Data collection copy

Dr. Perini PraveenaSri
Associate Professor
Atria Institute of Technology

Data can be defined as a systematic record of a particular
quantity. It is the different values of that quantity
represented together in a set.
It is a collection of facts and figures to be used for a specific
purpose such as a survey or analysis.
When arranged in an organized form, can be called
information. The source of data ( primary data, secondary
data) is also an important factor.

Data may be qualitative or quantitative.

Qualitative Data: They represent some characteristics or attributes. They
depict descriptions that may be observed but cannot be computed or
calculated.
For example, data on attributes such as intelligence, honesty, wisdom,
cleanliness, and creativity collected using the students of your class a
sample would be classified as qualitative. They are more exploratory than
conclusive in nature.
Quantitative Data: These can be measured and not simply observed.
They can be numerically represented and calculations can be performed on
them.
For example, data on the number of students playing different sports from
your class gives an estimate of how many of the total students play which
sport. This information is numerical and can be classified as quantitative.

The task of data collection begins after a research problem has been
defined and research design/ plan chalked out.
While deciding about the method of data collection to be used for
the study, the researcher should keep in mind two types of data viz.,
primary and secondary.
The primary data are those which are collected afresh and for the
first time, and thus happen to be original in character
Primary data is information collected through original or first-
hand research. They are collected by the investigator conducting
the research.

The secondary data, on the other hand,
-----are those which have already been collected by someone
else and which have already been passed through the statistical
process.
The researcher would have to decide
----which sort of data he would be using (thus collecting) for his
study and accordingly he will have to select one or the other
method of data collection.

The methods of collecting primary and secondary data differ
------since primary data are to be originally collected,
Example : Data collected by a student for his/her thesis or research
project.
while in case of secondary data the nature of data collection work is
merely that of compilation.
Example: Census data being used to analyze the impact of education on
career choice and earning.
The methods of collecting primary and secondary data differ :
----since primary data are to be originally collected,
-----while in case of secondary data the nature of data collection work is
merely that of compilation. (desk Research)

Let’s say you were researching trauma in burn survivors;
 You would typically begin your study by going through the literature on
the subject.
Data gleaned both from published papers and unpublished research notes
would be secondary data.
 Although it isn’t primary data, it could give you invaluable information
nonetheless. If you decided to go on to collect primary data, the secondary
data would give you what information you need to know where to begin.
If you took a trip to a trauma unit and interviewed burn survivors, the data
collected in that phase of your research would be primary data.
If one of your interviewees puts you in touch with a burn survivor
support group, and you are given access to a database of
information about the psychological state of a large group of
survivors in the years following the burn incident, that would also be
secondary, not primary, data.

Primary Data
These are the data that are collected for the first time by an investigator
for a specific purpose.
Primary data are ‘pure’ in the sense that no statistical operations have
been performed on them and they are original.
An example of primary data is the Census of India.
Secondary Data
They are the data that are sourced from someplace that has originally
collected it.
This means that this kind of data has already been collected by some
researchers or investigators in the past and is available either in
published or unpublished form.
This information is impure as statistical operations may have been
performed on them already.
An example is an information available on the Government of India,
Department of Finance’s website or in other repositories, books, journals, etc.

We collect primary data during the course of doing
experiments in an experimental research
but in case we do research of the descriptive type and
perform surveys, whether sample surveys or census
surveys,
-----then we can obtain primary data either through
observation or through direct communication with
respondents in one form or another or through personal
interviews.

Descriptive research is defined as a research method that
describes the characteristics of the population or phenomenon
that is being studied.
This methodology focuses more on the “what” of the research
subject rather than the “why” of the research subject.
In other words, descriptive research primarily focuses on
describing the nature of a demographic segment, without
focusing on “why” a certain phenomenon occurs.
In other words, it “describes” the subject of the research, without
covering “why” it happens.
It doesn’t emphasize on cause and effect relationship

For example, an apparel brand that wants to understand the
fashion purchasing trends among New York buyers will
conduct a demographic survey of this region,
gather population data and then conduct descriptive
research on this demographic segment.
The research will then uncover details on “what is the
purchasing pattern of New York buyers”, but not cover
any investigative details on “why” the patterns exits.
Because for the apparel brand trying to break into this
market, understanding the nature of their market is the
objective of the study.

1. OBSERVATION METHOD
2. CASE STUDY METHOD
3. SURVEY RESEARCH
1. Observational Method
It is the most effective method to conduct descriptive research and both
quantitative observation and qualitative observation are used in this research
method.
Quantitative observation is the objective collection of data which is primarily
focused on numbers and values – it suggests “associated to, of or depicted in
terms of a quantity”.
Results of quantitative observation are derived using statistical and
numerical analysis methods. It implies observation of any entity that
can be associated with a numeric value such as age, shape, weight,
volume, scale etc. For example, the researcher can track if current customers
will refer the brand by using a simple Net Promoter Score question.

Let's consider a scenario where a business which wants to
calculate Net Promoter Score for product X amongst its 50
consumers.
Now let's say after the responses are collected, 25 of the
respondents are Promoters, 15 are passive and 10 are
detractors.
So based on the Net Promoter Score formula:
Promoters = 50 - (15 + 10)/50* 100 = 50%
Passive = 50 - (25 + 10)/50 * 100 = 30%
Detractors = 50 - (25 + 15)/50 * 100 = 20%

Qualitative observation doesn’t involve measurements or numbers
but instead just monitoring characteristics.
In this case the researcher observes the respondents from a
distance. Since the respondents are in a comfortable environment,
the characteristics observed are natural and effective.
In descriptive research, the researcher can chose to be either a
complete observer, an observer as a participant, a
participant as an observer or a complete participant.
For example, in a supermarket, a researcher can from afar monitor
and track the selection and purchasing trends of the
customers. This offers a deeper insight into the purchasing
experience of the customer.

Case studies involve in-depth research and study of individuals or
groups. Case studies lead to a hypothesis and widen a further
scope of studying a phenomenon.
However, case studies should not be used to determine cause
and effect as they don’t have the capacity to make accurate
predictions because there could be a bias on the part of the
researcher.
The other reason why case studies are not an accurate way of
conducting descriptive research is because there could be an
a typical respondent in the research and describing them
leads to poor generalizations and move away from external
validity.

In survey research, respondents answer through surveys or
questionnaires, or polls. They are a popular market research tool
to collect feedback from respondents.
In order for a survey to gather good quality data, it should have
good survey questions, which should be a balanced mix of open-
ended questions and close ended-questions.
Closed-ended questions are those which can be answered by
a simple "yes" or "no," while open-ended questions are those
which require more thought and more than a simple one-word
answer.
The survey method can be conducting online or offline which is
makes it the go-to option for descriptive research where
the sample size is very large.

An experiment refers to an investigation in which a factor or variable under test is
isolated and its effect(s) measured.
In an experiment the investigator measures the effects of an experiment which he
conducts intentionally. It emphasizes the cause and effect relationship.

They can be enumerated or listed as follows :
(i) observation method,
(ii) Survey method
(iii) interview method,
(iv) through questionnaires,
(v) through schedules, and
(vi) other methods which includes
(a) warranty cards; (b) distributor audits; (c) pantry
audits; (d) consumer panels; (e) using mechanical
devices; (f) through projective techniques; (g) depth
interviews, and (h) content analysis.

The observation method is the most commonly used method specially in studies
relating to behavioral sciences.
Behavioral sciences explore the cognitive processes within organisms and
the behavioral interactions between organisms in the natural world..
In a way we all observe things around us, but this sort of observation is not scientific
observation.
Observation becomes a scientific tool and the method of data collection for the
researcher, when it serves a formulated research purpose, is systematically
planned and recorded and is subjected to checks and controls on validity and
reliability.
Observational data is a valuable form of research that can give researchers
information that goes beyond numbers and statistics. In general, observation
is a systematic way to collect data by observing people in natural
situations or settings.

Participant vs. Non-Participant Observation
Participant observation: the researcher is involved in the activity
Non-participant observation: the researcher is separate from the activity
Example
Participant observation is when a researcher is involved in the activity they are
observing. For example, the researcher is a participant in an Alcoholics Anonymous
group, and they are observing something about that group. In non-participant
observation, the researcher is separate from the activity (for example, an adult in the
back of the classroom observing students’ test-taking skills).
Participant observation offers the researcher more context and a greater
understanding of what’s being studied. However, participating in the activity can
change the behavior of those being observed.
By the same measure, non-participant observation allows researchers to use
tools like recorders or cameras to more accurately capture what is being
observed, but it may provide more limited insight into the dynamics and context of
what is being studied.

Both participant and non-participant observation can yield valuable or detrimental
observational data, depending on your study.
However, they are often most effective when used together to develop a more
complete picture of what’s being studied.
For example, a researcher who wants to study competitive chess players could first
watch chess tournaments from the stands (i.e. non-participant observation) to
get an overview of how the players interact. Then the researcher could participate in
the chess tournaments (i.e. participant observation) to directly interact with the chess
players and learn more about the community’s motivations and internal
dynamics.
Simple vs. Behavioral Observation
Simple observation: the researcher collects simple numerical data
Behavioral observation: the researcher interprets people’s behavior
Simple observations are often numerical, like how many cars pass through a given
intersection each hour or how many students are asleep during a class.
Behavioral observation, on the other hand, observes and interprets people’s behavior,
like how many cars are driving dangerously or how engaging a lecturer is.

