
Constant sum
Q-Sort and other procedures.
Classification of Scaling Techniques-Contd..
 Non-comparative scales: One of the two types of scaling
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techniques in which each stimulus object is called
independently of other objects in stimulus set.
There are 2 types of Non-comparative scales namely:
Continuous ranking scales
Itemized ranking scales : consisting of three types namely:
Likert Scale
Semantic Differential
Stapel
Sampling
 A sample is a part of a target population which is carefully
selected to represent the population.
 Sampling frame is the list of elements from which the
sample is actually drawn. Actually sampling frame is
nothing but the correct list of population. Example:
telephone directory, product finder, yellow pages.
Distinction between Census and Sample
 Census refers to complete inclusion of all elements in the
population. A sample is a subgroup of the population.
 Sampling unit: If individual respondents form the sample
elements and if we directly select some individuals in a
single step, the sampling unit is also the element. That is
both the unit and the element are the same.
When Census is appropriate
 When the size of the population is small.
 Sometimes the researcher is interested in gathering
information from every individual. Example: quality of
food served in a mess.
When Sample is Appropriate
 When the size of the population is large.
 When time and cost are the main considerations in
research.
 If the population is homogeneous.
 Also there are circumstances when census is not possible
Advantages of Sampling
 Sampling reduces time and cost of research studies.
 Sampling saves labor.
 The quality of study is often better with sampling.
 Sampling provides much quicker results.
 Sampling is the only procedure possible if the population is
infinite.
 Statistical sampling gains a advantage over any other
method.
Limitations of Sampling
 Sampling demands through knowledge of sampling
methods and procedures.
 When the characteristics to be measured occurs rarely in
the population, a very large sample is required to secure
units that will give reliable information about it.
 A complicated sampling plan requires more labor.
Sampling Process
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It consists of seven steps:
Define the population
Identify the sampling frame
Specify the sampling unit
Selection of sampling method
Determination of sample size
Specify the sampling plan
Selection of sample
Types of Sampling Design
 Sampling is divided in to 2 types:
 Probability sampling
 Non-Probability sample
Probability Sampling
 Probability sampling: In probability sampling, every unit in
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the population has a equal chances for being selected as a
sample unit.
The following are the characteristics:
Every population has a equal chance of being selected.
Such chance is known as probability.
Probability sampling yields a representative sample.
The closeness of a sample to the population can be
determined by estimating the sample bias or error.
Non Probability Sampling
 In the non probability sampling the units in the population
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have unequal or negligible , almost no chance for being
selected as a sample unit.
Its merits are as follows:
It does not ensure a selection chance to each population
unit
The selection probability is unknown
A non probability sample may not be a representative one.
Non probability sampling plan does not perform inferential
function.
Probability Sampling Techniques
 Simple random sampling
 Stratified random sampling
 Systematic random sampling
 Cluster sampling
 Area sampling
 Multi-Stage and sub-sampling
Non-Probability Sampling Techniques
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Convenience or accidental sampling
Purposive or deliberate or (Judgment) sampling
Shopping mall Intercept Sampling
Sequential sampling
Quota sampling
Snowball sampling
Panel samples
Random Sampling
 Random sampling or simple random sampling is a process
in which every item of the population has a equal
probability of being chosen.
 There are 2 methods used in the random sampling
 A) lottery method
 B) using random number tables
Advantages of Simple Random Sampling
 All elements in the population have an equal chance of
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being selected.
Of all the probability sampling techniques simple random
sampling is the easiest to apply.
It is the most simple type probability sampling to
understand.
It does not require prior knowledge of true composition of
the population.
The amount of sampling errors associated with any sample
can be easily computed.
Disadvantages of simple random sampling
 It is often impractical because of non availability of
population list.
 The use of simple random sample maybe wasteful if we fail
to use all of known information.
 It does not ensure proportionate representation of various
groups constituting the population.
Systematic random sampling
 There are 3 steps:
 Sampling interval k determined by the following
formula:
K= No of units in the population/No of units desired in
the sample
One unit between the first and kth unit in the
population list is randomly chosen.
Add kth unit to the randomly chosen number
Stratified random sampling
 A probability sampling procedure in which simple random
sub-samples are drawn from within different strata, that
are more or less equal on some characteristics. Stratified
sampling are of 2 types:
 Proportionate stratified sampling
 Disproportionate stratified sampling
Stratified random sampling- contd
 Sampling process is as follows:
 The population to be sampled is divided into groups
(stratified).
 A simple random sample is chosen.
Reasons for Stratified Sampling
 Marketing professionals want information about the
component part of the population.
 Stratified sampling can be carried out with
 Same proportion across the strata proportionate stratified
sample.
 Varying proportion across the strata disproportionate
stratified sample.
Cluster sampling
 The following steps are followed:
 The population is divided in to clusters.
 A simple random sample of few clusters is selected.
 All the units in the selected clusters are studied.
 The major advantage of cluster sampling is the case of
sample selection.
Cluster Sampling-Contd..
 Clustering is done on the basis of geographical area
 Heterogeneity is secured within subgroups
 Homogeneity is secured between subgroups
 Random selection of subgroups or clusters is done.
Cluster sampling process
 Identify clusters
 Examine the nature of clusters
 Determine the number of stages
 A) single stage
 B) two stage sampling
 C) multi stage sampling
Area Sampling
 This is a probability sampling, a special form of cluster
sampling.
 This is a important form of cluster sampling.
 In larger field surveys, clusters consisting of specific
geographical areas like districts, taluk, villages, or blocks in
a city are randomly drawn. As the geographical areas are
selected as sampling units in such cases, the sampling is
called area sampling.
Area sampling-example
 If someone wants to measure sale of a toffee in a retail
stores, one might choose a city locality and then audit
toffee sales in retail outlets in those localities.
 The main problem in area sampling is the non -availability
of lists of shops selling toffees in a particular area.
Therefore, it would be impossible to choose a probability
sample from these outlets directly. Thus the first job is to
choose a geographical area and then list out the outlets
selling toffee.
Multi Stage Sampling
 The name implies that sampling is done in several stages.
This is used with stratified/cluster design.
 In this method the sampling is carried out in 2 or more
stages. The population is regarded as being composed of a
number of first stage sampling units. each of them is made
up of number of second stage units and so forth. That is at
each stage, a sampling unit is a cluster of the sampling unit
of the subsequent stage.
Example of multi stage sampling
 The management of a newly opened club solicits
membership. During the first rounds, all corporates were
sent details so that those who are interested may enroll.
Having enrolled, the second round concentrates on how
many are interested to enroll for entertainment activities
that club offers such as billiards, indoor sports, swimming
and gym etc. after obtaining the information, we might
stratify the interested respondents. This will also inform
about the reaction of new members.
Advantages and limitations of multistage sampling
 Advantages:
 It results in concentration of fieldwork in small areas.
 Savings in cost, time labor and money.
 Limitations:
 Procedure of estimating sampling error
 Cost advantage is complicated.
Sub sampling
 Sub sampling is a part of multi stage sampling process.
 In multistage sampling the sampling in second and
subsequent stage frames is called sub sampling
Non-Probability Sampling
Convenience or Accidental Sampling
• This is a non-probability sampling
• It means selecting a sample units in a just hit and miss
fashion, example: interviewing people whom we happen to
meet.
• This sampling also means selecting whatever sampling units
are conveniently available.
