Research Methodology: Questionnaire, Sampling, Data Preparation

INDIA BEST COMPANY TO WORK
FOR 2015

Methodology
• Firstly, perceptions of the organisations'
employees are measured using the Great
Place To Work Trust Index© Model©
• Secondly, the strength of people related
management practices of the organisation is
assessed using the Great Place To Work
Culture Audit© Framework© and they have
selected nine key areas

Learning Objectives
• To able to decide on basics of scales and measurement in
research

13-8
Measurement
“If you can’t measure it,
you can’t manage it.”
Bob Donath,
Consultant

What Should be Measured?
 The measurement of physical properties is not a complex deal,
whereas measurement of psychological properties requires a
careful attention of a researcher.
 The quality of the research always depends on the fact that
what measurement techniques are adopted by the researcher
and how these fit in the prevailing research circumstances.

Measurement and Scaling
Measurement means assigning numbers or other symbols to
characteristics of objects according to certain pre-specified
rules.
What we are going to measure is not the object but the
characteristics of the same.

Measurement and Scaling
Scaling involves creating a continuum upon which measured
objects are located.
Consider an attitude scale from 1 to 100. Each respondent is
assigned a number from 1 to 100, with 1 = Extremely
Unfavorable, and 100 = Extremely Favorable. Measurement is
the actual assignment of a number from 1 to 100 to each
respondent. Scaling is the process of placing the respondents
on a continuum with respect to their attitude toward
department stores.

Scales of Measurement
 Nominal scale
 Ordinal scale
 Interval scale
 Ratio scale

13-13
Levels of Measurement
OrdinalOrdinal
intervalinterval
RatioRatio
NominalNominal ClassificationClassification

13-14
Nominal Scales
• The numbers serve only
as labels or tags for
identifying and
classifying objects.
• The numbers do not
reflect the amount of
the characteristic
possessed by the
objects.

13-15
OrdinalOrdinal
intervalinterval
RatioRatio
OrderOrder
ClassificationClassification

Ordinal Scales
• A ranking scale in which
numbers are assigned to
objects to indicate the
relative extent to which the
objects possess some
characteristic.
• Can determine whether an
object has more or less of a
characteristic than some
other object, but not how
much more or less.

OrdinalOrdinal
intervalinterval
RatioRatio
OrderOrder
OrderOrder
ClassificationClassification DistanceDistance

Interval Scales
• Numerically equal distances
on the scale represent
equal values in the
characteristic being
measured.
• It permits comparison of
the differences between
objects.
• The location of the zero
point is not fixed. Both the
zero point and the units of
measurement are arbitrary.

13-19
OrdinalOrdinal
intervalinterval
RatioRatio
OrderOrder
OrderOrder
Natural OriginNatural Origin
OrderOrder

13-20
Ratio Scales
• Characteristics of
previous scales plus an
absolute zero point
• Examples
– Weight
– Height
– Number of children

Primary Scales of Measurement
7 38
Scale
Nominal Numbers
Assigned
to Runners
Ordinal Rank Order
of Winners
Interval Performance
Rating on a
0 to 10 Scale
Ratio Time to
Finish, in
Third
place
Second
place
First
place
Finish
Finish
8.2 9.1 9.6
15.2 14.1 13.4

A comparison between the four levels of data measurement in terms of
usage potential

Methodology
• Corruption Perceptions Index (CPI): Higher the
score points on a scale of 0-100

Primary Scales of Measurement
Scale Basic
Characteristics
Common
Examples
Marketing
Examples
Nominal Numbers identify
& classify objects
Social Security
nos., numbering
of football players
Brand nos., store
types
Percentages,
mode
Chi-square,
binomial test
Ordinal Nos. indicate the
relative positions
of objects but not
the magnitude of
differences
between them
Quality rankings,
rankings of teams
in a tournament
Preference
rankings, market
position, social
class
Percentile,
median
Rank-order
correlation,
Friedman
ANOVA
Ratio Zero point is fixed,
ratios of scale
values can be
compared
Length, weight Age, sales,
income, costs
Geometric
mean, harmonic
mean
Coefficient of
variation
Permissible Statistics
Descriptive Inferential
Interval Differences
between objects
Temperature
(Fahrenheit)
Attitudes,
opinions, index
Range, mean,
standard
t tests,
regression

Sample Questionnaire
• 1. What is your name?______________
• 2. What is your gender? Please tick 1: Male 2. Female
• 3. What is your date of birth? ……………………
• 4. What is your age?....................
• a) 16.1-18 Years b) 18.1- 20 years c) 20.1 – 22 years d) More than 22
years
• 5. What is the total number of years you completed in an current
institute?…….
• Give ranking 1-5 on your favorite subject
• FM
• HRM
• Marketing
• Research
• Human Value

Sample Questionnaire
• 6. Which discipline do you belong to? Please select one:
• a) Management
• b) Engineering
• c) Pharmacy
• d) Any Other
• 7. What is your CGPA? ……………….
• 8. What is your height? …………..
• 9. How much are you satisfied with current program?
1- Extremely Dissatisfied 2- Dissatisfied 3- Neutral 4- Satisfied
5- Extremely Satisfied
10. How much are you satisfied with Faculty?
1- Extremely Dissatisfied 2- Dissatisfied 3- Neutral 4- Satisfied
5- Extremely Satisfied

Measurement Scales
 Comparative scales are based on the direct comparison of
stimulus and generally generate some ranking or ordinal data.
 Non-comparative scaling techniques generally involve the use
of a rating sale, and the resulting data are interval or ratio in
nature and each object is scaled independently of the others in
the stimulus set

A Classification of Scaling Techniques
Likert
Semantic
Differential
Stapel
Scaling Techniques
Noncomparative
Scales
Comparative
Scales
Paired
Comparison
Rank
Order
Constant
Sum
Q-Sort and
Multiple
Choice
Continuous
Rating Scales
Itemized Rating
Scales

Multiple-Choice Scale
 To generate some basic information to conduct his or her
research work and assign codes.
 It results in generating the nominal data.

Examples of multiple-choice scales

Rank Order Scaling
 In the rank order scaling technique, the respondents
rank different objects simultaneously from a list of
objects presented to them.

Paired-Comparison Technique
 As the name indicates, in the paired-comparison scaling
technique, a respondent is presented a pair of objects or
stimulus or brands and the respondent is supposed to provide
his or her preference of the object from a pair.
 When n items (obects or brands) are included in the study, a
respondent has to make n(n −1) / 2 paired comparisons.

Constant-Sum Scales
 In the constant-sum scaling technique, the respondents
allocate points to more than one stimulus objects or object
attributes or object properties, such that the total remains a
constant sum of usually 10 or 100.
 The sum of all the points should be equal to a predefined
constant 100 or 10, which is why this scale is called the
constant-sum scale. This scaling technique generates the ratio-
level data.

Example of a constant-sum scale

Q-Sort Scales
 The objective of the Q-sort scaling technique is to quickly
classify a large number of objects. In this kind of scaling
technique, the respondents are presented with a set of
statements, and they classify it on the basis of some predefined
number of categories (piles).