The value of simple observation lies partly in its name – it collects simple numbers that
researchers can use to easily calculate trends or averages and provide clear numerical evidence.
However, simple observation doesn’t account for why those numbers might be. Behavioral
observation can be very valuable in terms of contextually understanding certain numerical trends,
but it does leave a lot of room for researcher bias or subjectivity.
For example, a researcher may combine simple observational data (how many people
attend a workshop) with behavioral observational data (how actively people participate in
the workshop) to assess how effective a workshop is.
Direct vs. Indirect Observation
Direct observation: the researcher observes an activity as it happens
Indirect observation: the researcher observes the results of an activity
While they can seem similar, direct and indirect observation have important methodical
differences. Direct observation is when the researcher is observing an activity or process while it’s
happening (e.g. they are watching students in the cafeteria at lunch to learn about their
eating habits).
In contrast, indirect observation involves the researcher observing the results of an activity or process after it
happens (e.g. they examine the trash left over after students’ lunches to learn about their food waste
habits). While they are very similar, direct and indirect observation occur at different times during the study and,
more notably, offer different relevant information to the researcher.

Covert observation: the researcher observes secretly
Overt observation: people know the researcher is observing them
Covert vs. overt observation has arguably the most noticeable difference in the role the
researcher plays in the process. Covert observation takes places when a researcher
is observing the activity in secret (perhaps through a hidden video camera). In
overt observation, as the name describes, the people being observed know a
researcher is observing them.
(ii) Survey Method
Once the sponsor or researcher has determined that surveying or interviewing is the
appropriate data collection approach, various means may be used to secure information
from individuals.
A researcher can conduct a semi structured interview or survey by personal
interview or telephone or can distribute a self-administered survey by
mail, fax, computer, e-mail, the Internet, or a combination of these.

The computer revolution been felt more strongly than in the area of the self-
administered survey. Computer-delivered self-administered
questionnaires (also labeled computer-assisted self- interviews, or
CASIs) use organizational intranets, the Internet, or online services via tablet
and mobile devices to reach their participants.
Participants may be targeted (as when BizRate, an online e-business rating
service, sends an e-mail to a registered e-purchaser to participate in a
survey following the completion of their order) or self-selecting (as
when a computer screen pop-up window offers a survey to an
individual who clicks on a particular website or when a potential
participant responds to a postcard or e-mail inquiry looking for
participants).
The questionnaire and its managing software may reside on the computer or its
network, on the cloud, or both may be sent to the participant by mail— disk-by-
mail (DBM) survey.
Data from Department of Commerce’s Economics and Statistics Administration
and National Telecommunications and Information Administration (NTIA) show
78.38 percent of U.S. households are actively online, while that number drops
below 50 percent 10 for poor households.

Intercept surveys—at malls, conventions, state fairs, vacation destinations, even busy city street
corners—may use a traditional paper-and-pencil questionnaire or a computer-delivered survey via an iPad, or
netbook computer, or a kiosk. The respondent participates without interviewer assistance, usually in a
predetermined environment, such as a room in a shopping mall.
(iii) Interview Method
Pre-requisites and basic tenets of interviewing: For successful implementation of the interview
method, interviewers should be carefully selected, trained and briefed. They should be
honest, sincere, hardworking, impartial and must possess the technical competence
and necessary practical experience.
Occasional field checks should be made to ensure that interviewers are neither cheating, nor
deviating from instructions given to them for performing their job efficiently. In
addition, some provision should also be made in advance so that appropriate action may be taken
if some of the selected respondents refuse to cooperate or are not available when an interviewer
calls upon them.
In fact, interviewing is an art governed by certain scientific principles. Every effort should
be made to create friendly atmosphere of trust and confidence, so that respondents
may feel at ease while talking to and discussing with the interviewer.
The interviewer must ask questions properly and intelligently and must record the responses
accurately and completely. At the same time, the interviewer must answer legitimate question(s),
if any, asked by the respondent and must clear any doubt that the latter has. The interviewers
approach must be friendly, courteous, conversational and unbiased. The interviewer should
not show surprise or disapproval of a respondent’s answer but he must keep the direction of
interview in his own hand, discouraging irrelevant conversation and must make all possible
effort to keep the respondent on the track

The interview method of collecting data involves presentation of oral-verbal stimuli
and reply in terms of oral-verbal responses. This method can be used through
personal interviews and through telephone interviews.
(a) Personal interviews: Personal interview method requires a person known as the
interviewer asking questions generally in a face-to-face contact to the other person
or persons This sort of interview may be in the form of direct personal
investigation or it may be indirect oral investigation.
In the case of direct personal investigation the interviewer has to collect the
information personally from the sources concerned. He has to be on the spot and has
to meet people from whom data have to be collected. This method is particularly
suitable for intensive investigations.
But in certain cases it may not be possible or worthwhile to contact directly the
persons concerned or on account of the extensive scope of enquiry, the direct personal
investigation technique may not be used. In such cases an indirect oral examination
can be conducted under which the interviewer has to cross-examine other persons
who are supposed to have knowledge about the problem under investigation and the
information, obtained is recorded.
Example :
Most of the commissions and committees appointed by government to carry

The method of collecting information through personal interviews is usually carried
out in a structured way. As such we call the interviews as structured interviews. Such
interviews involve the use of a set of predetermined questions and of highly
standardized techniques of recording. follows a rigid procedure laid down,
asking questions in a form and order prescribed.
As against it, the unstructured interviews are characterized by a flexibility of approach
to questioning. Unstructured interviews do not follow a system of pre-determined
questions and standardized techniques of recording information.
In a non-structured interview, the interviewer is allowed much greater freedom to
ask, in case of need, supplementary questions or at times he may omit
certain questions if the situation so requires. He may even change the
sequence of questions. He has relatively greater freedom while recording
the responses to include some aspects and exclude others. But this sort of
flexibility results in lack of comparability of one interview with another and the
analysis of unstructured responses becomes much more difficult and time-consuming
than that of the structured responses obtained in case of structured interviews.

They are focused interview, clinical interview and the non-directive interview.
Focussed interview is meant to focus attention on the given experience of the
respondent and its effects. Under it the interviewer has the freedom to decide the
manner and sequence in which the questions would be asked and has also the
freedom to explore reasons and motives. The main task of the interviewer in case of
a focussed interview is to confine the respondent to a discussion of issues with which
he seeks conversance. Such interviews are used generally in the development of
hypotheses and constitute a major type of unstructured interviews.
The clinical interview is concerned with broad underlying feelings or motivations
or with the course of individual’s life experience. The method of eliciting
information under it is generally left to the interviewer’s discretion.
In case of non-directive interview, the interviewer’s function is simply to encourage
the respondent to talk about the given topic with a bare minimum of direct
questioning.
The interviewer often acts as a catalyst to a comprehensive expression of the
respondents’ feelings and beliefs and of the frame of reference within which such
feelings and beliefs take on personal significance.

Telephone interviewing can be combined with immediate entry of the responses into a data file
by means of terminals, personal computers, or voice data entry. This brings added savings
in time and money.
The computer-assisted telephone interview (CATI) is used in research organizations
throughout the world. A CATI facility consists of acoustically isolated interviewing carrels
organized around supervisory stations. The telephone interviewer in each carrel has a
personal computer or terminal that is networked to the phone system and to the central
data processing unit.
A software program that prompts the interviewer with introductory statements, qualifying
questions, and precoded questionnaire items drives the survey. These materials appear on the
interviewer’s monitor.
CATI works with a telephone number management system to select numbers, dial the sample,
and enter responses. One facility, the Survey Research Center at the University of Michigan,
consists of 55 interviewer carrels with 100 interviewers working in shifts from 8 a.m. to
midnight (EST) to call nationwide. When fully staffed, it produces more than 10,000
interview hours per month.
Another means of securing immediate response data is the computer-administered telephone
survey. Unlike CATI, there is no human interviewer. A computer calls the phone number,
conducts the interview, places data into a file for later tabulation, and terminates the
contact.

This method of data collection is quite popular, particularly in case of big enquiries. It is being
adopted by private individuals, research workers, private and public organisations and even by
governments.
In this method a questionnaire is sent (usually by post) to the persons concerned with a request
to answer the questions and return the questionnaire.
A questionnaire consists of a number of questions printed or typed in a definite order on a
form or set of forms. The questionnaire is mailed to respondents who are expected to
read and understand the questions and write down the reply in the space meant for the
purpose in the questionnaire itself.
The respondents have to answer the questions on their own.
The method of collecting data by mailing the questionnaires to respondents is most extensively
employed in various economic and business surveys.
The merits of this method are as follows:
 There is low cost even when the universe is large and is widely spread geographically.
 It is free from the bias of the interviewer; answers are in respondents’ own words.
 Respondents have adequate time to give well thought out answers.
 Respondents, who are not easily approachable, can also be reached conveniently.
 Large samples can be made use of and thus the results can be made more dependable and
reliable.