• Example: A teacher may select students in his class
• This is also known as accidental sampling because the
respondents whom the researcher selects are accidentally
included in the sample.
Advantages and Limitations
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Advantages:
Cheapest and simplest method
It does not require list of population
It does not require any statistical expertise
Limitations:
Highly biased
Least reliable sampling method
The findings cannot be generalized
Purposive or Deliberate Sampling
 This is also known as judgment sampling
 The investigator uses his discretion in selecting sample
observations from the universe.
 This method involves selection of cases which we judge as
the most appropriate ones for the given study.
 The investigator chooses the sample that may be true
representative of the universe.
Purposive Sampling-Contd..
 Example: Test market cities for the launch of an new
product is being selected on the basis of judgment
sampling, because these cities are viewed as typical cities
matching with certain demographic characteristics.
 Example: A researcher may deliberately choose industrial
undertakings in which quality circles are believed to be
functioning successfully and undertakings in which quality
circles are believed to be total failure.
Advantages and limitations-Purposive Sampling
 Advantages: It is less costly and more convenient.
 It guarantees inclusion of relevant elements in the sample.
 Limitations: It does not ensure
Shopping mall intercept sampling
 This is a non probability sampling method. In this method
the respondents are recruited for individual interviews at
fixed locations in shopping malls.
 This type of study would include several malls each serving
socio economic population.
Merits and Demerits-Shopping Mall Intercept
 Merits
 It has relatively small universe
 It is expected to give quick results.
 The level of accuracy can vary from the prescribed norms.
 Demerits:
 It allows bias
 Subjectivity of the enumerator.
Sequential sampling
 This is a method in which the sample is formed on the
basis of series of successive decisions.
 They aim at answering the research question on the basis
of accumulated evidence.
Example-Sequential Sampling
 Assume that a product needs to be evaluated.
 A small probability sample is taken from among the
current users. Suppose it is found that the average annual
usage is between 200 and 300 units. It is known that the
product is economically viable only if the average
consumption is 400 units. This Information is sufficient to
take a decision to drop the product.
 On the other hand if the initial sample shows a
consumption level of 450 to 600 units additional samples
are needed for further studies.
Quota Sampling
 Quota sampling is quite frequently used in marketing
research. It involves the fixation of certain quotas which are
to be fulfilled by the interviewers.
 Suppose 2,000 students are appearing for competitive
examination. We need to select 1% of them based on quota
sampling.
 The classification of quota may be as follows.
Classification of samples
category
General merit
quota
1000
sports
600
NRI
100
SC/ST
300
Total
2000
Steps in quota sampling
 The population is divided in to segments on the basis of
certain characteristics. Here the segments are termed as
cells.
 A quota is selected from each cells.
Limitations-Quota Sampling
 It may not be possible to a representative sample within
quota.
 Because of too much liberty to the interviewers the quality
of work suffers.
Panel samples
 Panels are frequently used in marketing research.
 Example: suppose that one is interested in knowing the
change in the consumption patterns of household. A
sample of households are drawn.
 These households are contacted to gather information on
the pattern of consumption.
 Subsequently may be after a period of six months the same
households are approached once again and the necessary
information on their consumption is collected.
Errors in sampling
 Sampling error
 Non sampling error
 Sampling frame error
 Non response error
 Data error
Sampling error
The only way to guarantee the minimization of sampling
error is choose the appropriate sample size.
As the sample keeps increasing the sampling error decreases.
Sampling error is the gap between sample mean and the
population mean.
Non sampling errors
 non sampling errors occurs in some systematic way which
is difficult to estimate.
 A sampling frame error occur when list of population is not
sufficient.
Other sampling errors
 Non response errors occurs because the planned sample
and the final sample vary significantly.
 Data errors occurs during data collection analysis of data or
interpretation.
 Respondents sometimes give distorted answers
unintentionally for questions which are difficult, or the
question is exceptionally long and the respondent may not
have answer.
 Data errors also occurs because of the physical and social
characteristics of the interviewer and the respondent.
Sample size Determination-Symbols
 Sampling Distribution: The distribution of the values of a
sample statistic computed for each possible sample that
could be drawn from the target population under a
specified sampling plan.
 Statistical Inference: The process of generalizing the
sample results to the population results.
 Normal Distribution: A basis for classical statistical
inference that is bell shaped and symmetrical in
appearance. It measures the central tendency are all
identical.
 Standard Error: The Standard deviation of the sampling
distribution of the mean or proportion.
Statistical Approach to determining Sample Size
 The Confidence Interval Approach
 Sample size determination: Means
 Sample Size Determination: Proportions
 Adjusting the statistically determined Sample Size.
Module 3-Field Procedures
 In the data gathering, stage there are 2 primary objectives.
 Maximizing the relevant information that is elicited from
the people providing it. (and who confirm to the sampling
specification).
 Minimizing errors which are of numerous varieties and
occurs easily.
Constraints in Field work
 The researcher must operate under some serious
constraints such as:
 Time
 Money
 Environment
Field procedures for Data Collection Methods
 Observation: observers act in 2 quite different capacities . One is that
of obtaining information that is already recorded or deals with objects
that are fixed in nature over some period (that is nonbehavioural).
Some of the more prevalent records such as financial statements,
economic data, and content analysis of competing advertising. Another
form, physical condition analysis, involves the field personnel
obtaining sales data on concerned brands through a store audits of the
incoming merchandise records and counts of inventory on hand. Facts
on prices, displays, and shelf facings might also be noted in the study
to determine competitive conditions. Observation plans can specify the
details of who, what when, where, and how to observe an object or
individual.
Field procedures for Data Collection Methods-Contd..
 The other capacity of observers is that of perceiving and recording people’s
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behavior. This form of observation can be classified into 4 major categories:
Nonverbal behavior, which includes body movement, motor expressions,
and glances.
Linguistic behavior, which involves the study of presentation content, or
what, how, and how much information is conveyed in a particular situation.
Extra linguistic behavioral dimensions including vocal (Pitch, loudness,)
temporal (rate of speaking, duration and rhythm), interaction (Tendencies
to interrupt, dominate or inhibit), verbal stylistic (Vocabulary,
pronunciation, and dialect).
Spatial relationship: which involves how one relates physically to others ,
such as maintaining appropriate distances between oneself and others.
Observation Plans
 It specifies details of :
 Who? The researcher must give the observer the specifications that would qualify a
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subject to be observed.
What? The basic unit of observation must be defined so the observer will know
what to record. For example, should the observer record an expressed thought, a
physical movement, a transaction, a facial expression, a particular behavior or what.
When? If time is an important factor to the study, then the observer must be told
when to observe. He or she can be directed to observe on a particular day or during a
particular week. Or the observer will be instructed to observe during a particular
period of time, say every 30 minutes of each hour.
Where? The observer must be told where to observe. Field observation tends to be
in the natural settings, but the observer must be directed to appropriate store,
street, a particular asile within a store and so forth.
How? The observer must be instructed on how they are to observe the subject. That
is, whether the presence of the observer should be made known to the subject. On
the other hand, some situations may require the observers to be hidden
Personal Interviewing
 In this method, of Personal Interface between Interviewers
and respondents, there tends to be the greatest opportunity
for gathering abundant information. It also offers the widest
range of interviewing techniques. On the other hand it is the
most expensive and time consuming method and is sometimes
the most fraught with error potentialities.