Itemized Rating Scales
 Multi-item scaling techniques generally generate some interval
type of information.
 For the majority of researchers, the rating scales are the
preferred measuring device to obtain interval (or quasi-interval)
data on the personal characteristics (i.e., attitude, preference,
and opinions) of the individuals of all kind.

The Likert Scales
 In a Likert scale, each item response has five rating categories, “strongly
disagree” to “strongly agree” as two extremes with “disagree,” “neither
agree nor disagree,” and “agree” in the middle of the scale. Typically, a
1- to 5-point rating scale is used, but few researchers also use another
set of numbers such as −2, −1, 0, +1, and +2.

Semantic Differential Scales
 The semantic differential scale consists of a series of bipolar
adjectival words or phrases placed on the two extreme points
of the scale.
 Good semantic differential scales keep some negative adjectives
and some positive adjectives on the left side of the scale to
tackle.

Example of semantic differential scale

Staple Scales
 The staple scale is generally presented vertically with a single
adjective or phrase in the centre of the positive and negative
ratings.
 Similar to the Likert scale and the semantic differential scale, in a
staple scale, points are at equidistant position both physically and
numerically, which usually results in the interval-scaled responses.

Continuous Rating Scales
 In a continuous rating scale, the respondents rate the object by
placing a mark on a continuum to indicate their attitude. In this
scale, the two ends of continuum represent the two extremes of
the measuring phenomenon.

Example of a continuous rating scale

Factors in Selecting an Appropriate Measurement
scale

Forced or
Unforced Choices
Very bad
Bad
Neither good nor bad
Good
Very good
Very bad
Bad
Good
Very good
No opinion
Don’t know
How good a player is MS Dhoni?

Balanced or Unbalanced
Very bad
Bad
Good
Very good
Poor
Fair
Good
Very good
Excellent

Number of Scale Points
Very bad
Bad
Good
Very good
Very bad
Somewhat bad
A little bad
A little good
Somewhat good
Very good

Scale Basic
Characteristics
Examples Advantages Disadvantages
Continuous
Rating
Scale
Place a mark on a
continuous line
Reaction to
TV
commercials
Easy to construct Scoring can be
cumbersome
unless
computerized
Itemized Rating Scales
Likert Scale Degrees of
agreement on a 1
(strongly disagree)
to 5 (strongly agree)
scale
Measurement
of attitudes
Easy to construct,
administer, and
understand
More
time-consuming
Semantic
Differential
Seven - point scale
with bipolar labels
Brand,
product, and
company
images
Versatile Controversy as
to whether the
data are interval
Stapel
Scale
Unipolar ten - point
scale, - 5 to +5,
witho ut a neutral
point (zero)
Measurement
of attitudes
and images
Easy to construct,
administer over
telephone
Confusing and
difficult to apply
Basic Noncomparative Scales

13-60
OrdinalOrdinal
intervalinterval
RatioRatio
OrderOrder
OrderOrder
Natural OriginNatural Origin
OrderOrder

Construct
• In the context of business research, a
construct is the abstract idea, underlying
theme, or subject matter that one wishes to
measure using survey questions.
• For example:- Job Satisfaction, Service Quality,
Consumer Behavior

• Measurement is so difficult in management because the
phenomena of interest are typically behavioral in nature. As
such, current measuring devices (e.g., questionnaires) are
subject to substantial measurement error.
• Researchers must first operationally defined the constructs
and then devise a means by which they can be measured.

The Criteria for Good Measurement
• Validity
• Reliability
• Sensitivity

1. Validity
 In fact, Validity is the ability of an instrument to measure what is
designed to measure.
 How well does the measure or design do what it points to do?
 It sounds simple that a measure should measure what it is supposed
to measure but has a great deal of difficulty in real life.

Service Quality
• Tangibility
• Empathy
• Responsiveness
• Assurance
• Reliability

2. Reliability
 Reliability is the degree to which an assessment tool produces stable and
consistent results.
 Reliability is the tendency of a respondent to respond in the same or in a
similar manner to an identical or a near identical question (Burns & Bush,
1999).

3. Sensitivity
 Sensitivity is the ability of a measuring instrument to measure the meaningful
difference in the responses obtained from the subjects included in the study.
 It is to be noted that the dichotomous categories of response such as yes or no
can generate a great deal or variability in the responses.
 Hence, a scale with many items as a sensitive measure is required.
 For example, a scale based on five categories of responses, such as “strongly
disagree,” “disagree,” “neither agree nor disagree,” “agree,” and “strongly
agree,” presents a more sensitive measuring instrument.

Learning Objectives
• To develop insight on various types of
secondary data and their uses

Meaning of Secondary Data
 Primary data are mainly collected by a researcher to address the
research problem. In other words, these are not readily available from
various sources, rather the researcher has to systematically collect it for
a pre-specified research problem.
 Secondary data are the data that have already been collected by
someone else before the current needs of a researcher.
 The present researcher only uses these data with related reference and
never collects it from the field.
 When compared with the primary data, secondary data can be collected
easily with time and cost efficiency.

Benefits of Using
Secondary Data
 The main advantage of using secondary data sources is that they
already exist
 There may be cases when the problem is general, such as the
demographic structure of a population at a particular region, in
such cases there is no meaning in collecting the primary data.
 The various available secondary data sources such as the
indiastat.com, the Centre for Monitoring Indian Economy (CMIE)
products, and so on are capable of providing this information and
are easily accessible.

The 2011 census shows that only 4.6 per cent of
India's population has ownership of all four assets -
television, computer/laptop, scooter/car and
telephone/ mobile phone.
http://www.dailymail.co.uk/indiahome/indianews/article

WHAT INDIA OWNS
■ Bicycles: 44.8% ■ Car/jeep/van: 4.7%
■ Computer/laptop: 9.5%
■ Computer/laptop with internet: 3.1%
■ Computer/laptop without internet: 6.3%
■ Radio/transistor: 19.9%
■ Scooter/motorcycle/ moped: 21%
■ Telephone/mobile phone: 63.2%
■ Both telephone and mobile phone: 6%
■ Landline only: 4% ■ Mobile only: 53.2%
■ TV: 47.2%
■ TV, computer/laptop, telephone/mobile phone, scooter/car:
4.6%
■ None of the specified assets available: 17.8%

Limitations of Using
Secondary Data
 The disadvantages of using secondary data are related to the
fact that their selection and quality, and the methods of their
collection, are not under the control of the researcher and that
they are sometimes impossible to validate
 The researcher may try to use the secondary data that are
developed for some other purpose in some other time frame in
some other circumstances.
 This poses a great question mark on the currency and relevance
of the data in terms of its use in the current problem.
 Moreover, the secondary data become outdated quickly. It is a
big restriction on the frequent use of the secondary data.

Classification of Secondary Data Sources

Books, Periodicals, and Other
Published Material
 The books, periodicals, and other published material generally
available in most of the libraries are big sources of secondary
data.
 Now, most of the big libraries in our country are in the process
of digitizing the published material.
 Libraries also provide access to some good research journals of
the country.