The main demerits of this system can also be listed here:
 Low rate of return of the duly filled in questionnaires; bias due to no-response is often indeterminate.
 It can be used only when respondents are educated and cooperating.
 The control over questionnaire may be lost once it is sent.
 There is inbuilt inflexibility because of the difficulty of amending the approach once questionnaires have
been dispatched.
 There is also the possibility of ambiguous replies or omission of replies altogether to certain questions;
interpretation of omissions is difficult.
 It is difficult to know whether willing respondents are truly representative.
 This method is likely to be the slowest of all
.
Before using this method, it is always advisable to conduct ‘pilot study’ (Pilot Survey) for testing the
questionnaires. In a big enquiry the significance of pilot survey is felt very much. Pilot survey is
infact the replica and rehearsal of the main survey. Such a survey, being conducted by experts,
brings to the light the weaknesses (if any) of the questionnaires and also of the survey
techniques.
From the experience gained in this way, improvement can be effected.
Main aspects of a questionnaire:
Quite often questionnaire is considered as the heart of a survey operation. Hence it should be very carefully
constructed. If it is not properly set up, then the survey is bound to fail.
This fact requires us to study the main aspects of a questionnaire viz., the general form, question
sequence and question formulation and wording. Researcher should note the following with regard to
these three main aspects of a questionnaire:

General form: So far as the general form of a questionnaire is concerned, it can either be
structured or unstructured questionnaire. Structured questionnaires are those questionnaires
in which there are definite, concrete and pre-determined questions.
The questions are presented with exactly the same wording and in the same order to all
respondents. Resort is taken to this sort of standardization to ensure that all respondents reply to
the same set of questions. The form of the question may be either closed (i.e., of the type ‘yes’
or ‘no’) or open (i.e., inviting free response) but should be stated in advance and not constructed
during questioning.
Thus a highly structured questionnaire is one in which all questions and answers are specified
and comments in the respondent’s own words are held to the minimum. When these
characteristics are not present in a questionnaire, it can be termed as unstructured or non-
structured questionnaire.
More specifically, we can say that in an unstructured questionnaire, the interviewer is
provided with a general guide on the type of information to be obtained, but the exact
question formulation is largely his own responsibility and the replies are to be taken down
in the respondent’s own words to the extent possible; in some situations tape recorders may
be used to achieve this goal.

. A proper sequence of questions reduces considerably the chances of individual questions
being misunderstood. The question-sequence must be clear and smoothly-moving,
meaning thereby that the relation of one question to another should be readily
apparent to the respondent, with questions that are easiest to answer being put in the
beginning.
The first few questions are particularly important because they are likely to influence the
attitude of the respondent and in seeking his desired cooperation. The opening questions
should be such as to arouse human interest.
The following type of questions should generally be avoided as opening questions in a
questionnaire:
 questions that put too great a strain on the memory or intellect of the respondent;
 questions of a personal character;
 questions related to personal wealth, etc.
Following the opening questions, we should have questions that are really vital to the
research problem and a connecting thread should run through successive questions. Ideally,
the question sequence should conform to the respondent’s way of thinking. Knowing what
information is desired, the researcher can rearrange the order of the questions (this is
possible in case of unstructured questionnaire) to fit the discussion in each particular case.

But in a structured questionnaire the best that can be done is to determine the
question-sequence with the help of a Pilot Survey which is likely to produce good
rapport with most respondents.
Relatively difficult questions must be relegated towards the end so that even if
the respondent decides not to answer such questions, considerable information would
have already been obtained.
Thus, question-sequence should usually go from the general to the more
specific and the researcher must always remember that the answer to a given
question is a function not only of the question itself, but of all previous questions as
well.
For instance, if one question deals with the price usually paid for coffee and the
next with reason for preferring that particular brand, the answer to this latter
question may be couched largely in terms of price differences.

With regard to this aspect of questionnaire, the researcher should note that each question must
be very clear for any sort of misunderstanding can do irreparable harm to a survey. Question
should also be impartial in order not to give a biased picture of the true state of affairs.
Questions should be constructed with a view to their forming a logical part of a well thought out
tabulation plan. In general, all questions should meet the following standards—
a. should be easily understood;
b. should be simple i.e., should convey only one thought at a time;
c. should be concrete and should conform as much as possible to the respondent’s way of
thinking.
For instance, instead of asking. “How many razor blades do you use annually?” The more
realistic question would be to ask, “How many razor blades did you use last week?”
Concerning the form of questions, we can talk about two principal forms, viz., multiple
choice question and the open-end question.
In the former the respondent selects one of the alternative possible answers put to him,
whereas in the latter he has to supply the answer in his own words. The question with only two
possible answers (usually ‘Yes’ or ‘No’) can be taken as a special case of the multiple choice
question, or can be named as a ‘closed question.’ There are some advantages and
disadvantages of each possible form of question. Multiple choice or closed questions have the
advantages of easy handling, simple to answer, quick and relatively inexpensive to analyze.

This schedule method of data collection is very much like the collection of data
through questionnaire, with little difference which lies in the fact that
schedules (proforma containing a set of questions) are being filled in by the
enumerators who are specially appointed for the purpose.
These enumerators along with schedules, go to respondents, put to them the
questions from the proforma in the order the questions are listed and record the
replies in the space meant for the same in the proforma.
Enumerators should be intelligent and must possess the capacity of cross
examination in order to find out the truth. Above all, they should be honest,
sincere, hardworking and should have patience and perseverance.
This method of data collection is very useful in extensive enquiries and can lead to
fairly reliable results. It is, however, very expensive and is usually adopted in
investigations conducted by governmental agencies or by some big
organizations. Population census all over the world is conducted through this
method.

Warranty cards:
Warranty cards are usually postal sized cards which are used by dealers of consumer
durables to collect information regarding their products.
The information sought is printed in the form of questions on the ‘warranty cards’
which is placed inside the package along with the product with a request to the
consumer to fill in the card and post it back to the dealer.

Distributor or store audits:
Distributor or store audits are performed by distributors as well as manufactures through their salesmen
at regular intervals. Distributors get the retail stores audited through salesmen and use such
information to estimate market size, market share, seasonal purchasing pattern and so on.
The data are obtained in such audits not by questioning but by observation.
Store Audit : an examination of information relating to different products sold in a store and how
effective advertising, sales, price, etc. are compared to any competitors: Large retail companies are
turning to technology to manage the often vast quantities of information associated with store
audits.
.

For instance, in case of a grocery store audit, a sample of stores is visited periodically and data
are recorded on inventories on hand either by observation or copying from store records. Store
audits are invariably panel operations, for the derivation of sales estimates and compilation of
sales trends by stores are their principal ‘raison detre’. The principal advantage of this method
is that it offers the most efficient way of evaluating the effect on sales of variations of different
techniques of in-store promotion
Pantry audits:
Pantry audit technique is used to estimate consumption of the basket of goods at the
consumer level. In this type of audit, the investigator collects an inventory of
types, quantities and prices of commodities consumed. Thus in pantry audit
data are recorded from the examination of consumer’s pantry.
The usual objective in a pantry audit is to find out what types of consumers buy certain
products and certain brands, the assumption being that the contents of the pantry
accurately portray consumer’s preferences. A survey of consumer goods that homes
have at a given time. Apantry audit may be conducted over the telephone or using
a questionnaire and is intended to inform producers and retailers of what they
should make or stock.
Quite often, pantry audits are supplemented by direct questioning relating to
reasons and circumstances under which particular products were purchased in
an attempt to relate these factors to purchasing habits.

Consumer panels:
An extension of the pantry audit approach on a regular basis is known as ‘consumer panel’, where a set of
consumers are arranged to come to an understanding to maintain detailed daily records of their
consumption and the same is made available to investigator on demands.
In other words, a consumer panel is essentially a sample of consumers who are interviewed repeatedly
over a period of time. Mostly consume panels are of two types viz., the transitory consumer panel and
the continuing consumer panel.
A transitory consumer panel is set up to measure the effect of a particular phenomenon. Usually such a panel
is conducted on a before-and-after-basis. Initial interviews are conducted before the phenomenon takes
place to record the attitude of the consumer. A second set of interviews is carried out after the
phenomenon has taken place to find out the consequent changes that might have occurred in the
consumer’s attitude. It is a favourite tool of advertising and of social research.
A continuing consumer panel is often set up for an indefinite period with a view to collect data on a particular
aspect of consumer behaviour over time, generally at periodic intervals or may be meant to serve as a
general purpose panel for researchers on a variety of subjects.
Such panels have been used in the area of consumer expenditure, public opinion and radio and TV
listenership among others. Most of these panels operate by mail. The representativeness of the panel
relative to the population and the effect of panel membership on the information obtained after the two major
problems associated with the use of this method of data collection.

Use of mechanical devices:
The use of mechanical devices has been widely made to collect information by way of indirect means.
Eye camera, Pupilometric camera, Psychogalvanometer, Motion picture camera and Audiometer are
the principal devices so far developed and commonly used by modern big business houses, mostly
in the developed world for the purpose of collecting the required information.
Eye cameras are designed to record the focus of eyes of a respondent on a specific portion of a
sketch or diagram or written material. Such an information is useful in designing advertising
material.
Pupillometric cameras record dilation of the pupil as a result of a visual stimulus. The extent of
dilation shows the degree of interest aroused by the stimulus.
Psychogalvanometer is used for measuring the extent of body excitement as a result of the visual
stimulus.
Motion picture cameras can be used to record movement of body of a buyer while deciding to buy a
consumer good from a shop or big store. Influence of packaging or the information given on the
label would stimulate a buyer to perform certain physical movements which can easily be
recorded by a hidden motion picture camera in the shop’s four walls.
Audiometers are used by some TV concerns to find out the type of programmes as well as stations
preferred by people. A device is fitted in the television instrument itself to record these
changes. Such data may be used to find out the market share of competing television
stations.