Tasks in Personal Interviewing
 It Involves 5 extensive tasks, which is summarized as below:
 Fulfill the sampling plan by covering the designated areas or
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locations and reaching the designated persons.
Administer the questionnaire in strict accordance with
instructions.
Record the responses precisely as given, in terms of the
measurements that are called for by instructions.
Return the information to the central point of editing and data
processing by the stipulated time.
Complete the field work within budgeted cost.
Here the first 4 tasks fall entirely on the interviewer, and the
fifth needs his or her cooperation.
Personal Interviewing- Contd..
 Interviewing essentially is an interpersonal process in which one person
(i.e. the Interviewer) endeavors to elicit data or attitudinal responses from
another person (the respondent). After establishing sufficient rapport, or
level of understanding with the respondent, the interviewer offers a
stimulus-usually a question- to obtain a response that provides the
needed data. Upon receiving the response, the interviewer must interpret
it in a number of ways.
 Surveys differ widely in their demands on the interviewer. When the
interviewer can obtain the needed information with complete structuring
(employing only standardized questions), there is a little reliance on the
interviewer’s ability to the direct the communications. In many instances,
where probing or formulation of appropriate question on the spot is
needed to obtain full or appropriate information, however greater reliance
is placed on the interviewer’s intelligence, dependability, and grasp of the
study’s objectives.
Personal Interviewing- Contd..
 Business and professional interviews on matters relative to a trade,
profession, or industry (for example, surveying doctors on medical
matters), may place the interviewer in more difficult role.
Sometimes they involve technical terms or jargons and considerable
understanding of the interviewee’s field or profession. They may be
more demanding also in that the most valuable business and
professional surveys are unstructured, so that the formulation of
questions devolves on the interviewer.
 The group or focus interview is one in which six or eight persons are
going to be interviewed together and in a free wheeling discussions
rather than answering a structured questionnaire. In this situation,
one clearly faces quite different interviewing procedures than under
the contrasting situation of interviewing a person individually.
Personal Interviewing- Contd..
 The unstructured characteristics of group interviewing makes
it similar to informal or depth interviewing of a single person,
a somewhat clinical psychological method, that is not included
in our coverage techniques. This means, the interviewer is not
limited to certain concretely stated questions, but rather has
latitude to compose and phrase questions as the interview
proceeds.
Telephone Interviewing
 In this medium of communication there is only a vocal
interface between interviewer and the respondent. The tasks
of telephone interviewing are somewhat modified from those
that we described for personal interviewing , so our list is as
follows:
 Call the telephone numbers listed in the sampling plan. Ask for
the person designated in the plan.
 When the person is reached, administer the questionnaire in
strict accordance with the instructions.
 Turn over the completed questionnaire to the personnel who
will edit it and prepare it for data processing.
Telephone Interviewing-Contd..
 The telephone interviewer does not have the effort of legwork to
find respondents and can easily dial callbacks in attempting to reach
those not at home.
 Telephone usage in surveys has been rising for at least a decade and
is now one of the most used techniques.
 Telephone surveys now tend to be made from a central point, often
the national office of the survey agency.
 Interviewing methods by telephone do not differ greatly from those
that are appropriate face to face. Rapport with the person
interviewed must be developed unseen, with everything resting on
the friendliness of the voice and what is said. The interviewer must
be well prepared to keep the interview moving steadily, because it is
much easier to lose a respondent over the phone than when in his
or her prescence.
Techniques of Telephone Interviewing
 Random Digit dialing: It enables the researcher to access all
working telephones regardless of whether their numbers are
published in directories. Several methods have been
developed, that include choosing phone numbers by using
random digit dialing, with or without the computer.
 Computer assisted telephone interviewing: The telephone
interviewer sits at a table in front of a CRT console, which has
a television like screen that displays questions, answers, and
directions for conducting an Interview.
Mail Surveys
 In this medium of data collection there is no field worker and no personal
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interface of course for communication. Instead, the respondent is reached by a
postal carrier, an impersonal contact. The absence of an intermediary, the
interviewer does make the communication direct and simpler, but the field
work of mail surveys is equally challenging. All the functions are shifted to
persons in some central office locations, whose tasks are of these types:
Compile or purchase address lists of the desired kinds of respondents who are
located within the sample areas.
Address envelopes, stuff them with the questionnaire and other materials and
mail them.
To those not responding by selected cutoff date, mail follow-up and
questionnaires. (when those who responded cannot be identified, follow-up
mailings should be sent to the entire original mailing list).
Edit the questionnaires, returned and prepare them for data processing,.
Mail Surveys-Contd..
The problems peculiarly associated with mail surveys stem largely from supervising
the various tasks namely, overseeing the assistants who perform and record these
routine activities. One aspect of researcher’s supervision entails his or her close
attention to the assistants who are preparing the survey materials for mailings. The
researcher must make sure that each envelope contains the proper number of
items (questionnaires, covering letter, incentive, return envelope etc.
The other aspect of researcher’s supervision concerns detailed record keeping. Once
the questionnaires are mailed (Usually should be mailed at one time), the
researcher should keep track of the response trends. Also, the researcher should
request a dated receipt from the post office indicating the number of envelopes
mailed as a evidence of original sample size.
Success in the mail surveys depends on 2 aspects:
The questionnaire and its covering messages and instructions.
The researcher’s supervision.
Error Sources in Field Work
 The twin objectives of field work are to:
 Maximize the flow of pertinent , accurate data.
 Minimize the errors committed by the Interviewers.
Sources of Errors
 Respondent Selection error
 Non-response errors
 Communication errors
Respondent Selection Error
 This kind of errors may be made in selecting sample members:
 Obtaining information in the wrong place.
 Obtaining information from the wrong person.
 Omitting information from persons who were supposed to be
interviewed in the sample design.
 Among the three media of communication (personal, mail,
telephone), the likelihood of these kinds of errors varies.
Non-response Errors
 Non-response are the most common field sampling errors.
These results primarily from:
 Failure to reach the intended respondents because they are
available or because there has not been an effort to reach
them or
 Persons reached do not provide the requested information.
 The first problem is the worse, due to not at homes.
Communication errors
In the interpersonal process of question and answer, numerous
possible errors, can be made by the interviewer or person
whom he or she is interrogating. Trouble may first arise in the
effort to obtain proper rapport with the interviewee.
Interviewing errors may also stem from failure to follow
instructions in administering a questionnaire. The interviewee
may not receive a proper explanation of the survey’s natureor may receive one in a manner that would bias responses.
Another type of interviewing error is categorized as “omissions”.
Finally erroneous responses form another interviewing problem.
..
Communication errors- Contd
 The other communication errors may be such as:
 Recording errors
 Falsification
Managing the field work
 There are 6 Phases in Managing field work:
 Pretesting
 Simplifying procedures.
 Interviewer recruitment and selection.
 Instructing the field workers.
 Supervision
 control
Phases of Field work
 Pretesting: Pretesting of every important project should be a
standard procedure. Minor and repetitive projects may be
conducted without pretests or with a quick testing of a
questionnaire on some convenient subjects. An adequate
pretest is much more than that, for it applies the complete
methods of data collection to a sample of persons similar to
those specified for the full study.

Pretest may reveal to those directing the gathering of
primary data, various planning errors that otherwise would
have gone unnoticed. Errors frequently result from what the
interviewer is requested to do rather than from mistakes on
the part of interviewer or the person interviewed.