Reports and Publication from
Government Sources
 Government sources also provide data. The accuracy and quality of these
data sources are unquestionable.
 Hence, most researchers rely on government sources of data to conduct
their research programme.
 Ministry of Statistics and Programme Implementation, Government of
India (http://mospi.gov.in).
 National Statistics Commission, the Central Statistical Organization.
 The Office of Registrar General and Census Commissioner, India (
http://www.censusindia.gov.in).
 Director General of Commercial Intelligence and Statistics, Ministry of
Commerce and Industry, Government of India (http://www.dgciskol.nic.in
).
 Reserve Bank of India (http://www.rbi.org.in)
 Planning Commission, Government of India(http://planningcommission
.gov.in)

Computerized Commercial and
Other Data Sources
 In India, there are various firms involved in selling data. For
example, indiastat.com and CMIE are two private firms involved
in the accumulation and selling of the data.

Home page of Prowess V. 3.1 (a product of the CMIE)

Media Resources
 Some relevant and authentic information can also be gathered from
the broadcast and print media. Apart from the academic
researchers, the print and electronic media frequently conduct
researches related to personal life, professional life, life style,
change in life style, income status, change in income status, and
many other issues.
 Leading news papers such as The Economic Times, Pioneer, The
Hindu, The Hindustan Times, The Indian Express, The Telegraph
(Kolkata), The Asian Age, The Hindu Business Line, Business
Standard, The Financial Express, and many more national and
regional newspapers have plentiful information.
 Apart from the daily newspapers, some magazines such as India
Today, Outlook, Business India, Business Today, Competition
Success Review, and so on provide a lot of information related to the
current issues.

Step 1: Identifying the Need of
Secondary Data for Research
 The secondary data sources help in developing a theoretical model, which
ultimately should be tested statistically.
 To develop a model, a researcher has to specify the relationship between
two or more variables and the secondary data support in specifying this
relationship.
 More sophisticated forecasting techniques use the secondary data to
forecast some research variables such as sales, profit, income, and so on.
 After identifying the need of the secondary data, the researcher has to
decide whether an internal or external secondary data source is to be
used.

Step 2: Utility of Internal Secondary
Data Sources for the Research Problem
 As a second step, a researcher has to examine the utility of
in-house secondary data in light of
 Objective
 Relevancy
 Accuracy
 Currency
 Authenticity
 Dependability
 action ability

Step 3: Utility of External Secondary
Data Sources for the Research Problem
 The external secondary data should also be tested for all the
parameters as it is done for the internal secondary data.
 In addition, the authenticity of the external secondary data
must also be tested, which was the matter of concern for the in-
house generated data.
 To address the issue of authenticity of the data, a researcher has
to determine “who” collected the data.
 Some research organizations, magazines, books, periodicals,
journals, and so on have got high reputation and credibility in
the society or concerned field. Government data sources are
also authentic.

Step 4: Use External Secondary
Data for the Research Problem
 After qualifying the first three stages, a researcher finds himself
or herself in a comfortable stage to use the data, as he or she is
sure that the data are useful for the research problem and there
is no harm in using it as it has already been tested for all the
discussed parameters.
 The final decision is a matter of the researcher’s discretion.
 In most of the cases, it is noted that the researchers commonly
use it to explore the problem and develop insights in to it.

What is a Questionnaire?
 A questionnaire consists of formalized and pre-specified set of
questions designed to obtain responses from potential
respondents.
 Questions in the questionnaire reflect the research objective
under investigation.
 Questionnaire design process requires a careful attention to
each step as the questionnaire or research instrument should be
adapted to the specific cultural environment and should not be
biased in terms of any one culture (Malhotra et al., 1996).

Questionnaire Objectives
• It must translate the information needed
into a set of specific questions that the
respondents can and will answer.
• A questionnaire must uplift, motivate, and
encourage the respondent to become
involved in the interview, to cooperate,
and to complete the interview.

Questionnaire Design Process
 Designing of the questionnaire is a systematic
process. This section explores the systematic process
of questionnaire design in three phases: pre-
construction phase, construction phase, and post-
construction phase.

Steps in questionnaire design process

Step 1: Decision Regarding Question
Format: Structured Questions Versus
Unstructured Questions
 Questionnaires use two types of question formats. These are
open-ended questions and closed-ended questions.
 The closed ended question format can be further divided into
dichotomous, multiple-choice questions, and scales. The
following sections focus on open-ended questions and closed-
ended questions.

Open-ended Questions
One of the major limitations is to handle the interviewer and the
interpretation bias.

Closed-ended Questions
 Closed-ended questions are structured questions.
 The choice offered to the respondents can be either in the form
of a rating system or a set of response alternatives.
 The closed-ended questionnaires are generally cheaper, more
reliable, and faster to code, and analyse the collected data
 The closed-ended question format can be further divided into
dichotomous, multiple-choice questions, and scales.

Dichotomous Questions
Dichotomous questions have only two response alternatives usually
presenting the two extremes “yes” or “no.” To make the alternatives
balanced, the researchers often present a third neutral alternative
“don’t know.”

Wording

Question Wordings Must Be Simple and
Easy to Understand

Vague or Ambiguous Words
Must Be Avoided
Some words such as “often,” “occasionally” and “usually,” “how
long,” “how much,” and “reasonably well” may be confusing for a
respondent because these words specify a specific time frame.

Double-Barrelled Questions
Must Be Avoided
Double-barrelled questions are those with wordings such as “and” or
“or.” In a double-barrelled question, a respondent may agree to one
part of the question but not to the other part.
√

Avoid Leading and Loaded Questions
A leading question is the one which clearly reveals the researcher’s
opinion about the answer to the question.
Identifying the loaded question bias in a question requires more
judgment because the wording elements in a loaded question allude
to the universal belief or rules of behaviour.

Avoid Using Overstated Words
The answer will always be overblown due to the first part of the
question, which generates a worry in the mind of the respondent
and results in a positive answer, which is not possible otherwise. A
more poised way of asking the same question is shown below.
√

Implied Assumptions Must Be Avoided
Above question has an implicit assumption that the discount policy
on bulk purchase offered by Company “A” is working excellent and
by answering “yes,” the company will continue its policy.
√

Respondent’s Memory Should
Not Be Overtaxed
√

Generalization and Estimation
Must Be Avoided
Generalization means respondent’s belief, “what must happen” or
“what should happen.”
√

Respondent’s Ability to Answer
Must Be Considered
A question targeted to officers older than 55 years to assess the
importance of Internet banking is as follows:
×
×
Targeting following question to young respondents may not be an
appropriate choice.

Step 3:Decision Regarding Question
Sequencing
 Question sequence also plays a key role in generating the
respondent’s interest and motivation to answer the question.
Questions should have a logical sequencing in the questionnaire
and should not be placed abruptly.
 To facilitate the responses, a researcher has to follow some
logical steps in sequencing the questions in the questionnaire.