Secondary data means data that are already available i.e., they refer to the data which have already
been collected and analyzed by someone else.
When the researcher utilizes secondary data, then he has to look into various sources from where he
can obtain them. In this case he is certainly not confronted with the problems that are usually
associated with the collection of original data.
Secondary data may either be published data or unpublished data. Usually published data are
available in: (a) various publications of the central, state are local governments; (b) various publications
of foreign governments or of international bodies and their subsidiary organisations; (c) technical and
trade journals; (d) books, magazines and newspapers; (e) reports and publications of various
associations connected with business and industry, banks, stock exchanges, etc.; (f) reports prepared
by research scholars, universities, economists, etc. in different fields; and (g) public records and
statistics, historical documents, and other sources of published information.
The sources of unpublished data are many; they may be found in diaries, letters, unpublished
biographies and autobiographies and also may be available with scholars and research workers, trade
associations, labour bureaus and other public/ private individuals and organisations.

Definition of Qualitative Research
Qualitative research is one which provides insights and understanding of the problem setting.
It is an unstructured, exploratory research method that studies highly complex phenomena that
are impossible to elucidate with the quantitative research. Although, it generates ideas or
hypothesis for later quantitative research.
Qualitative research is used to gain an in-depth understanding of human behaviour, experience,
attitudes, intentions, and motivations, on the basis of observation and interpretation, to find out
the way people think and feel. It is a form of research in which the researcher gives more weight
to the views of the participants.
Case study, grounded theory, ethnography, historical and phenomenology are the types of
qualitative research.
An ethnography is a means to represent graphically and in writing the culture of a
group.
Phenomenology is a qualitative research method that is used to describe how human
beings experience a certain phenomenon. ... Phenomenological research is typically
conducted through the use of in-depth interviews of small samples of participants.
Example: It is studying the green flash that sometimes happens just after sunset or just
before sunrise.

Phenomenology is a qualitative research method that is used to describe how
human beings experience a certain phenomenon.
A phenomenological study attempts to set aside biases and preconceived
assumptions about human experiences, feelings, and responses to a particular
situation.
Phenomenology is the philosophical study of observed unusual people or events as
they appear without any further study or explanation. An example of
phenomenology is studying the green flash that sometimes happens just after
sunset or just before sunrise.
Phenomenology makes use of a variety of methods including interviews,
conversations, participant observation, action research, focus meetings, analysis of
diaries and other personal texts.
Historical method is the collection of techniques and guidelines that historians
use to research and write histories of the past. Primary sources and other evidence
including those from archaeology are used.

Nature of Measurement
In everyday usage, measurement occurs when an established proxy verifies the height, weight,
or other feature of a physical object. How well you like a song, a painting, or the personality of a
friend is also a measurement. To measure is to discover the extent, dimensions,
quantity, or capacity of something, especially by comparison with a standard. We
measure casually in daily life, but in research the requirements are rigorous. Measurement
in research consists of assigning numbers to empirical events, objects or properties, or activities
in compliance with a set of rules.
This definition implies that measurement is a three-part process:
1. Selecting observable empirical events.
2. Developing a set of mapping rules: a scheme for assigning numbers or symbols to
represent aspects of the event being measured.
3. Applying the mapping rule(s) to each observation of that event.
Let us recall the term empirical . Researchers use an empirical approach to describe, explain,
and make predictions by relying on information gained through observation.

Researchers might also want to measure the styling desirability of a new
concept car at this show. They interview a sample of visitors and assign, with a
different mapping rule, their opinions to the following scale:
What is your opinion of the styling of the concept Car Styling?
Very desirable 5 4 3 2 1 Very undesirable
All measurement theorists would call the rating scale in Exhibit 11-1 a form of
measurement, but some would challenge whether classifying males and
females is a form of measurement.
Their argument is that measurement must involve quantification—that is,
“the assignment of numbers to objects to represent amounts or degrees of a
property possessed by all of the objects.”
This condition was met when measuring opinions of car styling. Our
approach endorses the more general view that “numbers as symbols within a
mapping rule” can reflect both qualitative and quantitative concepts.
The goal of measurement—indeed, the goal of “assigning numbers to
empirical events in compliance with a set of rules”—is to provide the
highest-quality, lowest-error data for testing hypotheses, estimation or
prediction, or description

1. NOMINAL SCALE
In business research, nominal data are widely used. In statistics, nominal data (also known
as nominal scale) is a type of data that is used to label variables without providing any
quantitative value. A common example of nominal data is gender; male and female. Other
examples include eye colour and hair colour. With nominal scales, you are collecting
information on a variable that naturally or by design can be grouped into two or more categories
that are mutually exclusive and collectively exhaustive.
If data were collected from the symphony patrons at the Glacier compound, patrons could be
classified by whether they had attended prior symphony performances or this was their first time.
Every patron would fit into one of the two groups within the variable attendance. The counting
of members in each group is the only possible arithmetic operation when a nominal scale is
employed. If we use numerical symbols within our mapping rule to identify categories, These
numbers are recognized as labels only and have no quantitative value

For example, the number 13 on a license plate
(i) does not imply the number of traffic tickets the registered owner has received,
(ii) or the number of accidents the car has been involved in,
( iii) or the number of state lines it has crossed, and
(iv) not the level of driving skill of its owner;
it is only a means of identification as it is assigned to a particular vehicle.
Nominal classifications may consist of any number of separate groups if the groups
are mutually exclusive and collectively exhaustive.

Mapping rule A given in the table is not a sound nominal scale because its
categories are not mutually exclusive or collectively exhaustive. Mapping rule B
meets the minimum requirements; it covers all the major religions and offers an
“other” option.
 Nominal scales are the least powerful of the other four data types. They
suggest no order or distance relationship and have no arithmetic origin. The
scale wastes any information that we may have about varying degrees of
attitude, skills, understandings, etc. In spite of all this, nominal scales are still
very useful and are widely used in surveys and other ex-post-facto research
when data are being classified by major sub-groups of the population.

Ordinal scales include the characteristics of the nominal scale plus an indication of order. Ordinal
data require conformity to a logical postulate, which states: If a is greater than b and b is greater
than c , then a is greater than c .
The use of an ordinal scale implies a statement of “greater than” or “less than” (an equality statement
is also acceptable) without stating how much greater or less. While ordinal measurement speaks of
greater-than and less-than measurements, other descriptors may be used—“superior to,” “happier
than,” “poorer than,” or “important than.”
Like a rubber yardstick, an ordinal scale can stretch varying amounts at different places along its length.
Thus, the real difference between ranks 1 and 2 on a satisfaction scale may be more or less than the
difference between ranks 2 and 3.
An ordinal concept can be extended beyond the three cases used in the simple illustration of a > b >
c. Any number of cases can be ranked.
Examples of ordinal data include attitude and preference scales. Because the numbers
used with ordinal scales have only a rank meaning, the appropriate measure of central
tendency is the median.
The median is the midpoint of a distribution. A percentile or quartile reveals the
dispersion.
Correlational analysis of ordinal data is restricted to various ordinal techniques. Measures of
statistical significance are technically confined to a body of statistics known as
nonparametric methods, synonymous with distribution-free statistics

Interval scales have the power of nominal and ordinal data plus one additional
strength:
They incorporate the concept of equality of interval (the scaled distance between 1
and 2 equals the distance between 2 and 3). Calendar time is such a scale. For
example, the elapsed time between 3 and 6 a.m. equals the time between 4 and 7
a.m.
One cannot say, however, that 6 a.m. is twice as late as 3 a.m., because “zero time”
is an arbitrary zero point. Centigrade and Fahrenheit temperature scales are other
examples of classical interval scales. Both have an arbitrarily determined zero point,
not a unique origin. Researchers treat many attitude scales as interval.
When a scale is interval and the data are relatively symmetric with one mode, you
use the arithmetic mean as the measure of central tendency.
You can compute the average time of a TV promotional message or the average
attitude value for different age groups in an insurance benefits study.
The standard deviation is the measure of dispersion. The product-moment
correlation, t -tests, F -tests, and other parametric tests are the statistical
procedures of choice for interval data.

The Celsius scale is a type of centigrade scale. A centigrade scale has 100 degrees
between the freezing and boiling points of water. The original Celsius scale actually had a
boiling point of 0 degrees and freezing point of 100 degrees. It ran in the opposite direction
of the modern scale!