Phases of field work- Contd..
 Simplifying Procedures
 There are a number of efficiencies that may be utilized to simplify field work tasks. Some
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illustrations are given below, which does apply to all types of survey.
The interviewer may be equipped with cards or pages to be handed over to
interviewees, to clarify or illustrate desired responses or to introduce the interviewer
and thereby help to establish rapport. Diagrams of rating scales, pictures, statements in
multiple choice questions, and desired categories of response are other examples of
such handouts.
The self-administered interview may substitute for having the interviewer ask the
questions. The interviewee may complete his or her answers while the interviewer
waits, or the latter may call back to retrieve the answers at some specified time.
Tape recording of interviews obviously enhances the fidelity of recorded responses and
eases the interviewer’s role.
Previous appointment usually by telephone tends to increase the number of completed
interviews per personal visit.
Various ways of improving the design and legibility of questionnaires may be applied to
easing the work and enhancing accuracy.
Phases of field work-Contd..
 Interviewer Recruitment and selection
 The data collection process in which interviewers are entrusted to gather
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
the data is a crucial stage in the research process. The research project will
be no better than the data gathered in the field by the interviewers.
Interviewer error is of significant concern. Some of the characteristics
taken into consideration include the following:
Education: Interviewers must have reasonably good reading and writing
skills.
Gender: In most cases women are recruited for interviewing positions.
Voice quality: The voice of the interviewer must be such that it is free of
any heavy accents, harshness or features that could be irritating.
Experience: An advantage of hiring experienced interviewers is that they
are likely to do a better job at the following instructions, obtaining
respondent co-operation, being able to record accurately, and guiding
respondents through the interview in a smooth and flowing manner.
Phases of field work-Contd..
 Instructing field workers: Instructions should be stated in clear and distinct terms, of course
and should cover these topics :
 What the survey is about
 When the survey is to start and when it is to be finished.
 How many persons are to be interviewed, where and how to select them, and what to do
about persons not at home.
 How to introduce oneself and initiate the interview.
 How each question should be asked and which ones contingent on the answers to other
questions.
 Methods of probing, encouraging responses and aiding memory.
 If any items are to be observed, what is to be noted.
 How each questionnaire is to be studied and corrected before returning the form.
 What to do with completed questionnaires.
 When and how the interviewer will be paid for his/her work.
 The exact basis on which the quality of work will be appraised.
Phases of field work-Contd..
 There are four basic method s to teach the interviewer:
 Written materials are used.
 Lectures and demonstrations
 Role plays
 Field practice
Phases of field work- Contd..
 Supervision
 Interviewers should be under a field supervisor whose duties
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would include:
Training, assisting and overseeing
Mapping, and prelisting addresses for the specific sample
selection in the field.
Hiring local interviewing help.
Editing the questionnaires turned in before forwarding them
to the central office.
Phases of field work-Contd..
 Supervision
 The key to good supervision is acquiring the needed

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
information to evaluate an interviewers performance. The
interviewer can be evaluated on several factors such as:
Costs
Response rates
Quality of data
Quality of interviewing
Control
 Good control hinges on having realistically anticipated and
planned a survey, with the plan written in detail so that its
execution can be followed closely. Two other steps that are
instrumental in achieving control are scheduling and
validating.
Field-work Data Collection Process
 Selection of field work
 Training of field workers
 Supervision of field workers
 Validation of field work
 Evaluation of field work.
Field work- Process
 Selection of Field workers: here the researcher should
 Develop job specifications for the project, taking into account
the mode of collecting data.
 Decide what characteristics the field workers should have.
 Recruit appropriate individuals.
 Interviewers background, characteristics, opinions,
perceptions, expectations, and attitudes can affect the
responses they elicit.
Field work Process- Contd..
 Training the field workers: it should cover:
 Making the initial contact
 Asking the questions
 Probing: helps the respondent to focus on specific content of the interview.
Some of the techniques of probing are repeating the questions, repeating
the respondents reply, using a pause or silent probe, boosting or reassuring
the respondent, eliciting clarification, using objective questions.
 Recording the answers
 Terminating the interview.
Field work Process- Contd..
 Supervision of field workers: Involves
 Quality control and editing
 Sampling control: attempts to ensure that the interviewers
are strictly following the sampling plan rather than selecting
the sample based on convenience or accessibility.
 Control of cheating.
Field work process-Contd..
 Validation: of field work means verifying that the field workers
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are submitting authentic interviews.
Evaluation of field workers: is based on the following creteria:
Cost and time
Response rates
Quality of interviewing
Quality of data.
Tabulation
 Tabulation refers to counting the number of cases that fall in
to various categories.
 The results are summarized in the form of statistical tables.
 The raw data is divided in to groups and sub-groups.
 The counting and placing of data in a particular group and subgroup are done.
Tabulation-contd..
 The tabulation involves:
 Sorting and counting
 Summarizing of data
 Tabulation may be of 2 types:
 Simple tabulation
 Cross tabulation
Types of tabulation
 In simple tabulation a single variable is counted.
 Cross tabulation involves 2 or more variables which are
treated simultaneously.
 Tabulation can be done entirely by hand or by machine, or
by both hand and machine.
Tabulation- Contd..
 The form in which tabulation is to be done is decided by taking
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in to account:
The purpose of study
The use of statistical tools
E.g. mean, mode, standard deviation etc.
Improper tabulation may create difficulties in the use of these
tools.
Sorting and counting data
 Sorting by manual method is as follows:
 Sorting of data
 Income
 1000
 1500
 2000
 2500
tally marks
IIII/
IIII/ III
IIII/ IIII/ II
IIII/ IIII/ IIII/ I
frequencies
5
8
12
16
Tabulation-Contd..
 The tabulation may include table number, title, head note, sub
caption, sub-entries, body of the table, footnote and the
source.
 The following example explains the component of a table.
Format of a blank table
 TITLE- number of children per family
 Head Note- Unit of Measurement
Sub heading
Caption
Body
Foot note
Total
Tabulation-Contd..
 The table must have a clear and brief title.
 The head note, usually the measurement unit, is placed at the
top of the table in the right hand corner in a bracket.
 Stub indicates the row title or the row headings and is placed
in the left-hand column.
 Caption indicates what each column is meant for.
Kinds of Tabulation
 1) Simple or one-way tabulation:
 The multiple choice question which allows only one answer
may use one tabulation or univariate. The questions are
predetermined and consists of counting the number of
responses falling into particular category and calculate the
percentage.
Types of univariate tabulation
 Question with only one response
 Multiple responses to question
 Question with one response: if the question has only one
answer the tabulation may be of the following type:
Table 1
study of no of children in a family
No of children
o
1
2
3
4
More than 4
family
10
30
70
60
20
10
200
percentage
5
15
35
30
10
5
100
Question with multiple response
 Sometimes respondents may give more than one answer to a
given question.
 In this case, there will be an overlap, and responses when
tabulated, need not add to 100 percent.
Table 2
choice of an automobile
Parameter
No of respondents
Engine
10
Body
15
Mileage
15
Interior
06
Colour
18
Maintenance frequency
16
Inconvenience
20
E.g.-Contd..
 There is duplication because respondents may be dissatisfied
with the mileage given by the vehicle and may dislike interior
of the car.
 Here there are more than one parameters to dislike the car by
the owner.