Decision parameters regarding question sequence
Decision regarding question sequencing
Screening
questions
Opening
questions
Difficult to
answer
questions
Identification
and
categorizatio
n
questions
Logical
order
of
questionin
g

Opening Questions
 The opening questions should be simple, encouraging, and trust
building. From the research objective point of view, these
questions may sometimes be little irrelevant but should be good
initiators.
 These questions should not seek in-depth information and
should be as general as possible.
 For example, a microwave company, trying to assess “shift in
consumer attitude” from traditional way of cooking, should ask a
first opening question as follows:

Identification and Categorization
Questions
 Identification questions are used to generate some basic
identification information such as name, mailing address, office
phone number, personal phone number, or cell phone number.
 Categorization questions are mainly used to generate
demographic information.
 For example, researchers generally want to generate the
information related to age, experience, gender, and occupation
of the respondents.

Logical Order of Questioning
 In a questionnaire, the questions must flow in a logical
sequence. There are at least three approaches to suggest the
roadmap to place the questions in a logical sequence; they are
funnel technique, work technique, and sections technique.
 Funnel technique suggests asking general questions first and
then the specific questions.
 Work technique suggests that difficult-to-answer, sensitive, or
complicated questions should be placed later in the
questionnaire.
 The third technique is the section technique in which questions
are placed in different sections with respect to some common
base.

Response Choice
 It is important to understand that too many response choices
will burden the respondent and he or she will be perplexed
while answering.
 Few response choices will not be able to cover all ranges of
possible alternatives.
 As a general rule, the researchers present a question with five to
seven response alternatives.

Step 5: Decision Regarding
Questionnaire Layout
 Questionnaire layout is important to enhance the response rate.
A recent study revealed that a user-friendly format, and to
some extent colour, is valuable to increase mail survey response
rate.
 The appearance of a questionnaire is particularly important in
mail surveys because the instrument, along with the preliminary
letter and/ or cover letter, must sell itself and convince the
recipient to complete and return it.
 It has been observed that the respondent emphasizes the
questions that are placed at the top of the questionnaire
compared with that at the bottom.

Step 6: Producing First Draft of the
Questionnaire
 Printing on a poor, quality paper or an unprofessional look of
the questionnaire may generate a non-serious feeling among the
respondents.
 So, the questionnaire may be printed on a good, quality paper
and must have a professional look.
 The appearance of the front cover on a mail questionnaire and
the nature of first questions have been purported to have an
important influence on the respondent’s decision to complete
the questionnaire.

Phase III: Post-Construction Phase
 Phase III is the post-construction phase of the questionnaire
design process. It consists of four steps:
 Pre-testing of the questionnaire
 Revisiting the questionnaire based on the inputs obtained from
the pre-testing
 Revising final draft of the questionnaire
 Administering the questionnaire and obtaining responses.

Step 1. Specify The Information Needed
Step 2. Type of Interviewing Method
Step 3. Individual Question Content
Step 4. Overcome Inability and Unwillingness to Answer
Step 5. Choose Question Structure
Step 6. Choose Question Wording
Step 7. Determine the Order of Questions
Step 8. Form and Layout
Step 9. Reproduce the Questionnaire
Step 10. Pretest
Questionnaire Design Checklist

Step 1. Specify the Information Needed
1. Ensure that the information obtained fully addresses all the
components of the problem. Review components of the
problem and the approach, particularly the research questions,
hypotheses, and specification of information needed.
2. Have a clear idea of the target population.
Step 2. Type of Interviewing Method
1. Review the type of interviewing method determined based on
considerations discussed.

Step 3. Individual Question Content
1. Is the question necessary?
2. Are several questions needed instead of one to obtain the
required information in an unambiguous manner?
3. Do not use double-barreled questions.

Step 4. Overcoming Inability and Unwillingness to Answer
1. Is the respondent informed?
2. If respondents are not likely to be informed, filter questions that
measure familiarity, product use, and past experience should be
asked before questions about the topics themselves.
3. Can the respondent remember?
4. Questions which do not provide the respondent with cues can
underestimate the actual occurrence of an event.
5. Can the respondent articulate?

Step 4. Overcoming Inability and Unwillingness to Answer
7. Minimize the effort required of the respondents.
8. Is the context in which the questions are asked appropriate?
9. Make the request for information seem legitimate.
10. If the information is sensitive:
a. Place sensitive topics at the end of the questionnaire.
b. Preface the question with a statement that the behavior of
interest is common.
c. Ask the question using the third-person technique.
d. Hide the question in a group of other questions which
respondents are willing to answer.
e. Provide response categories rather than asking for specific
figures.
f. Use randomized techniques, if appropriate.

Step 5. Choosing Question Structure
1. Open-ended questions are useful in exploratory research and as
opening questions.
2. Use structured questions whenever possible.
3. In multiple-choice questions, the response alternatives should
include the set of all possible choices and should be mutually
exclusive.
4. In a dichotomous question, if a substantial proportion of the
respondents can be expected to be neutral, include a neutral
alternative.
5. Consider the use of the split ballot technique to reduce order bias in
dichotomous and multiple-choice questions.
6. If the response alternatives are numerous, consider using more than
one question to reduce the information processing demands on the
respondents.

Step 6. Choosing Question Wording
1. Define the issue in terms of who, what, when, where, why, and way
(the six Ws).
2. Use ordinary words. Words should match the vocabulary level of the
respondents.
3. Avoid ambiguous words: usually, normally, frequently, often,
regularly, occasionally, sometimes, etc.
4. Avoid leading questions that clue the respondent to what the answer
should be.
5. Avoid implicit alternatives that are not explicitly expressed in the
options.
6. Avoid implicit assumptions.
7. Respondent should not have to make generalizations or compute
estimates.
8. Use positive and negative statements.

Step 7. Determine the Order of Questions
1. The opening questions should be interesting, simple, and non-
threatening.
2. Qualifying questions should serve as the opening questions.
3. Basic information should be obtained first, followed by classification,
and, finally, identification information.
4. Difficult, sensitive, or complex questions should be placed late in the
sequence.
5. General questions should precede the specific questions.
6. Questions should be asked in a logical order.
7. Branching questions should be designed carefully to cover all
possible contingencies.
8. The question being branched should be placed as close as possible to
the question causing the branching, and (2) the branching questions
should be ordered so that the respondents cannot anticipate what
additional information will be required.

Step 8. Form and Layout
1. Divide a questionnaire into several
parts.
2. Questions in each part should be
numbered.
3. The questionnaire should be pre-coded.
4. The questionnaires themselves should
be numbered serially.

Step 9. Reproduction of the Questionnaire
1. The questionnaire should have a professional appearance.
2. Booklet format should be used for long questionnaires.
3. Each question should be reproduced on a single page (or double-
page spread).
4. Vertical response columns should be used.
5. Grids are useful when there are a number of related questions
which use the same set of response categories.
6. The tendency to crowd questions to make the questionnaire look
shorter should be avoided.
7. Directions or instructions for individual questions should be
placed as close to the questions as possible.