Ratio scales incorporate all of the powers of the previous scales plus the provision for
absolute zero or origin. Ratio data represent the actual amounts of a variable. Measures of
physical dimensions such as weight, height, distance, and area are examples.
In business research, we find ratio scales in many areas. There are money values,
population counts, distances, return rates, productivity rates, and amounts of time (e.g.,
elapsed time in seconds before a customer service representative answers a phone
inquiry). IST – India Standard Time / India Time (Standard Time) ... India Standard
Time(IST) is 5:30 hours ahead of Coordinated Universal Time (UTC). This time zone is in
use during standard time in: Asia. India Standard Time is a half-hour time zone. Its
local time differs by 30 minutes instead of the normal whole hour.
Swatch’s Beat Time —a proposed standard global time introduced at the 2000 Olympics
that may gain favor as more of us participate in cross-time-zone chats (Internet or
otherwise)—is a ratio scale. It offers a standard time with its origin at 0 beats (12 midnight
in Biel, Switzerland, at the new Biel Meridian timeline). A day is composed of 1,000 beats,
with a “beat” worth 1 minute, 26.4 seconds.
With the Glacier project, Jason could measure a customer’s age, the number of years he
or she has attended, and the number of times a selection has been performed in the
Glacier summer festival.
These measures all generate ratio data. For practical purposes, however, the analyst would
use the same statistical techniques as with interval data.

A ratio scale has all the properties of an interval scale. Ratio data on the ratio scale has measurable
intervals. For example, the difference between a height of six feet and five feet is the same as the interval
between two feet and three feet.
.

The statistical techniques mentioned up to this point are usable with ratio scales. Other manipulations .
carried out with real numbers may be done with ratio-scale values. Thus, multiplication and division can be
used with this scale but not with the others mentioned.
Geometric and harmonic means are measures of central tendency, and coefficients of variation may also be
calculated for describing variability. Researchers often encounter the problem of evaluating variables that
have been measured on different scales. For example, the choice to purchase a product by a consumer is a
nominal variable, and cost is a ratio variable.
Certain statistical techniques require that the measurement levels be the same. Since the nominal variable
does not have the characteristics of order, distance, or point of origin, we cannot create them artificially after
the fact. The ratio-based salary variable, on the other hand, can be reduced. Rescaling product cost into
categories (e.g., high, medium, low) simplifies the comparison.
Variable pay is the portion of sales compensation determined by employee performance. When employees
hit their goals (aka quota), variable pay is provided as a type of bonus, incentive pay, or commission.
Base salary, on the other hand, is fixed and paid out regardless of employees meeting their goals.
In summary, higher levels of measurement generally yield more information. Because of the measurement
precision at higher levels, more powerful and sensitive statistical procedures can be used. As we saw with the
candy bar example, when one moves from a higher measurement level to a lower one, there is always a loss
of information. Finally, when we collect information at higher levels, we can always convert, rescale, or
reduce the data to arrive at a lower level.

There are three major criteria for evaluating a measurement tool or characteristics of good
measurement : validity, reliability, and practicality.
• Validity is the extent to which a test measures what we actually wish to measure.
• Reliability has to do with the accuracy and precision of a measurement procedure.
• Practicality is concerned with a wide range of factors of economy, convenience,
and interpretability.
I Test of Validity: one widely accepted classification of validity consists of three
major forms: (a) content validity, (b) criterion-related validity, and (c) construct validity
(a) Content Validity :
Content validity is the extent to which a measuring instrument provides adequate coverage of the topic
under study. If the instrument contains a representative sample of the universe, the content validity is
good. Its determination is primarily judgmental and intuitive.
It can also be determined by using a panel of persons who shall judge how well the measuring
instrument meets the standards, but there is no numerical way to express it.
Example : In the Glacier study, Jason must first determine what factors are influencing customer
satisfaction before determining if published indexes can be of value.
If the data collection instrument adequately covers the topics that have been defined as the relevant
dimensions, we conclude the instrument has good content validity.

(b) Criterion-related validity reflects the success of measures used for prediction or estimation. You may
want to predict an outcome or estimate the existence of a current behavior or time perspective.
The researcher may want to develop a preemployment test that will predict sales success. There may be
several possible criteria, none of which individually tells the full story.
Total sales per salesperson may not adequately reflect territory market potential, competitive conditions, or
the different profitability rates of various products. One might rely on the sales manager’s overall evaluation,
but how unbiased and accurate are such impressions? The researcher must ensure that the validity criterion
used is itself “valid.” Any criterion measure must be judged in terms of four qualities: (1) relevance,
(2) freedom from bias, (3) reliability, and (4) availability.
(1) criterion is relevant if it is defined and scored in the terms we judge to be the proper measures of
salesperson success. If you believe sales success is adequately measured by dollar sales volume achieved per
year, then it is the relevant criterion.
(2) Freedom from bias is attained when the criterion gives each salesperson an equal opportunity to score
well. The sales criterion would be biased if it did not show adjustments for differences in territory potential
and competitive conditions.
(3) A reliable criterion is stable or reproducible. An erratic criterion (using monthly sales, which are highly
variable from month to month) can hardly be considered a reliable standard by which to judge performance
on a sales employment test.
(4) Finally, the information specified by the criterion must be available.

(c ) Construct Validity In attempting to evaluate construct validity, we consider both the
theory and the measuring instrument being used. If we were interested in measuring the effect
of trust in cross-functional teams, the way in which “trust” was operationally defined would
have to correspond to an empirically grounded theory.
If a known measure of trust was available, we might correlate the results obtained using this
measure with those derived from our new instrument. Such an approach would provide us with
preliminary indications of convergent validity (the degree to which scores on one scale
correlate with scores on other scales designed to assess the same construct).
If Jason were to develop a customer satisfaction index for Glacier and, when compared, the
results revealed the same indications as a predeveloped, established index, Jason’s instrument
would have convergent validity.
Similarly, if Jason developed an instrument to measure satisfaction with the Complete Care
program and the derived measure could be confirmed with a standardized customer satisfaction
measure, convergent validity would exist.

A measure is reliable to the degree that it supplies consistent results. Reliability is a
necessary contributor to validity but is not a sufficient condition for validity.
The relationship between reliability and validity can be simply illustrated with the use of
a bathroom scale. If the scale measures your weight correctly (using a concurrent
criterion such as a scale known to be accurate), then it is both reliable and valid.
If it consistently overweighs you by six pounds, then the scale is reliable but not valid.
If the scale measures erratically from time to time, then it is not reliable and therefore
cannot be valid.
So if a measurement is not valid, it hardly matters if it is reliable—because it does not
measure what the designer needs to measure in order to solve the research problem.
In this context, reliability is not as valuable as validity, but it is much easier to assess.

Convenience
A measuring device passes the convenience test if it is easy to administer. A questionnaire or
a measurement scale with a set of detailed but clear instructions, with examples, is easier to
complete correctly than one that lacks these features.
In a well-prepared study, it is not uncommon for the interviewer instructions to be several
times longer than the interview questions.
Naturally, the more complex the concepts and constructs, the greater is the need for clear and
complete instructions. We can also make the instrument easier to administer by giving close
attention to its design and layout.
A long completion time, complex instructions, participant’s perceived difficulty with the survey,
and their rated enjoyment of the process also influence design.
Layout issues include crowding of material, poor reproductions of illustrations, and the
carryover of items from one page to the next or the need to scroll the screen when taking a
Web survey. Both design and layout issues make completion of the instrument more difficult.

Meaning of Scaling
It describes the procedures of assigning numbers to various degrees of opinion,
attitude and other concepts. This can be done in two ways viz.,
(i) making a judgement about some characteristic of an individual and then placing
him directly on a scale that has been defined in terms of that characteristic and
(ii) constructing questionnaires in such a way that the score of individual’s responses
assigns him a place on a scale. It may be stated here that a scale is a continuum,
consisting of the highest point (in terms of some characteristic e.g., preference,
favorableness, etc.) and the lowest point along with several intermediate points
between these two extreme points.
These scale-point positions are so related to each other that when the first point
happens to be the highest point, the second point indicates a higher degree in terms
of a given characteristic as compared to the third point and the third point indicates
a higher degree as compared to the fourth and so on.
Numbers for measuring the distinctions of degree in the attitudes/opinions are, thus,
assigned to individuals corresponding to their scale-positions.

Nature of Attitudes
There are numerous definitions, but one seems to capture the essence: An attitude is a
learned, stable predisposition to respond to oneself, other persons, objects, or issues in a
consistently favorable or unfavorable way.
Important aspects of this definition include the learned nature of attitudes, their relative
permanence, and their association with socially significant events and objects.
Because an attitude is a predisposition, it would seem that the more favorable one’s attitude is
toward a product or service, the more likely that the product or service will be purchased.
Let’s use Myra as an example to illustrate the nature of attitudes:
1. She is convinced that MindWriter has great talent, terrific products, and superior
opportunities for growth.
2. She loves working at MindWriter.
3. She expects to stay with the firm and work hard to achieve rapid promotions for greater
visibility and influence.
The first statement is an example of a cognitively based attitude. It represents Myra’s memories,
evaluations, and beliefs about the properties of the object. A belief is an estimate
(probability) about the truth of something.

In this case, it is the likelihood that the characteristics she attributes to her work environment
are true. The statement “I think the cellular market will expand rapidly to incorporate radio and
video” is also derived from cognition and belief.
The second statement above is an affectively based attitude. It represents Myra’s feelings,
intuition, values, and emotions toward the object. “I love the Yankees” and “I hate corn flakes”
are other examples of emotionally oriented attitudes.
Finally, researchers recognize a third component, conative or behaviorally based attitudes.
The concluding statement reflects Myra’s expectations and behavioral intentions toward her firm
and the instrumental behaviors necessary to achieve her future goals.
Attitude Scaling
Attitude scaling is the process of assessing an attitudinal disposition using a number that
represents a person’s score on an attitudinal continuum ranging from an extremely favorable
disposition to an extremely unfavorable one.
Scaling is the “procedure for the assignment of numbers (or other symbols) to a property of
objects in order to impart some of the characteristics of numbers to the properties in question.”
Procedurally, we assign numbers to indicants of the properties of objects.