Tabulation of cause of inconvenience felt by car owners
 It can be classified as follows:
 Cramped legroom
 Rear seat problem
 Difficulty in raising the window
 Difficulty in locking the door.
 Now the tabulation of each of the specific factors would help
to identify the real reasons for dislike
Cross tabulation or 2 way tabulation
 This is known as bivariate tabulation.
 The data includes 2 or more variables.
 Cross tabulation is very commonly used in market research.
 The usefulness of cross tabulation is indicated with the
example which is as follows:
Table 3
use of health drink
Income
per month
0
1
2
3
4
5
More
than 5
No of
families
<1000
5
0
8
9
11
15
25
73
10012000
10
5
8
10
13
18
27
91
20013000
20
10
12
14
20
22
32
130
30014000
12
3
6
7
13
20
30
91
40015000
6
2
6
5
10
15
20
64
>5000
6
1
4
5
7
10
18
51
59
21
44
50
74
100
152
500
E.g.-Contd..
 The above table shows that consumption of a health drink not
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only depends on income but also on the number of children
per family.
Health drinks are also popular among the family with no
children.
This shows that even adults consume this drink.
It is obvious from the table that 59 out of 500 families
consume health drinks even though they have no children.
The table also shows that families in the income group of
2001-3000 consume health drink the most.
Module-4- Data Analysis
Multivariate analysis
 This can be studied under:
 Discriminant analysis
 Factor analysis
 Cluster analysis
 Conjoint analysis
 Multidimensional scaling
Discriminant Analysis
 In this analysis 2 or more groups are compared. In the final
analysis, we need to find out whether the groups differ one
from another.
Example of discriminant analysis
 Where discriminant analysis is used:
 Those who buy our brand and those who buy competitors
brand.
 Good salesman and poor salesman, medium salesman.
 Those who go to food world to buy and those who buy in a
kirana shop.
 Heavy user, medium user and light user of the product.
Equn for discriminant analysis
 Z= b1x1+b2x2+b3x3………
 Z= Discriminant score
 B1=Discriminant weight for variable 1
 B2= Discriminant weight for variable 2
 B3= Discriminant weight for variable 3
 X=Independent variable
Application of discriminant analysis
 A company manufacturing FMCG products introduces a sales
contest among its marketing executives to find out “How many
distributors can be roped in to handle the company’s product”.
 Assume that this contest runs for 3 months. Each marketing
executive is given a target regarding number of new
distributors and they can generate during the period.
 This target is fixed and based on the past sales achieved by
them about which, the data is available in the company.
Application of discriminant analysis-Contd..
 It is also announced that the marketing executives who add 15
or more distributors will be given a maruti omni-van as prize.
 Those who generate between 5 and 10 distributor will be
given a 2 wheeler as prize.
 Those who generate less than 5 distributor will get nothing.
 Now assume that 5 marketing executives won a maruti van
and 4 won a 2 wheeler.
Application of discriminant analysis-contd..
 The company wants to find out, which activities of the
marketing executive made the difference in terms of winning a
prize and not winning the prize.
 One can proceed in a number of ways.
 The company could compare those who won maruti van
against others.
 Alternatively the company might compare those who won,
one of the 2 prizes, against those who won nothing.
Application- contd..
 Discriminant analysis will highlight the difference in activities
performed by each group members to get the prize. The
activity might include:
 More number of calls made to the distributors.
 More personal visits to the distributors with advance
appointments.
 Use of better convincing skills.
Conducting Discriminant Analysis
 The steps involved in conducting Discriminant
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Analysis is as follows:
Formulate the problem
Estimate the discriminant function coefficients.
Interpret the results
Assess the validity of discriminant analysis
Factor analysis
 The main purpose of factor analysis is to group large set of
variable factors into fewer factors.
 Each factor will account for one or more component.
 Each factor a combination of many variables.
Factor analysis model
 Mathematically, factor analysis is
somewhat similar to multiple
regression analysis, in that each
variable is expressed as a linear
combination of underlying factors.
Factor Analysis Model- Contd..
 If the variables are standardized, the factor model may be
represented as:
 Xi=Ai1F1+Ai2F2+Ai3F3+……..+AimFm+ViUi
Where
 Xi= ith Standardized variable
 Aij= standardized multiple regression coefficient of variable i
on common factor j.
 F=Common Factor
 Vi= standardized regression coefficient of variable i on unique
factor i.
 Ui= the unique factor for variable i.
 M= number of common factors.
Statistics associated with factor analysis
 Bartlett’s test of sphericity: is a test of statistics used to
examine the hypothesis that the variables are uncorrelated in
the population. In other words, the population correlation
matrix is an identity matrix.
 Correlation matrix: A correlation matrix is a lower triangle
matrix showing the simple correlation, r between all the
possible pairs of variables included in the analysis.
 Communality: is the amount of variance, a variable shares
with all the other variables being considered. This is also the
proportion of variance explained by the common factors.
 Eigen value: represents the total variance explained by each
factor.
Statistics associated with factor analysis- Contd..
 Factor loadings: are simple correlations between the variables
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and the factors.
Factor loading plot: A factor loading Plot is the plot of original
variables using the factor loadings as coordinates.
Factor matrix: A factor matrix contains the factor loadings of
all the variables on the factors extracted.
Factor scores: Factor Scores are composite scores estimated
for each respondent on the derived statistics.
KMO: Kaiser Meyer Olkin measure of sampling adequacy: is an
index used to examine the appropriateness of factor analysis.
High values between 0.5 and 1.0 indicate factor analysis is
appropriate. Values below 0.5 imply that factor analysis may
not be appropriate.
Statistics associated with factor analysis- Contd..
 Percentage of variance: This is the percentage of the
total variance attributed to each factor.
 Residuals: Residuals are the differences between the
observed correlations, as given in the input correlation
matrix, and the reproduced correlations, as estimated
from the factor matrix.
 Scree plot: A scree plot is a plot of the eigenvalues against
the number of factors in order of extraction.
Conducting factor analysis
 The steps involved in conducting factor analysis is as
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follows:
Formulate the problem
Construct of correlation matrix.
Determine the method of factor analysis.
Determine the number of factors.
Rotate the factors.
Interpret the factors: calculate the factor scores, select
the surrogate variables.
Determine the model fit.
Conducting factor analysis- Contd..
 Principal component analysis: An approach to factor
analysis that considers the total variance in the data.
 Common factor analysis: An approach to factor analysis
that estimates the factors based on the common
variance.

Conducting factor analysis- Contd..
 Determine the number of factors:
 The number of factors can be determined using the following
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approaches:
A priori determination.
Determination based on Eigen values.
Determination based on scree plots.
Determination based on percentage of variance.
Determination based on split-half reliability: The sample is
split in half and factor analysis is performed on each half.
Determination based on significance tests.
Conducting factor analysis- Contd..
 The rotation of factor can be done based on;
 Orthogonal Rotation: Rotation of factors in which the axes
are maintained at right angles.
 Variance procedure: It is a commonly used procedure. An
orthogonal method of factor rotation that minimizes the
number of variables with high loadings on a factor,
thereby enhancing the interpretability of the factors.
 Oblique rotation: Rotation of factors, when the axes are
not maintained at right angles.
Factor analysis –contd..
 There are 2 most commonly employed factors analysis
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procedures. They are:
Principle component analysis
Common factor analysis
When the objective is to summarize information from a large
set of variables in to a few factors, principle component factor
analysis is used.