Step 10. Pretesting
1. Pretesting should be done always.
2. All aspects of the questionnaire should be tested, including question content,
wording, sequence, form and layout, question difficulty, and instructions.
3. The respondents in the pretest should be similar to those who will be
included in the actual survey.
4. Begin the pretest by using personal interviews.
5. Pretest should also be conducted by mail or telephone if those methods are to
be used in the actual survey.
6. A variety of interviewers should be used for pretests.
7. The pretest sample size is small, varying from 15 to 30 respondents for the
initial testing.
8. Use protocol analysis and debriefing to identify problems.
9. After each significant revision of the questionnaire, another pretest should be
conducted, using a different sample of respondents.
10. The responses obtained from the pretest should be coded and analyzed.

Learning Objectives
• To develop skill on how to design a sampling
process for a particular research

Sampling
A researcher generally takes a small portion of the
population for study, which is referred to as sample. The
process of selecting a sample from the population is
called sampling.
The results for the sample are then used to make
estimates of the larger group

SAMPLING
• Sample: Contacting a portion of the
population (e.g., 10% or 25%)
– best with a very large population (n)
– easiest with a homogeneous population
• Census: The entire population
– most useful is the population ("n") is small
– or the cost of making an error is high

Sample Vs. Census
Conditions Favoring the Use of
Type of Study Sample Census
1. Budget Small Large
2. Time available Short Long
3. Population size Large Small
4. Variance in the characteristic Small Large
5. Cost of sampling errors Low High
6. Cost of nonsampling errors High Low
7. Nature of measurement Destructive Nondestructive
8. Attention to individual cases Yes No

Why Sample?
Greater
accuracy
Availability of
elements
Availability of
elements
Greater speedGreater speed
Sampling
provides
Sampling
provides
Lower costLower cost

Characteristics of Good Samples
• Representative
• Accessible

• The total sample of
survey, conducted in all
70 constituencies and
210 polling stations,
was 4,459. In Wave I of
the survey in December
2014, the sample size
was 4,273.

The exit polls this time estimated
AAP’s seat tally from a high of 53
seats by India News- Axis to a low
of 31-39 at the bottom end by
CVoter. Others predicted seats in
the mid-range - 48 (News 24-
Chanakya), 43 (IT-Cicero), and 39
(ABP News-Nielsen).

…this (bad)…
Population
Sample

…or this (VERY bad)…
Population
Sample

Sampling Design Process
Define Target Population
Determine Sampling Frame
Determine Sampling Procedure
Probability Sampling
Type of Procedure
Simple Random Sampling
Stratified Sampling
Cluster Sampling
Non-Probability Sampling
Type of Procedure
Convenience
Judgmental
Quota
Determine Appropriate
Sample Size
Execute Sampling
Design

The Sampling Design Process
Step 1: Target population must be defined
 Target population is the collection of the objects which possess the
information required by the researcher and about which an
inference is to be made.
 It addresses the question “Ideally, who do you want to survey?”
– Age, gender, product use, those in industry
– Geographic area

The target population should be defined in terms of elements,
sampling units, extent, and time.
– An element is the object about which or from which the
information is desired, e.g., the respondent.
– A sampling unit is a unit containing the element, that is
available for selection at some stage of the sampling
process.
– Extent refers to the geographical boundaries.
– Time is the time period under consideration.

Activity
• Suppose we want to conduct a research on
which factors are responsible for shopping in a
particular departmental store in Ludhiana for
Jan to march, 2016, what will be our target
population and how we will define it?

Target Population for
Departmental Store Project
• Element - Male or Female head of household
responsible for most of shopping at
departmental store
• Sampling Unit: Household
• Extent: Ludhiana
• Time: Jan-Mar, 2016

Target Population for Election Poll
• Element:
• Sampling Unit:
• Extent:
• Time:

The Sampling Design Process
Step 2: Sampling frame must be determined
 A researcher takes a sample from a population list, directory, map,
city directory, or any other source used to represent the
population. This list possesses the information about the subjects
and is called the sampling frame.
 Sampling is carried out from the sampling frame and not from the
target population.

Sampling Frame for Departmental
Store
• Past Records and numbers of all person who
have shopped from the store

Sampling Frame for Election Polls
• ?????

The Sampling Design Process (Contd.)
Step 3: Appropriate sampling technique must be selected
Probability Sampling: Equal chance of being included in the sample (random)
Non-Probability Sampling: Unequal chance of being included in the sample (non-
random)
Step 4: Sample size must be determined
 Sample size refers to the number of elements to be included in the study.
Step 5: Sampling process must be executed

Learning Objective
Students will be able to
• decide on selection of a particular method in
sampling

Random Versus Non-random Sampling
 In random sampling, each unit of the population has the same
probability (chance) of being selected as part of the sample.
 In non-random sampling, members of the sample are not
selected by chance. Some other factors like familiarity of the
researcher with the subject, convenience, etc. are the basis of
selection

Random and non-random sampling methods

11-182
• Each possible sample of a given size (n) has a known
and equal probability of being the sample actually
selected.
• This implies that every element is selected
independently of every other element.

11-183
A Graphical Illustration of
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Select five random
numbers from 1 to 25.
The resulting sample
consists of population
elements 3, 7, 9, 16,
and 24. Note, there is
no element from Group
C.

16-185
Simple Random
Advantages
• Easy to implement with
RDD
Disadvantages
• Requires list of
population elements
• Time consuming
• Produces larger errors
• High cost

11-186
Systematic Sampling
• The sample is chosen by selecting a random starting point
and then picking every ith element in succession from the
sampling frame.
• Systematic sampling increases the representativeness of
the sample.

11-187
Systematic Sampling
• If the ordering of the elements produces a cyclical
pattern, systematic sampling may decrease the
representativeness of the sample.
For example, there are 100,000 elements in the
population and a sample of 1,000 is desired. In this case
the sampling interval, i, is 100. A random number
between 1 and 100 is selected. If, for example, this
number is 23, the sample consists of elements 23, 123,
223, 323, 423, 523, and so on.

11-188
Systematic Sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Select a random number
between 1 to 5, say 2.
consists of population 2,
(2+5=) 7, (2+5x2=) 12,
(2+5x3=)17, and (2+5x4=) 22.
Note, all the elements are
selected from a single row.

16-189
Systematic
Advantages
• Simple to design
• Easier than simple
random
• Easy to determine
sampling distribution of
mean or proportion
Disadvantages
• Periodicity within
population may skew
sample and results
• Trends in list may bias
results
• Moderate cost

Stratified Sampling
• A two-step process in which the population is partitioned
into subpopulations, or strata (e.g. race, age, gender etc.).
• Next, elements are selected from each stratum by a random
procedure, usually SRS. Each group is called stratum.
• The elements within a stratum should be as homogeneous as
possible, but the elements in different strata should be as
heterogeneous as possible.
• Every population element should be assigned to one and
only one stratum and no population elements should be
omitted.

11-193
Stratified Sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Randomly select a number
from 1 to 5
for each stratum, A to E. The
resulting
sample consists of
population elements
4, 7, 13, 19 and 21. Note, one
element
is selected from each
column.