Examples:
 Thus, one assigns a number scale to the various levels of heat and cold
and calls it a thermometer. To measure the temperature of the air,
you know that a property of temperature is that its variation leads to an
expansion or contraction of mercury.
 A glass tube with mercury provides an indicant of temperature change by
the rise or fall of the mercury in the tube.
 Similarly, your attitude toward your university could be measured on
numerous scales that capture indicators of the different dimensions of your
awareness, feelings, or behavioral intentions toward the school.

They can be listed as follows:
(I) Rating Attitude Scales
(II) Likert’s Scale
(III) Semantic Differential Scale
(IV) Thurstone scale
(V) Multi-Dimensional Scaling.

The rating scale involves qualitative description of a limited number of
aspects of a thing or of traits of a person. When we use rating scales (or
categorical scales), we judge an object in absolute terms against some specified
criteria i.e., we judge properties of objects without reference to other similar objects.
For example, a researcher asks questions about participants’ attitudes toward the
taste of a soft drink. The responses are “thirst quenching,” “sour,” “strong bubbly,”
“orange taste,” and “syrupy.”
These answers alone do not provide a means of discerning the degree of favorability
and thus would be of limited value to the researcher. However, with a properly
constructed scale, the researcher could develop a taste profile for the target
brand.
We use rating scales to judge properties of objects without reference to other similar
objects. These ratings may be in such forms as “like—dislike,” “approve—
indifferent—disapprove,” or other classifications using even more categories.

The graphic rating scale is quite simple and is commonly used in practice. Under it
the various points are usually put along the line to form a continuum and the rater
indicates his rating by simply making a mark (such as ü) at the appropriate point
on a line that runs from one extreme to the other.
Scale-points with brief descriptions may be indicated along the line, their function
being to assist the rater in performing his job. The following is an example of five-
points graphic rating scale when we wish to ascertain people’s liking or disliking any
product.

The simple category scale (also called a dichotomous scale) offers two mutually exclusive response
choices. In Exhibit 12-3 they are “yes” and “no,” but they could just as easily be “important”
and “unimportant,” “agree” and “disagree,” or another set of discrete categories if the
question were different.
This response strategy is particularly useful for demographic questions or where a dichotomous
response is adequate. When there are multiple options for the rater but only one answer is sought, the
multiple-choice, single-response scale is appropriate. Our example has five options.
The primary alternatives should encompass 90 percent of the range, with the “other” category
completing the participant’s list. When there is no possibility for an “other” response or exhaustiveness
of categories is not critical, the “other” response may be omitted. Both the multiple-choice, single-
response scale and the simple category scale produce nominal data.
A variation, the multiple-choice, multiple-response scale (also called a checklist ), allows
the rater to select one or several alternatives. In the example in Exhibit 12-3, we are
measuring seven items with one question, and it is possible that all seven sources for home
design were consulted.
The cumulative feature of this scale can be beneficial when a complete picture of the participant’s
choice is desired, but it may also present a problem for reporting when research sponsors expect
the responses to sum to 100 percent. This scale generates nominal data

The respondents may check at almost any position along the line which fact may
increase the difficulty of analysis. The meanings of the terms like “very much” and
“some what” may depend upon respondent’s frame of reference so much so that the
statement might be challenged in terms of its equivalency.
An example of itemized scale can be given to illustrate it.
Suppose we wish to inquire as to how well does a worker get along with his fellow
workers? In such a situation we may ask the respondent to select one, to express his
opinion, from the following:
 He is almost always involved in some friction with a fellow worker.
 He is often at odds with one or more of his fellow workers.
 He sometimes gets involved in friction.
 He infrequently becomes involved in friction with others.
 He almost never gets involved in friction with fellow workers.
The chief merit of this type of scale is that it provides more information and meaning to
the rater, and thereby increases reliability. This form is relatively difficult to develop
and the statements may not say exactly what the respondent would like to
express.

They require less time, are interesting to use and have a wide range of applications.
Besides, they may also be used with a large number of properties or variables. But their
value for measurement purposes depends upon the assumption that the respondents can
and do make good judgements.
If the respondents are not very careful while rating, errors may occur. Three types of errors
are common viz., the error of leniency, the error of central tendency and the error of hallo
effect.
 The error of leniency occurs when certain respondents are either easy raters or hard
raters.
 When raters are reluctant to give extreme judgements, the result is the error of
central tendency.
 The error of hallo effect or the systematic bias occurs when the rater carries over a
generalized impression of the subject from one rating to another.
This sort of error takes place when we conclude for example, that a particular report is
good because we like its form or that someone is intelligent because he agrees with us or
has a pleasing personality. In other words, hallo effect is likely to appear when the rater is
asked to rate many factors, on a number of which he has no evidence for judgement.

The Likert scale, developed by Rensis Likert (pronounced Lick-ert), is the most
frequently used variation of the summated rating scale. Summated rating scales
consist of statements that express either a favorable or an unfavorable attitude
toward the object of interest.
The participant is asked to agree or disagree with each statement. Each
response is given a numerical score to reflect its degree of attitudinal
favorableness, and the scores may be summed to measure the participant’s
overall attitude.

In Exhibit 12-3 , the participant chooses one of five levels of agreement. This is the
traditional Likert scale because it meets Likert’s rules for construction and testing.
The numbers indicate the value to be assigned to each possible answer, with 1 the
least favorable impression of Internet superiority and 5 the most favorable. Likert
scales also use 7 and 9 scale points.
Technically, this is known as a Likert type scale since its construction is often less
rigorous. However, the advantages of the 7- and 9- point scales are a better
approximation of a normal response curve and extraction of more variability among
respondents.
Originally, creating a Likert scale involved a procedure known as item analysis. In
the first step, a large number of statements were collected that met two criteria:
(1) Each statement was relevant to the attitude being studied;
(2) each was believed to reflect a favorable or unfavorable position on that attitude.
People similar to those who are going to be studied were asked to read each
statement and to state the level of their agreement with it, using a 5-point scale.
(3) A scale value of 1 indicated a strongly unfavorable attitude (strongly disagree). The
other intensities were 2 (disagree), 3 (neither agree nor disagree), 4 (agree), and
5 (strongly agree), a strongly favorable attitude (see Exhibit 12-3) .

The two extreme groups represent people with the most favorable and
least favorable attitudes toward the attitude being studied.
Item analysis assesses each item based on how well it discriminates
between those persons whose total score is high and those whose
total score is low.
It involves calculating the mean scores for each scale item among the
low scorers and high scorers.
The mean scores for the high-score and low-score groups are then tested
for statistical significance by computing t values.
(In evaluating response patterns of the high and low groups to the
statement “My digital camera’s features are exciting,” we secure the
results Exhibit: 12.4 )

After finding the t values for each statement, they are rank-ordered, and those
statements with the highest t values are selected. In statistics, the t-statistic is the
ratio of the departure of the estimated value of a parameter from its
hypothesized value to its standard error. It is used in hypothesis testing
The 20 to 25 items that have the highest t values (statistically significant differences
between mean scores) are selected for inclusion in the final scale.
Researchers have found that a larger number of items for each attitude object
improve the reliability of the scale. As an approximate indicator of a statement’s
discrimination power, one authority also suggests using only those statements whose
t value is 1.75 or greater, provided there are 25 or more subjects in each group.
Although item analysis is helpful in weeding out attitudinal statements that do not
discriminate well, the summation procedure causes problems for researchers.
The following example on website banner ads shows that the same summated score
can mean different things:
1. This banner ad provides the relevant information I expect.
2. I would bookmark this site to use in the future.
3. This banner ad is annoying.
4. I would click for deeper links to discover more details.

>Exhibit 12-4 Evaluating a Scale Statement by Item
Analysis For the statement “My digital camera’s features
are exciting,”
we select the data from the bottom 25 percent of the
distribution (low total score group) and the top 25
percent (high total score group).
There are 73 people in each group. The remaining 50
percent of the middle of the distribution is not
considered for this analysis.

Semantic Differential (SD) is a type of a rating scale designed to
measure the connotative meaning of objects, events, and concepts.
The connotations are used to derive the attitude towards the given
object, event or concept.
Osgood's Semantic Differential was an application of his more general
attempt to measure the semantics or meaning of words, particularly
adjectives, and their referent concepts
The respondent is asked to choose where his or her position lies, on a
scale between two polar adjectives (for example: "Adequate-Inadequate",
"Good-Evil" or "Valuable-Worthless").
Semantic differentials can be used to
measure opinions, attitudes and values on a psychometrically
controlled scale.