On the other hand if the researcher wants to analyze the
components of the main factor, common factor analysis is
used.
Example of common factor analysis
 Example: inconvenience inside a car. The components may be:
 Leg room
 Seat arrangement
 Entering the rare seat
 Inadequate dickey space
 Door locking mechanism
Example of principle component factor analysis
 Example: customer feedback about a 2 wheeler manufactured
by a company.
 The MR Manager prepares a questionnaire to study the
customer feedback.
 The researcher has identified 6 variables or factors for this
purpose.
e.g for principle factor analysis- contd..
 The factors are as follows:
 Fuel efficiency (A)
 Durability (B)
 Comfort (C)
 Spare parts availability (D )
 Breakdown frequency (E)
 Price (F)
Factor analysis- contd..
 The questionnaire may be administered to 5000 respondents.
The opinion of the customer is gathered. Let us allot points 1
to 10 for the variables factors A to E. 1 is the lowest and 5 is
the highest.
 Let us assume that the application of factor analysis has led to
grouping the variables as follows.
Factor analysis- contd..
 A, B, D,E into factor 1
 F into factor-2
 C into factor-3
 Factor-1 can be termed as technical factors
 Factor-2 can be termed as Price factor.
 Factor-3 can be termed as Personal factor.
Applications of factor Analysis
 It is used for market segmentation.
 Product research: can be employed to determine the
brand attributes that influence the consumers choice.
 Advertising studies: media consumption habits of target
audience.
 Pricing studies: to identify characteristics of price
sensitive consumers.
Cluster Analysis
 Cluster analysis is used to:
 To classify persons or objects into small number of clusters or
groups.
 To identify specific customer segment for the company’s
brand.
 Cluster analysis is a technique used for classifying objects into
groups.
 This can be used to sort data( a number of people, companies,
cities, brands or any other objects) into homogenous groups
based on their characteristics.
Applications of Cluster Analysis
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Customer segmentation
Estimation of segment sizes
Industries where this technique is useful includes
Automobiles
Retail stores
Insurance
B to B
Durables and packaged goods
VALS (consumer Behavior)
Statistics associated with cluster Analysis
 Agglomeration schedule: An agglomeration schedule gives information on
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the objects or cases being combined at each stage of the hierarchical
clustering process.
Cluster centroid: Is the mean values of the variables for all the cases or
objects in a particular cluster.
Cluster membership: Indicates the cluster to which each case or object
belongs.
Dendrogram: A Dendrogram or tree graph is a graphical dev ice for
displaying clustering results.
Distances between cluster centers: These distances indicate how separated
the individual pairs of clusters are.
Icicle diagram: An icicle diagram is a graphical display of clustering results,
so called because it resembles a row of icicles hanging from the eaves of
the house.
Similarity/distance coefficient matrix: Is a lower triangle matrix containing
pair wise distances between objects or cases.
Conducting Cluster Analysis
 Formulate the problem
 Select a distance measure
 Select a clustering procedure
 Decide on the number of clusters
 Interpret and profile clusters.
 Assess the validity of clustering.
Select a clustering Procedure
 Hierarchical Clustering: A Clustering procedure characterized by the
development of hierarchy or tree like structure.
 Agglomerative clustering: hierarchical clustering procedure where each object
starts out in a separate cluster.
 Divisive clustering: Hierarchical clustering procedure where all the objects
start out in one giant cluster. Clusters are formed by dividing this cluster into
smaller and smaller clusters.
 Linkage methods: Agglomerative methods of hierarchical clustering that
cluster objects are based on computation of distances between them.
 Single linkage: Linkage method that is based on minimum distance or the
nearest neighbor approach.
 Complete linkage: Linkage method that is based on maximum distance or the
farthest neighbor approach.
 Average linkage: A Linkage method based on the average distance between all
the pairs of objects, where one member of the pair is from each of the clusters.
Select a clustering Procedure- Contd..
 Variance methods: An agglomerative method of
hierarchical clustering in which clusters are generated to
minimize the within cluster variance.
 Ward’s procedure: variance method in which the squared
Euclidean distance to the cluster means is minimized.
 Centroid methods: A Variance method of hierarchical
clustering in which the distance between 2 clusters is the
distance between their centroids.
Select a clustering Procedure- Contd..
 Non-hierarchical clusters: A Procedure that first assigns or
determines a cluster center and then groups all objects within a
prespecified threshold value from the center.
 Sequential threshold method: A non-hierarchical clustering method
in which a cluster center is selected and all the objects within a
prespecified threshold value from the center are grouped together.
 Parallel threshold method: Non-hierarchical clustering method that
specifies several cluster centers at once. All objects that are within a
prespecified threshold value from the center are grouped together.
 Optimizing partitioning method: Non-hierarchical clustering method
that allows for later reassignment of objects to clusters to optimize
an overall criterion.
Cluster analysis is applicable
 An FMCG company wants to map the profile of its target
audience in terms of lifestyle, attitude, and perceptions.
 A consumer durable company wants to know the features and
services a consumer takes into account, when purchasing
through catalogues.
 A housing finance corporation wants to identify and cluster
the basic characteristics, lifestyles and mindset of persons who
would be availing housing loans.
 Clustering can be done based on parameters such as interest
rates, documentation, processing fee, number of installments
Process
 There are 2 ways in which cluster analysis is carried out:
 First, objects/respondents are segmented into a pre-
decided number of clusters. In this case a method called
non-hierarchical method can be used which partitions
data into the specified number of clusters.
 The second method is called the hierarchical method.
Interpretation of Results
 Ideally the variables should be measured on an interval or
ratio scale.
 This is because the clustering techniques use the distance
measure to find the closest objects to group into clusters.
 An example of its use can be clustering of towns similar to
each other which will help decide where to locate new
retail stores.
Interpretation of Results-Contd..
 If clusters of customers are found based on their
attitudes towards new products and interest in
different kinds of activities an estimate of the
segment size for each segment of the population can
be obtained by looking at the number of objects in
each cluster.
 Names can also be given to clusters to describe each
one.
 Marketing strategies for each segment are based on
segment characteristics.
Steps in Cluster Analysis
 Selection of the sample to be clustered
(buyers, products, employees)
 Definition on which the measurement to be
made. (e.g. Product attributes, buyer behavior,
characteristics)
 Clusters should be arranged in hierarchy.
 Cluster comparison and validation.
Steps in Cluster Analysis-Contd..
 Selection of the sample to be clustered
(buyers, products, employees)
 Definition on which the measurement to be
made. (e.g. product attributes, buyer
characteristics).
 Computing the similarities among the
entities.
 Arrange the clusters in hierarchy.
 Cluster comparison and validation.
Conjoint Analysis
 A technique that attempts to determine the relative
importance consumers attach salient attributes and
the utilities they attach to the level of attributes.
 Conjoint analysis is concerned with the measurement
of the joint effect of the 2 or more attributes that are
important from the consumers point of view.
Statistics associated with conjoint analysis
 Part worth functions: The part worth functions or utility functions
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describe the utility consumers attach to the levels of each attribute.
Relative importance weights: The relative important weights are
estimated and indicate which attributes are important in influencing
consumer choice.
Attribute levels: The attribute levels denote the values assumed by
the attributes.
Full profiles: Full profiles or complete profiles of brands are
constructed in terms of all the attributes by using the attribute
levels specified by the design.