16-194
Stratified
Advantages
• Control of sample size in
strata
• Increased statistical
efficiency
• Provides data to represent
and analyze subgroups
• Enables use of different
methods in strata
Disadvantages
• Increased error will result if
subgroups are selected at
different rates
• Especially expensive if
strata on population must
be created
• High cost

11-195
Cluster Sampling
 In cluster sampling, we divide the population into non-
overlapping areas or clusters.
 Elements within a cluster should be as heterogeneous as
possible, but clusters themselves should be as
homogeneous as possible. Ideally, each cluster should be a
small-scale representation of the population.
 Then a random sample of clusters is selected, based on a
probability sampling technique such as SRS.
 For each selected cluster, either all the elements are
included in the sample (one-stage) or a sample of elements
is drawn probabilistically (two-stage).

Cluster Random Sampling
1. Divide population into clusters
(usually along geographic boundaries)
2. Randomly sample clusters
3. Measure units within sampled clusters

11-198
Cluster Sampling (2-Stage)
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Randomly select 3 clusters,
B, D and E.
Within each cluster,
randomly select one
or two elements. The
resulting sample
consists of population
elements 7, 18, 20, 21, and
23. Note, no elements are
selected from clusters A and
C.

16-199
Cluster
Advantages
• Provides an unbiased
estimate of population
parameters if properly done
• Economically more efficient
than simple random
• Lowest cost per sample
• Easy to do without list
Disadvantages
• Often lower statistical
efficiency due to subgroups
being homogeneous rather
than heterogeneous
• Moderate cost

When to use stratified sampling?
• If primary research objective is to compare groups.
• Using stratified sampling may reduce sampling errors.
When to use cluster sampling?
• If there are substantial fixed costs associated with each
data collection location.
• When there is a list of clusters but not of individual
population members

16-201
Stratified and Cluster Sampling
Stratified
• Population divided into
few subgroups
• Homogeneity within
subgroups
• Heterogeneity between
subgroups
• Choice of elements
from within each
subgroup
Cluster
• Population divided into
many subgroups
• Heterogeneity within
subgroups
• Homogeneity between
subgroups
• Random choice of
subgroups

Multi-Stage Sampling
 As the name indicates, multistage sampling involves the selection
of units in more than one stage.
Multi-stage (four stages) sampling

16-203
Nonprobability Samples
Cost
FeasibilityFeasibility
TimeTime
IssuesIssues
No need to
generalize
Limited
objectives
Limited
objectives

11-204
Convenience Sampling
Convenience sampling attempts to obtain a sample of
convenient elements. Often, respondents are selected
because they happen to be in the right place at the right
time.
– use of students, and members of social organizations
– mall intercept interviews without qualifying the
respondents
– department stores using charge account lists
– “people on the street” interviews

11-205
A Graphical Illustration of Convenience
Sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Group D happens to
assemble at a
convenient time and
place. So all the
elements in this
Group are selected.
consists of elements
16, 17, 18, 19 and 20.
Note, no elements are
selected from group
A, B, C and E.

11-206
Judgmental Sampling
Judgmental sampling is a form of convenience
sampling in which the population elements are
selected based on the judgment of the researcher.
– test markets
– purchase engineers selected in industrial marketing
research
– expert witnesses used in court

11-207
Graphical Illustration of Judgmental
Sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
The researcher
considers groups B, C
and E to be typical and
convenient. Within each
of these groups one or
two elements are
selected based on
typicality and
convenience. The
resulting sample
consists of elements 8,
10, 11, 13, and 24. Note,
no elements are selected
from groups A and D.

11-208
Quota Sampling
Quota sampling may be viewed as two-stage restricted judgmental
sampling.
– The first stage consists of developing control categories, or quotas, of
population elements.
– In the second stage, sample elements are selected based on
convenience or judgment.
Population Sample
composition composition
Control
Characteristic Percentage Percentage Number
Sex
Male 48 48 480
Female 52 52 520
____ ____ ____
100 100 1000

11-209
Quota Sampling
In quota sampling, certain subclasses, such as age,
gender, income group, and education level are used as
strata. Stratified random sampling is based on the
concept of randomly selecting units from the stratum.
However, in case of quota sampling, a researcher uses
non-random sampling methods to gather data from
one stratum until the required quota fixed by the
researcher is fulfilled.

11-210
Quota Sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
A quota of one
element from each
group, A to E, is
imposed. Within each
group, one element is
selected based on
judgment or
convenience. The
resulting sample
consists of elements
3, 6, 13, 20 and 22.
Note, one element is
selected from each
column or group.

11-211
Snowball Sampling
In snowball sampling, an initial group of respondents is
selected, usually at random.
– After being interviewed, these respondents are asked
to identify others who belong to the target population
of interest.
– Subsequent respondents are selected based on the
referrals.

11-212
Snowball Sampling
A B C D E
1 6 11 16 21
2 7 12 17 22
3 8 13 18 23
4 9 14 19 24
5 10 15 20 25
Elements 2 and 9 are selected
randomly from groups A and
B. Element 2 refers elements
12 and 13. Element 9 refers
element 18. The resulting
sample consists of elements
2, 9, 12, 13, and 18. Note,
there are no element from
group E.
Random Selection
Referrals

11-214
Technique Strengths Weaknesses
Nonprobability Sampling
Convenience sampling
Least expensive, least
time-consuming, most
convenient
Selection bias, sample not
representative, not recommended for
descriptive or causal research
Judgmental sampling Low cost, convenient,
not time-consuming
Does not allow generalization,
subjective
Quota sampling Sample can be controlled
for certain characteristics
Selection bias, no assurance of
representativeness
Snowball sampling Can estimate rare
characteristics
Time-consuming
Probability sampling
Simple random sampling
(SRS)
Easily understood,
results projectable
Difficult to construct sampling
frame, expensive, lower precision,
no assurance of representativeness.
Systematic sampling Can increase
representativeness,
easier to implement than
SRS, sampling frame not
necessary
Can decrease representativeness
Stratified sampling Include all important
subpopulations,
precision
Difficult to select relevant
stratification variables, not feasible to
stratify on many variables, expensive
Cluster sampling Easy to implement, cost
effective
Imprecise, difficult to compute and
interpret results
Strengths and Weaknesses of
Basic Sampling Techniques

11-215
Choosing Nonprobability Vs.
Probability Sampling
Conditions Favoring the Use of
Factors Nonprobability
sampling
Probability
sampling
Nature of research Exploratory Conclusive
Relative magnitude of sampling
and nonsampling errors
Nonsampling
errors are
larger
Sampling
errors are
larger
Variability in the population Homogeneous
(low )
Heterogeneou
s (high)
Statistical considerations Unfavorable Favorable
Operational considerations Favorable Unfavorable

Sampling and Non-Sampling Errors
Sampling Error
Sampling error occurs when the sample is not a true representative
of the population. In complete enumeration, sampling errors
are not present.
It is the variation between the true mean value for the population
and the true mean value for the original sample.