The method consists of a set of bipolar rating scales, usually with 7 points, by
which one or more participants rate one or more concepts on each scale item.
The SD scale is based on the proposition that an object can have several
dimensions of connotative meaning. The meanings are located in
multidimensional property space, called semantic space.
Connotative meanings are suggested or implied meanings, in addition to the
explicit meaning of an object. For example, a roaring fire in a fire place may
connote romantic as well as its more explicit meaning of burning flammable
material within a brick kiln.
One restaurant trying to attract patrons on slow Tuesday evenings offered a
special Tuesday menu and called it “down home cooking.” Yankee pot roast,
stew, and chicken pot pie, although not its usual cuisine, carried the
connotative meaning of comfort foods and brought patrons into the
restaurant, making Tuesday one of the busiest nights of the week.
Advertisers, salespeople, and product and package designers have
long known that they must use words, shapes, associations, and
images to activate a person’s connotative meanings

Osgood and his associates developed the semantic differential
method to measure the psychological meanings of an object to an
individual.
They produced a list of 289 bipolar adjective pairs, which were
reduced to 76 pairs and formed into rating scales for attitude research.
Their analysis allowed them to conclude that semantic space is
multidimensional rather than unidimensional.
Three factors contributed most to meaningful judgments by
participants:
(1) evaluation, (2) potency, and (3) activity.
These concepts from the historical thesaurus study (Exhibit 12-5)
illustrate the wide applicability of the technique to persons, abstract
concepts, events, institutions, and physical objects.

One study explored a retail store image using 35 pairs of words or
phrases classified into eight groups.
These word pairs were especially created for the study. Excerpts from this
scale are presented in Exhibit 12-6.
Other categories of scale items were “general characteristics of the company,”
“physical characteristics of the store,” “prices charged by the store,” “store
personnel,” “advertising by the store,” and “your friends and the store.”
Since the scale pairs are closely associated with the characteristics of the
store and its use, one could develop image profiles of various stores.

The semantic differential has several advantages. It is an efficient and
easy way to secure attitudes from a large sample. These attitudes may be
measured in both direction and intensity.
The total set of responses provides a comprehensive picture of the
meaning of an object and a measure of the person doing the rating.
It is a standardized technique that is easily repeated but escapes many
problems of response distortion found with more direct methods. It
produces interval data. Basic instructions for constructing an SD scale
are found in Exhibit 12-7 .

In Exhibit 12-8 we see a scale being used by a panel of corporate leaders evaluating
candidates for a high-level position in their industry’s lobbying association.
The selection of the concepts is driven by the characteristics they believe the
candidate must possess to be successful in advancing their agenda.
There are three candidates. Based on the panel’s requirements, we choose 10 scales
to score the candidates. The letters along the left side, which show the relevant
attitude dimension, would be omitted from the actual scale, as would the numerical
values shown.
Note that the evaluation, potency, and activity scales are mixed. To analyze the
results, the set of evaluation (E) values is averaged, as are those for the potency (P)
and activity (A) dimensions.
The data are plotted in a “snake diagram” in Exhibit 12-9 . Here the adjective pairs
are reordered so that evaluation, potency, and activity descriptors are grouped
together, with the ideal factor refl ected by the left side of the scale.

It is well established that in everyday life, there is a strong
affective component to our olfactory experience. Indeed,
when smelling a novel odor for the first time our initial
reaction is to determine whether we like or dislike the odor.
Subsequent exposure to even a familiar odor often evokes an
emotional response (good-bad) prior to any other conscious
awareness of the odor properties or source. Indeed, Woskow
(Woskow, 1968), using multidimensional mapping
techniques, identified one of the primary dimensions
of olfactory experience as an affective one.
The Use of Semantic Differential Scaling to Define the MultiDimensional
Representation of Odors

When smelling an odor, it is common to recognize that
it is familiar and that it belongs in a general class or
category (i.e. food vs. floral), but producing a name or
label for the olfactory sensation is often a very difficult
task.
Several factors have been proposed to account for this
phenomenon, commonly called ‘the tip of the nose.
In addition, there is no universally accepted system for describing
many odors which also leads to greater reliance on specific item
associations. However, although the descriptive vocabulary for smells
is sparse and limited primarily to nomenclature related to the source
objects (i.e., smells like orange, banana, coffee) or the situation
where encountered (i.e., smells like the movies, beach, locker room),
the mental representation of any odor may well consist of a rich
network of semantic dimensions which can be elucidated via
language.

Lawless (Lawless, 1999) has persuasively argued that that
assumptions of independence in odor quality descriptors in
the psychophysical intensity model may be insufficient to
fully capture the multi-dimensional nature of odor
representation.
In addition, developing a classification system to obviate
utilizing culture specific odor quality descriptors can foster
better cross-cultural comparisons of olfactory experience.
For these reasons, we explored whether Semantic
Differential Scaling, a well established method for
evaluating affective responses to examples in many
other stimulus domains, would be of utility in
characterizing the affective response to odors.

The goal of the current study was to use the SD
methodology to determine the dimensions of affective
variation inherent in olfactory experience and
representation, and more practically to develop from a
larger group, a set of SD adjectives which were most
relevant and useful for evaluating olfactory
experience.

Materials and Methods
Participants 300 healthy adults were tested. They had a mean age
of 28 (+/−11) and were approximately equally distributed between
males (n=149) and females (n=151).
They were drawn from the metropolitan Philadelphia area
and recruited through advertisements placed in local
newspapers.
The racial/ethnic distribution of the test population was as
follows: Caucasian (n=145), African-American (n=47), Asians
(n=87), Hispanic (n=16) and Native American (n=5).
The individuals who were selected were free of colds or allergies at
the time they were being tested. All participants completed
standardized vocabulary tests prior to enrollment in the study to
ensure minimum comparable verbal skills.

Odor Stimuli Table 1 presents the 30 odorants that were used in this study.
The odorants were delivered in the form of Viscopearls ™, small polystyrene
beads that were impregnated with the appropriate fragrance, obtained from
the KAO company (KAO, Tokyo, Japan) and matched for relative intensity.
2.0 g of the fragrance beads were placed into small, opaque jars that were
covered with a perforated plastic cover, through which the subjects could
sniff the fragrance.
An airtight cover was placed on the jars when the odorants were not being
sampled and were refrigerated when not being used at 45 F to maintain
consistency over the course of the study.

Since rating 30 odorants on 50 scales is an extremely time-
consuming task and in particular, in the field of olfaction,
can adapt or desensitize the nose, the odorants were divided
into 3 sets and each set was rated by a group of 100 subjects.
Each subject evaluated only 10 odor stimuli out of the 30
total using the semantic differential scale, but the
assignment of odorant stimuli to subjects was randomized
so that 100 subjects evaluated each odorant.
The odorants were selected to represent odors that were
both familiar (i.e., lemon, banana) and unfamiliar (i.e.,
hinoki, galbanum, cassis) to participants in the US. Each set
of 10 contained both familiar and unfamiliar odorants

The name of L.L. Thurstone is associated with differential scales which have been
developed using consensus scale approach.
Under such an approach the selection of items is made by a panel of judges who
evaluate the items in terms of whether they are relevant to the topic area and
unambiguous in implication. The detailed procedure is as under:
 The researcher gathers a large number of statements, usually twenty or
more, that express various points of view toward a group, institution, idea, or
practice (i.e., statements belonging to the topic area).
 These statements are then submitted to a panel of judges, each of whom
arranges them in eleven groups or piles ranging from one extreme to another
in position.
 Each of the judges is requested to place generally in the first pile the
statements which he thinks are most unfavorable to the issue, in the second
pile to place those statements which he thinks are next most unfavorable and
he goes on doing so in this manner till in the eleventh pile he puts the
statements which he considers to be the most favorable.

 This sorting by each judge yields a composite position for each of the items. In
case of marked disagreement between the judges in assigning a position to an
item, that item is discarded.
 For items that are retained, each is given its median scale value
between one and eleven as established by the panel. In other words,
the scale value of any one statement is computed as the ‘median’
position to which it is assigned by the group of judges.
 A final selection of statements is then made. For this purpose a sample of
statements, whose median scores are spread evenly from one extreme
to the other is taken. The statements so selected, constitute the final
scale to be administered to respondents. The position of each
statement on the scale is the same as determined by the judges.

After developing the scale as stated above, the respondents are asked during the
administration of the scale to check the statements with which they agree.
It may be noted that in the actual instrument the statements are arranged in random order of
scale value.
If the values are valid and if the opinionnaire deals with only one attitude dimension, the
typical respondent will choose one or several contiguous items (in terms of scale values) to
reflect his views.
However, at times divergence may occur when a statement appears to tap a different attitude dimension.
The Thurstone method has been widely used for developing differential scales which are utilized to measure
attitudes towards varied issues like war, religion, etc. Such scales are considered most appropriate
and reliable when used for measuring a single attitude.
But an important deterrent to their use is the cost and effort required to develop them. Another
weakness of such scales is that the values assigned to various statements by the
judges may reflect their own attitudes. The method is not completely objective; it
involves ultimately subjective decision process.
Critics of this method also opine that some other scale designs give more information about
the respondent’s attitude in comparison to differential scales.

Thurstone scale is defined as a unidimensional scale that
is used to track respondent’s behavior, attitude or feeling
towards a subject.
This scale consists of statements about a particular issue
or topic where each statement has a numerical value that
indicates the respondents attitude towards the topic as
favorable or unfavorable.
Respondents indicate the statements that they agree
with, and an average is computed. A mean score of the
agreements or disagreements is calculated as the
attitude of the respondent towards the topic.