Pair wise tables: In Pair wise tables, the respondents evaluate two
attributes at the same time until all the required pairs of attributes
have been evaluated.
Statistics associated with conjoint analysis-Contd..
 Cyclical designs: Cyclical designs are designs employed to
reduce the number of paired comparisons.
 Fractional factorial designs: Fractional factorial designs
are designs employed to reduce the number of stimulus
profiles to be evaluated in the full profile approach.
 Orthogonal arrays: Orthogonal arrays are a special class
of factorial designs that enable the efficient estimation of
all main effects.
 Internal validity: This involves correlations of the
predicted evaluations for the holdout or validation stimuli
with those obtained from the respondents.
Steps in Conducting Conjoint Analysis
 Formulate the Problem
 Construct the Stimuli.
 Decide on the form of Input data.
 Select a Conjoint analysis procedure.
 Interpret the results.
 Assess reliability and validity.
Conjoint Analysis Model
 Conjoint analysis model:
 The mathematical model expressing the fundamental
relationship between attributes and utility in conjoint
analysis.
Conjoint Analysis Model-Contd..
The model estimated may be represented by:
m ki
 U(X)= ∑ ∑ aij xij
i=1 j=1
Where
U(X)= overall utility of an alternative
aij= the part worth contribution or associated with the jth level.
(j, j= 1,2…..ki) of the ith attribute (i, i = 1,2……m)
Ki = number of levels of attribute i
m = number of attributes
Xij = 1 if the jth level of ith attribute is present
= 0 otherwise
Hybrid Conjoint Analysis
 A form of conjoint analysis that can simplify the data
collection task and estimate selected interactions as well
as all its main effects.
 It has been developed to serve 2 main purposes:
 Simplify data collection task by imposing less burden on
each respondent.
 Permit the estimation of selected interactions at the
subgroup level as well as all main effects at individual
level.
Conjoint Analysis-Contd..
 In a situation where the company would like to know the
most desirable attributes or their combination for a new
product or service, the use of conjoint analysis is not
appropriate.
Example for conjoint analysis
 An airline would like to know, which is the most desirable
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combination of attributes to a frequent traveller:
Punctuality
Airfare
Quality of food served on the flight
Hospitality and empathy shown
Conjoint analysis.. Contd
 Conjoint analysis is a multivariate technique
that captures the exact levels of utility that an
individual consumer places on various
attributes of the product offering. Conjoint
analysis enables direct comparison.
Example of conjoint analysis
 Designing an automobile loan or insurance
plan in the insurance industry.
 Designing a complex machine for business
customers.
Process of conjoint analysis
 Design attributes for the product are first identified.
 For a shirt manufacturer, these could be design such
as designer shirts Vs plain shirts, this price of Rs400
versus Rs.800. The outlets can have exclusive
distribution. All possible combinations of these
attributes level are then listed out.
 Each design combination will be ranked by customers
and used as input data for conjoint analysis. Then the
utility of the products relative to the price are
measured.
Process of conjoint analysis
 The output is a part-worth or utility for each level of
each attribute.
 For example the design may get a utility level of 5 and
plain as 7.5. Similarly, the exclusive distribution may
have a part utility of 2, and mass distribution, 5.8. We
then put together the part utilities and come up with
a total utility for any product combination we want to
offer and compare that with the maximum utility
combination for this customer segment.
Approach to conjoint analysis
 From a discussion with the client, identify the design
attributes to be studied and the levels at which they
can be offered. Then build a list of product concepts
on offer. These product concepts are then ranked by
customers. Once this data is available, use conjoint
analysis to derive the part utilities of each attribute
level.
 This is then used to predict the best product design
for the given customer segment. Use the SPSS
Conjoint procedure to analyse the data.
Uses of Conjoint Analysis
 The uses of Conjoint analysis is as follows:
 Determining the relative importance of attributes in the
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consumer choice process.
Estimating market share of brands that differ in attribute
levels.
Determining the composition of most preferred brand.
Segmenting the market based on similarity of preferences
for attribute levels.
Applications of conjoint analysis have been made in
consumer goods, industrial goods, financial and other
services.
MDS
 The most common and useful marketing application of
multidimensional scaling is product positioning or brand
positioning.
 Positioning is essentially concerned with mapping a consumers
mind and placing all the competing brands of a product
category in appropriate slots or positions on it. One obvious
way to do that is to ask customers what they think of
competing brands or say 6 attributes with a rating scale of 5 to
10 points. This would result in rating for all the brands on all
attributes which could be taken as 2 attributes at a time and
plotted on a graph.
MDS
 A class of procedures for representing perceptions and
preferences of respondents spatially by means of a virtual
display.
 Perceived or psychological relationship among stimuli are
represented as geometric relationships among points in a
multidimensional space.
Statistics and terms associated with MDS
 Similarity judgments: are ratings on all possible pairs of
brands or other stimuli in terms of their similarity using a
likert- type scale.
 Preference rankings: are rank ordering of the brands or
other stimuli from the most preferred to least preferred .
They are normally obtained from the respondents.
 Stress: This is lack of fit-measure; higher the values of
stress indicates poor fits
Statistics and terms associated with MDS- Contd..
 R-Square: R Square is a squared correlation Index that
indicates the proportion of variance of the optimally scaled
data that can be accounted for by the MDS Procedure. This is a
goodness of fit measure.
 Spatial map: Perceived relationship among brands or other
stimuli are represented as geometric relationship among
points in a Multi Dimensional space called spatial map.
 Coordinates: indicate the positioning of a brand or a stimulus
in a spatial map.
 unfolding: The representation of both brands and
respondents as points in the same space is referred to as
unfolding.
Conducting MDS
 Formulate the problem
 Obtain input data
 Select an MDS Procedure
 Decide on the number of dimensions.
 Label the dimensions and interpret the configuration.
 Assess reliability and validity.
Conducting MDS-Contd..
 Obtain Input Data:
 Perception Data: Direct Approaches: In Direct
Approaches to gathering perception data, the
respondent, the respondents are asked to judge how
similar or dissimilar the various brands or stimuli are,
using their own criteria. Respondents are often
required to rate all possible pairs of brands or stimuli
in terms of similarity on a likert scale. These data are
referred to as similarity judgements.
Example
 Similarity judgments on all the possible pairs of toothpaste
brands may be obtained in the following manner:
very
very
Dissimilar
similar
 Colgate vs. Crest
1
2 3 4 5 6 7
 Aqua fresh vs,crest 1
2 3 4 5 6 7
 Colgate vs aquafresh 1
2 3 4 5 6 7
The number of pairs to be evaluated is n(n-1)/2, where n is
the number of stimuli. Other procedures are also available.
Conducting MDS- Contd..
 Derived approach: In MDS attribute based approach to
collecting perception data requiring the respondents
to rate the stimuli on the identified attributes using
semantic differential or likert scale
 For example different brands of toothpaste may be
rated on attributes such as:
 Whitens ------------------------------------------Does not
teeth
whiten
teeth
Conducting MDS- Contd..
Direct Vs Derived Approach:
 Direct approaches have the advantage that the researcher
does not have to identify a set of salient attributes.
Respondents make similarity judgments using their own
criteria, as they would under normal circumstances. The
disadvantages are that the criteria are influenced by the
brands or stimuli being evaluated.
 If various brands of automobiles being evaluated are in the
same price range, then price will not emerge as an important
factor. It may be difficult to determine before analysis if and
how the individual respondents judgment should be
combined.