.
Sampling errors can occur due to some specific reasons:
 Faulty selection of the sample.
 Sometimes due to the difficulty in selection a particular sampling
unit, researchers try to substitute that sampling unit with
another sampling unit which is easy to be surveyed.
 Sometimes researchers demarcate sampling units wrongly and
hence, provide scope for committing sampling errors.

Sampling and Non-sampling Errors
(Contd.)
Non-Sampling Errors
All errors other than sampling can be included in the category of
non-sampling errors including errors in problem definition,
approach, scales, questionnaire design, interviewing methods,
and data preparation and analysis.
Non-sampling errors consist of non-response errors and response
errors.

The following are some common non-sampling
errors:
 Faulty designing and planning of survey
 Response errors
 Non-response bias
 Errors in coverage
 Compiling error and publication error

Learning Objectives
• To develop insight on primary data Collection
& Survey Methods

Survey Method of Data Collection
 Survey means gathering information through respondents for any
pre-established research objective.
 In most of the scientific research, this information is obtained from a
representative sample of the population.

Advantages of Survey Methods
 Opportunity to the researcher to collect data at
one time.
 Ability to generate some standardized information
as the same questionnaire is administered to
different respondents more often on same time.
 Suitability for data coding, tabulation, analysis, and
interpretation.
 Ease in administering the questionnaire.

Disadvantages of Survey Methods
 Handling the unwillingness of the respondents to provide
responses.
 Individual characteristics of the interviewer or the way of
presentation of the questionnaire or the way of asking
questions makes a big difference in getting the responses.

• In spite of these limitations, this is a widely
used technique of data generation in the field
of business research.

A Classification of Survey Methods

1. Personal Interview
 Naturally, there may be different ways of contacting the subjects
(respondents).
 These ways can be classified on the basis of the respondents to
be contacted and the means to contact them.

Advantages and disadvantages of different personal interview techniques

2. Telephone Interview
 As the number of telephones increase, it might be expected that
the telephone interviews would assume greater role as an
approach to data collection
 Few researchers argue that the procedure of data collection
through telephone is both reliable and valid compared with the
collection through mail questionnaire.
 Telephone interviewing technique can be classified into four
categories: personal interview using telephone, fax survey,
voice mail survey, and computer-assisted telephone
interviewing (CATI).

Advantages and disadvantages of the different
telephone interview techniques

3. Mail Interview
 In a mail survey, the questionnaire is sent to the respondent through
mail and the respondent returns the filled questionnaire (providing his
opinion about the questions).
 In the mailing survey technique, the rate of return is a matter of
concern.
 Some researchers favour providing some incentives to the
respondents. Others believe that the incentive type has no impact on
the return of the survey
 Mail surveys generally provide accurate results because the respondent
has enough time to think and respond.
 Bias due to interviewer can also be controlled.
 Able to cover an extensive geographic area as compared with the
personal interview technique.
 Return time is not guaranteed.
 It eliminates the possibility of explanation of difficult-to-understand
question by the interviewer.

3(a) One-Time Mail Survey
3(b) Mail Panel
 In some cases, when the interviewer wants only onetime
response from the respondent and continuous information
gathering is not desired, one-time mail survey is used.
 Reduced cost as compared with the personal interview is one
major advantage of this type of survey. Non-response is a major
disadvantage.
 Mail panel is a group of respondents who have agreed to
participate in the survey conducted by the research agencies
related to some business issues. The researchers create the
mail panel to generate continuous responses on certain
research issues related to the business research.

Advantages and disadvantages of different mail interview techniques

4. Electronic Interview
 There seems to be a consensus that the electronic surveys in
general are less expensive than the traditional mail surveys
because they do not involve printing, folding, envelope
stuffing, and mailing cost.
 In addition, non-involvement of the interviewer eliminates the
possibility of bias due to the interviewer. The obtained input
data are also of superior quality in this technique. Electronic
surveys are also excellent facilitators in launching international
and cross-cultural research programmes.
 Electronic interview techniques are basically of two types: e-mail
interview and web-based interview.
http://cricket.yahoo.com/news/yahoo--cricket--ipl-public-perception-survey.ht
http://timesofindia.indiatimes.com/

Advantages and disadvantages of different electronic interview techniques

Evaluation Criteria for Survey Methods

Comparative evaluation of various survey methods on
different evaluation parameters

Criteria 1 2 3 4
Versatility
Number of Questions Personal Mail Web Telephone
Amount/ Variety of Question Personal Telephone Web Mail
Presentation of Stimuli Personal Web Telephone Mail
Time Web Telephone Personal Mail
Cost Web Mail Telephone Personal
Accuracy
Samling Control Personal Telephone Mail Web
Supervisory Control Web Mail Telephone Web
Opportunity for Clarification Personal Telephone Web Mail
Respondent Convenience Web Mail Telephone Personal

Learning Objective
• To develop insight on different methods for
data collection in observation with the
advantages and disadvantages

Observation Techniques
 Observation techniques involve watching and recording the
behaviour of test subjects or test objects in a systematic
manner without interacting with them.
 Compared with the emphasis on the survey techniques within
the marketing discipline, attention to observational data
collection methods is relatively rare.
 Observation research can be broadly classified as direct versus
indirect observation; structured versus unstructured
observation; disguised versus undisguised observation; and
human versus mechanical observation.

Direct versus Indirect Observation
 In direct observation, the
researchers directly observe the
behavior of a subject and
record it. E.g:- Observe
customers in a store and count
how many bags of candy they
purchase.
 In indirect observation, the
researcher observes outcome
of a behavior rather than
observing the behavior. E.g:-
look through trash cans on
garbage day to see how many
empty candy bags are in each
trash bin

Structured versus Unstructured
Observation
 For structured observation, the researcher specifies in detail
what is to be observed and how the measurements are to be
recorded, e.g., an auditor performing inventory analysis in a
store
 In unstructured observation, the observer monitors all aspects
of the phenomenon that seem relevant to the problem at hand,
e.g., observing children playing with new toys.

Disguised versus Undisguised
Observation
 In disguised observation, the subject happens to be unaware
that his or her behaviour or action is being monitored by the
observer. Disguise may be accomplished by using one-way
mirrors, hidden cameras, or inconspicuous mechanical devices.
Observers may be disguised as shoppers or sales clerks.
 In undisguised observation, the subject happens to be aware
that he or she is being observed by an observer.

Human versus Mechanical Observation
 Human observational techniques involve observation of the test
subjects or test object by a human being, generally an observer
appointed by a researcher.
 Mechanical observation techniques involve observation by a
non-human device.

Classification of Observation Methods
Observation methods can be broadly classified into five categories.
These are personal observation, mechanical observation, audits,
content analysis, and physical trace analysis

Personal Observation
As the name indicates, in personal
observation, an observer actually
watches the subject behaviour and
makes a record of it.
•The observer does not attempt to
manipulate the phenomenon being
observed but merely records what takes
place.
•For example, a researcher might record
traffic counts and observe traffic flows in
a department store.