How to conduct a Thurstone Scale Survey with an Example
An example of a Thurstone scale survey is to understand the attitude of
employees in an organization towards diversity hiring in that organization.
There are 2 distinct milestones in the Thurstone scale question; to derive
the final questions and administer the Thurstone scale question and
conduct its analysis.
Diversity is important in recruiting and retention. It's no doubt that
employers want to hire the best people for their company. ... They can
retain their employee's motivation who will continue to work resulting from
workforce diversity.
Diversity within a company will allow for employee productivity and
engagement.
Diversity hiring is hiring based on merit with special care taken to
ensure procedures are free from biases related to a candidate's age,
race, gender, religion, sexual orientation, and other personal
characteristics that are unrelated to their job performance.

Derive the Final Question
There are 5 distinctive steps to derive the final question.
They are:
1.Step 1 – Develop statements: Develop a large
number of agree/disagree statements on a certain topic.
For example, if you wanted to find out people’s attitudes
towards the policy of diversity hiring in an organization,
your statements may include:
 Policy on diversity hiring is wrong.
 Diversity hiring takes away jobs from deserving
candidates.
 Diversity hiring brings different viewpoints to the team.
 Diversity hiring brings out the best in the local
community.
 Diversity hiring aids in increasing the reputation of a
brand.

2. Step 2 – Rank each statement with a panel
of judges: The next step would be to have
a panel of judges rate each item on a scale of 1
to 11 where 1 is the least favorable attitude
towards the common vector – diversity hiring
and 11 is an extremely favorable attitude.
It is important to note that the judges are
required to rate each option and not agree or
disagree with them.

4. Step 3 – Calculate median and/or
mean and Interquartile range (IQR): The
data collected from all judges is to then
be analyzed to draw up a table with the
mean or median values in the ascending
order.
Using median or mean is a personal
choice and the options throw up accurate
results in the use of either.
If you have 50 statements, you need to
have 50 means/medians and 50 IQR’s.

5. Step 5 – Selecting final variables or
options: Select options on the basis of the above
table. For example, you could select one item from
each mean/median value.
You want the statements with the most agreement
between judges. For each median value, this is
the item with the lowest interquartile range. This is
a “Rule of Thumb”: you don’t have to choose this
item.
If you decide it’s poorly worded or ambiguous,
choose the item above it (with the next lowest
IQR).

Once the final questions have been decided
upon, they are shared with the respondents to
select from agreement or disagreement.
The ratings are shown in parenthesis but the
rating is not shared with the actual
respondents.

•Administering and analysis by median or
mean: The question and the subsequent options can
be administered to the respondents by using either the
mean or the median score in the below format.
•The weights of the statements are summed and
divided by the number of statements checked.
• If a respondent were to agree with statements 2, 5, 7
and 10; the attitude score is 10.5 + 2.5 + 4.5 + 6.0 =
23.5/4 = 5.8. Dividing the number of statements puts
this score at a little above the midway point of the 1-11
scale.
•This score indicates that the attitude is slightly
favorable to diversity hiring in the organization.

•Administering and analysis by simple
count or percentage: In the same
example above, if the question is
administered without the mean or median
score, the calculation can be depicted by a
simple count of agreement on the 1-11
scale or a percentage.
• If the respondent were to agree with
statements 1, 4, 5, 6, 8, 9 and 11; the
agreement count is 7 out of 11 which puts
the percentage at 63.63% which means
that the attitude is favorable towards
diversity hiring.

Use of a Thurstone Scale Survey
The Thurstone scale survey is used to measure the
respondents attitudes on a particular subject. The scale
can be applied to a wide range of market research
surveys, in market research including:
•Surveys that measure opinions: The Thurstone scale
question produces quantifiable data about the measures
of strength of the respondents opinions.
•Those that gauge attitudes or feelings: This scale is
used effectively in customer satisfaction to predict future
purchasing trends and in employee engagement to
calculate turnover.

Multidimensional scaling is a visual representation
of distances or dissimilarities between sets of
objects. “Objects” can be colors, faces, map
coordinates, political persuasion, or any kind of real or
conceptual stimuli.
Objects that are more similar (or have shorter
distances) are closer together on the graph than
objects that are less similar (or have longer distances).
As well as interpreting dissimilarities as distances on a
graph, MDS can also serve as a dimension reduction
technique for high-dimensional data.

Dimensionality in statistics refers to how many attributes a
dataset has. For example, healthcare data is notorious for
having vast amounts of variables (e.g. blood pressure,
weight, cholesterol level).
In an ideal world, this data could be represented in a
spreadsheet, with one column representing each
dimension. In practice, this is difficult to do, in part because
many variables are inter-related (like weight and blood
pressure).
Note: Dimensionality means something slightly different in
other areas of mathematics and science. For example, in
physics, dimensionality can usually be expressed in terms
of fundamental dimensions like mass, time, or length.

High Dimensional means that the number of dimensions are staggeringly high — so
high that calculations become extremely difficult. With high dimensional data, the
number of features can exceed the number of observations.
For example, microarrays, which measure gene expression, can contain tens of
hundreds of samples. Each sample can contain tens of thousands of genes.
One person (i.e. one observation) has millions of possible gene combinations. Other
areas where features exceed observations include finance, high resolution imaging,
and website analysis (e.g. advertising, crawling, or ranking).
What is Reduction of Dimensionality?
Reduction of dimensionality means to simplify understanding of data, either
numerically or visually. Data integrity is maintained. To reduce dimensionality,
you could combine related data into groups using a tool
like multidimensional scaling to identify similarities in data. You
could also use clustering to group items together.

High-dimensional microarray analysis.

The term scaling comes from psychometrics, where abstract concepts
(“objects”) are assigned numbers according to a rule (Trochim, 2006).
For example, you may want to quantify a person’s attitude to global
warming. You could assign a “1” to “doesn’t believe in global warming”, a
10 to “firmly believes in global warming” and a scale of 2 to 9 for
attitudes in between.
You can also think of “scaling” as the fact that you’re essentially scaling
down the data (i.e. making it simpler by creating lower-dimensional data).
Data that is scaled down in dimension keeps similar properties. For
example, two data points that are close together in high-
dimensional space will also be close together in low-dimensional
space (Martinez, 2005).
The “multidimensional” part is due to the fact that you aren’t limited to
two dimensional graphs or data. Three-dimensional, four-dimensional and
higher plots are possible.

Let’s say you were given a list of city locations, and were asked to create a
map that included distances between cities.
The procedure would be relatively straightforward, involving nothing more
complicated than taking a ruler and measuring the distance between each
city. However, what if you were given only the distances between the cities
(i.e. their similarities) — and not their locations?
You could still create a map — but it would involve a fair amount of
geometry, and some logical deductions. Kruskal & Wish (1978) —
the authors of one of the first multidimensional scaling books —
state that this type of logic problem is ideal for multidimensional
scaling.
You’re basically given a set of differences, and the goal is to create a map
that will also tell you what the original distances where and where they were
located.

Basic steps:
1.Assign a number of points to coordinates in n-
dimensional space. N-dimensional space could be 2-
dimensional, 3-dimensional, or higher spaces (at least,
theoretically, because 4-dimensional spaces and above are
difficult to model).
2. The orientation of the coordinate axes is arbitrary and is
mostly the researcher’s choice. For maps like the one in the
simple example above, axes that represent north/south and
east/west make the most sense.

1.Calculate Euclidean distances for all pairs of points.
The Euclidean distance is the “as the crow flies” straight-line
distance between two points x and y in Euclidean space.
2.It’s calculated using the Pythagorean theorem (c2 = a2 + b2),
although it becomes somewhat more complicated for n-
dimensional space (see “Euclidean Distance in n-dimensional
space“). This results in the similarity matrix.

.Compare the similarity matrix with the original input matrix by
evaluating the stress function. Stress is a goodness-of-fit measure,
based on differences between predicted and actual distances.
In his original 1964 MDS paper, Kruskal wrote that fits close to zero are
excellent, while anything over .2 should be considered “poor”. More
recent authors suggest evaluating stress based on the quality of the
distance matrix and how many objects are in that matrix.
3.Adjust coordinates, if necessary, to minimize stress.

Notation
Although MDS is commonly used as a measure of dissimilarity, MDS can technically
measure similarity as well. Dissimilarity between two points r and s is denoted
δrs and similarity is denoted srs.
Small δrs indicates values that are close together and larger values indicate values
that are farther apart (i.e. are more dissimilar). On the other hand, similarity values
are the opposite: small srs indicates values that are farther apart; larger values
suggest more similarity (i.e. values are closer together).
Similarity measures are easily converted from one to another by a monotone
decreasing transformation (Buja et. al, 2007). NCSS (n.d.) gives the following formula
for the transformation:
Where:
 drs = a dissimilarity
 srs = a similarity

1. BUSINESS RESEARCH METHODS, TWELFTH EDITIONDONALD R. COOPER |
PAMELA S. SCHINDLER.
2. RESEARCH METHODOLOGY: METHODS AND TECHNIQUES, CR
KOTHARI.
3. The Use of Semantic Differential Scaling to Define the MultiDimensional
Representation of Odors, Pamela Dalton1, Christopher Maute1, Akiko Oshida2,
Satoshi Hikichi2, and Yu Izumi2 1Monell Chemical Senses Center, Philadelphia,
PA USA 2KAO Corporation, Tokyo, Japan. Published in final edited form as: J
Sens Stud . 2008 ; 23(4): 485–497. doi:10.1111/j.1745-459X.2008.00167.x.

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