Conducting MDS- Contd..
 Direct Vs Derived Approach:
 The advantage of Derived or Attribute based approach is
that it is easy to identify respondents with homogenous
perceptions. The respondents can be clustered based on
the attribute ratings. It is also easier to label the
dimensions. A disadvantage is that the researcher must
identify all the salient attributes a difficult task. The
spatial map obtained depends on the attributes
identified.
Conducting MDS- Contd..
 Select an MDS Procedure
 Non-metric MDS- A type of MDS method that assumes
that the input data are ordinal.
 Metric MDS- A MDS method that assumes that the input
data are metric.
Conducting MDS- Contd..
 Decide on Number of Dimensions: The following guidelines are
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suggested for determining the number of dimensions:
A priori knowledge: theory or past research may suggest a particular
number of dimensions.
Interpretability of the spatial map: Generally it is difficult to
interpret configurations or maps derived in more than 3 dimensions.
Elbow criterion: A plot of stress versus dimensionality should be
examined. The point in this plot usually form a convex pattern. The
point at which a n elbow or a sharp bend occurs indicates
appropriate no of dimensions.
Ease of use: it is generally easier to work with 2 dimensional maps
or configurations than with those involving more dimensions.
Statistical approach: It is used for determining dimensionality.
Conducting MDS- Contd..
 Scaling Preference Data:
 Internal Preference Data: Takes into account both
brands stimuli and respondent points.
 External analysis of preference: vectors based on
preference data.
Example of MDS
 A product category of shampoos could be identified as
having 5 attributes important to consumers- price, lather,
fragrance, consistency, and favorable effects on hair. If
this were to be rated on a 7-point scale for say six leading
brands of shampoo A, B, C,D,E, and F, then we could pick
up any 2 attributes and plot the six brands on a map
according to consumer ratings.
Example of MDS- Contd..
 For example if we plotted rating on price Versus rating
on favorable effect on hair, we may find that all the 6
brands are positioned in different places based on
consumer ratings or perceptions. This is called
perceptual map of consumer perception about
competing brands in a product category.
Methods of MDS
 Attribute based approach
 Similarity/dissimilarity based approach
Recommended Usage
 Knowing particular attribute
 Number of dimensions as well as interpretation.
 Naming of attributes of the brands and their target
segment such as age, price, quality, or attempted
positioning through brand communication and so on.
Research report
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There are 2 types of report
Oral report
Written report
Oral report: This type of reporting is required, when the
researchers are asked to make an oral presentation. Making an
oral presentation is somewhat difficult compared to written
report. This is because the reporter has to interact directly
with the audience. Any faltering during an oral presentation
can leave a negative impression on the audience.
Nature of an oral presentation
 Opening
 Finding/Conclusion
 Recommendation
 Method of presentation.
Points to remember in oral presentation
 Language used must be simple and understandable.
 Time Management should be adhered.
 Use of charts, graphs etc, will enhance understanding by the
audience.
 Vital data such as figures, may be printed and circulated to the
audience, so that their ability to comprehend increases.
 The presenter should know his target audience well in
advance.
 The presenter should know the purpose of the report.
Guidelines for oral report
 Employ visual aids
 Avoid reading the report
 KYA- Know Your Audience
 Plan and deliver.
Types of written reports
 On the basis of time interval reports can be classified as:
 Daily, Weekly, Monthly, Quarterly, Yearly
 Types of Report:
 Short Report, long Report, Formal Report, Informal
Report, Government Report.
Preparation of written reports
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Preparation of research report:
The following is the format of research report:
Title Page
Page contents/Table of contents
Executive Summary
Introduction
Methodology
Data collection and Analysis
Conclusions
Suggestions and Recommendations
Bibliography.
Appendix
Explanation of contents of reports
 Executive summary: This includes a brief detail of what
the report consists of. It should be written in one or two
pages.
 Body: this section include:
Introduction: the introduction should clearly explain the
decision problem. Sometimes it consists of details about
the topic, company profile etc.
Contents of report- contd..
 Methodology: this includes the following:
 Statement of objectives
 Data collection method: whether primary, secondary data
or both.
 Questionnaire design, ie tools for data collection.
 Sample design: which includes sample type, sample size
etc.
Contents of report- contd..
 Analysis and interpretation: this should include analysis of
question in the questionnaire by using tables and graphs
and other statistical tools.
Contents of report- contd..
 Conclusions: this includes the conclusions drawn from the
study.
 Suggestions and recommendations: based on the conclusions,
suggestions and recommendations are made.
 Appendix: the purpose of appendix is to provide a place for
material which is not absolutely necessary in the body of the
report: such as questionnaire, broucher etc.
Bibliography
 If portions of the report is based on secondary data, use
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bibliography section to list the publications or sources
that you have consulted. It includes:
Title of the book
Name of the journal in case of article
Volume no
Page number
Edition
Writing the Report- Contd..
 Pre writing considerations:
 The outline :
Major Topic Heading
A Major subtopic heading
1. Sub topic
a. Minor subtopic
(1) Further details
(a) Even further details
I.
Writing the Report- Contd..
 Writing Considerations: Contd..
 The Bibliography
 Writing the Draft
 Readability
 Comprehensibility
 Tone
 Final proof.
Presenting the research report
 Carrying out professional approach
 Use short paragraphs
 Use headings and subheadings
 Use vertical listings of points.
 Incident part of the text that represents listings, long
quotations or examples.
Presenting the research report
 Presentation of statistics involves 4 ways:
 A text paragraph
 Semi tabular form
 Tables
 Graphics
 Pie charts
Presenting the research report
 Preparation
 Opening
 Findings and conclusions
 Recommendations.
 Delivery
Presenting the research report- Contd..
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Common Research Problems
Speaker problems
Vocal characteristics:
Should not speak too softly
Do not speak to rapidly
Vary volume tone quality
Do not use overworked pet phrase, uhs, etc.
Do not stare into space
Do not misuse visuals
Do not hitch or tug on clothing, scratch or fiddle with pocket.
Do not rock back and forth or twist from side to side, or lean
too much on the lectern.
Presenting the research report- Contd..
 Other problems
 Cost considerations
 Limitations on time
 Quality of research report
 Effectiveness of research.
Presenting the research report
 Audio-Visuals
Low tech:
 Chalk board and white boards
 Hand out materials
 Flip charts
 Overhead transparencies.
 Slides
High Tech
Computer drawn visuals
Computer animations
Writing the research Report- Contd..
 Other guidelines:
 Consider the audience
 Attitude 1: adopt fresh mind approach
 Kiss Approach (Keep it short and simple).
Oral and written report
Distinguish between oral and written report:
oral Report
 No rigid standard format
 Remembering all that is said is difficult if not impossible. This
is because the presenter cannot be interrupted frequently for
clarification.
 Tone, voice modulation, comprehensibility and several other
communication factors play an important role.
 Correcting mistakes if any is difficult.
 The audience has no control over the speed of presentation.
 The audience does not have the choice of picking and
choosing from the presentation.
Oral and written report
Distinguish between oral and written report:
Written Report
 Standard format can be adopted
 This can be read a number of times and clarification can be
sought whenever the reader chooses.
 Free from presentation problems.
 Mistakes if any, can be pinpointed and corrected.
 Not applicable
 The reader can pick and choose what he thinks is relevant to
him. For instance, the need for information is different for
technical and non technical persons.