Mechanical Observation
• Mechanical observation involves the
observation of behaviour of the respondents
through a mechanical device.

Photoemission Electron Microscopy

Mechanical Observation
Do not require respondents' direct participation.
– Turnstiles that record the number of people entering or
leaving a building.
– On-site cameras (still, motion picture, or video)
– Optical scanners in supermarkets
Do require respondent involvement.
– Eye-tracking monitors
– Voice pitch analyzers
– Devices measuring response latency

Audits
• Audit involves examination of particular
records or inventory analysis of the items
under investigation.
• In audit analysis, the researchers personally
collect the data and usually make the count of
the items under investigation.

Content Analysis
• Content analysis is a research technique used to
objectively and systematically make inferences
about the intentions, attitudes, and values of
individuals by identifying specified characteristics in
textual messages.

• The unit of analysis may be words, characters
(individuals or objects), themes (propositions),
space and time measures (length or duration
of the message), or topics (subject of the
message).
• Analytical categories for classifying the units
are developed and the communication is
broken down according to prescribed rules.

• For example:- A study of Social Media
Marketing by Pharmaceutical Industry
• As per review, the following factors were
found which were very significant for Pharma
Companies:
Brand Development, Corporate Social
Responsibility, Employer Branding, Empathy,
Engagement with People, Health awareness,
Company development, Employee
recognition, Awareness about future insights.

Physical Trace Analysis
• Physical trace analysis involves collection of
data through physical trace of the subjects in
terms of understanding their past behavior.

Observation Methods
Trace Analysis
Data collection is based on physical traces, or evidence,
of past behavior.
 The number of different fingerprints on a page was used to gauge the
readership of various advertisements in a magazine.
 The age and condition of cars in a parking lot were used to assess the
affluence of customers.
 Internet visitors leave traces which can be analyzed to examine
browsing and usage behavior by using cookies.

A Comparative Evaluation of Observation Methods
Criteria Personal Mechanical Audit Content Trace
Observation Observation Analysis Analysis Analysis
Degree of structure Low Low to high High High Medium
Degree of disguise Medium Low to high Low High High
Ability to observe High Low to high High Medium Low
in natural setting
Observation bias High Low Low Medium Medium
Analysis Bias High Low to Low Low Medium
Medium
General remarks Most Can be Expensive Limited to Method of
flexible intrusive commu- last resort
nications

Advantages of Observation Techniques
 Collection of data on the basis of actually observed information
rather than on the basis of using a measurement scale.
 Eliminates recall error
 Completely free from this bias of personal interview technique
as there is no interaction between the observer and the subject
who is being observed.
 Observations also allow an observer to collect data from the
group of subjects who are not able to provide written or verbal
information.

Limitations of Observation Techniques
 Inability to measure attitude or intentions of the subjects.
 Subjective observation by the observer.
 Require a lot of time and energy to be executed.
 Disguised observation is sometimes unethical

As per Naresh Malhotra
• From a practical standpoint, it is best to view
the observation method as a complement to
survey methods, rather than to view it as a
competitor

Data Preparation
 There exist two stages between data collection and
interpretation: data preparation and data analysis.
 Data preparation secures the first place in these two stages.
Data collected by the researchers from the field happens to be
in raw format. Before going for analysis, the researcher has to
convert raw data into the data format that is ready for data
analysis.

 Descriptive Data analysis is used to describe the data
 Inferential Data analysis is based on some sophisticated
statistical analysis to estimate the population parameter from
sample statistics.

1. Preliminary Questionnaire Screening
 Although preliminary questionnaire screening takes place during
the fieldwork, it is important to re-check the questionnaire.
 There is a possibility that few pages of the questionnaire may
be missing.
 Another possibility occurs in terms of irrational consistency in
filling the answer on a rating scale.
 If there is a continuous skipping of some questions or if there is
un-rationale selection of rating point as the answer to some
questions, this is an indication of lack of understanding of the
respondent.

2. Editing
 Editing is actually checking of the questionnaire for suspicious,
inconsistent, illegible, and incomplete answers visible from
careful study of the questionnaire.
 This type of incompleteness in the answer can be logically
detected and settled down.

3. Coding
 Before performing statistical analysis, a researcher has to
prepare data for analysis. This preparation is done by data
coding. Coding of data is probably the most crucial step in the
analytical process
 In coding, each answer is identified and classified with a
numerical score or other symbolic characteristics for processing
the data in computers.
 A codebook contains instructions for coding and information of
the variables taken for the study. It also contains variable
location in the data set. Even if the questionnaire is precoded,
coding helps researchers in identifying and locating the variables
easily.

14-277
Coding Questionnaires
• The respondent code and the record number appear on
each record in the data.
• The first record contains the additional codes: project
code, interviewer code, date and time codes, and
validation code.
• It is a good practice to insert blanks between parts.

4. Data Entry
 At this stage, the data are entered in the spreadsheet. This is a
crucial stage and is usually done by the computer typist. A
careful supervision of the data entry is essentially required by
the researcher.
 Data-cleaning exercise is undertaken by any researcher to deal
with the problem of missing data and illogical or inconsistent
entries.

Data Cleaning
 Data cleaning involves two stages: handling missing data and
checking data for illogical or inconsistent entries.
 Following are some of the guidelines to deal with such kind of
missing data.
 Leaving the missing data and performing the analysis
 Substituting a mean value
 Case-wise deletion

5. Data Analysis
 Data analysis exercise cannot be launched independently
ignoring the previous steps of the research to deal with the
problem.
 By and large statistical techniques for analysis can be placed in
two categories: univariate and multivariate.
 Univariate statistical techniques are used only when one
measurement of each element in sample is taken or multiple
measurement of each element are taken but each variable is
analyzed independently.
 Multivariate statistical techniques are collection of procedure
for analyzing the association between two or more set of
measurement that were made on each object in one or more
samples of objects.

Example of Multivariate Analyse
• Evaluating the likelihood of domestic violence taking
into account age of the individuals, whether or not
they consume alcohol, ethnic background and level
of education.

 When the data are nominal or ordinal, non-parametric statistical tests are
used for data analyses, whereas when they are interval or ratio parametric,
parametric tests are used.
 Parametric test are statistical techniques used to test a hypothesis
based on some restrictive assumption about the population, where as
non parametric tests are not dependent on restrictive normality
assumption of the population.

Classification of Univariate statistical techniques

Three Judgment and Classification parameters for Multivariate Analysis
 Dependence of Variables
 Number of Variables treated as dependent in single analysis
 Data type :- Metric or Non Metric Data

Classification of multivariate statistical techniques

Research Proposal
• A written proposal is often required and is
desirable for establishing agreement on a
number of issues
• A research proposal may also be oral. This is
more likely when a manager directs his or her
own research.

The Research Proposal
4-288
Delivery
Legally-binding
contract
Legally-binding
contract
ObligationsObligations
Written
proposals
establish
Written
proposals
establish
Methods
TimingTiming
BudgetsBudgets
ExtentExtentPurposePurpose

Research Methodology: Questionnaire, Sampling, Data Preparation

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