Business Research Methods Unit 3 notes

Notes by Prof. Sujeet Tambe
Notes by Prof. Sujeet Tambe Page 1
BRM
UNIT IV
MESUREMENT AND DATA
1.1 MEASUREMENT
 Measurement is the foundation of any scientific investigation
 Everything we do begins with the measurement of whatever it is we want to study
 Definition: measurement is the assignment of numbers to objects
What is Measurement? The measurement is the process of assigning numbers or symbol to the
characteristics of the object as per the specified rules. Here, the researcher assigns numbers, not
to the object, but to its characteristics such as perceptions, attitudes, preferences, and other
relevant traits.
For example, consider a scale from 1 to 10 for locating consumer characteristics (preference
for the product). Each respondent is assigned a number from 1 to 10 denoting the degree of
unfavorableness for the product, with ‘1’ indicating extremely unfavorable and ’10’ indicating
extremely favorable. Here, the measurement is the process of assigning the actual number from
1 to 10 to each respondent while the scaling is a process of placing respondents on a continuum
with respect to their preference for the product.
In research, usually, the numbers are assigned to the qualitative traits of the object because the
quantitative data helps in statistical analysis of the resulting data and further facilitates the
communication of measurement rules and results.
IMPORTANCE OF MEASUREMENT
Measure is important in research. Measure aims to ascertain the dimension, quantity, or
capacity of the behaviors or events that researchers want to explore. According to Maxim
(1999), measurement is a process of mapping empirical phenomena with using system of
numbers.
Basically, the events or phenomena that researchers interested can be existed as domain.
Measurement links the events in domain to events in another space which called range. In
another words, researchers can measure certain events in certain range. The range is consisting

of scale. Thus, researchers can interpret the data with quantitative conclusion which leads to
more accurate and standardized outcomes. Without measure, researchers can’t interpret the data
accurately and systematically.
Quantitative Measurement is a quantitative description of the events or characteristics which
involves numerical measurement. For example, the description made as “There are three birds
in the nest”. This description includes the numerical measurement on the birds. Quantitative
measurement enables researchers to make comparison between the events or characteristics. For
example, researchers tend to know who the tallest person in a family is. So, they use centimeter
to measure their height and make comparison between all the family members.
PROBLEMS IN MEASUREMENT IN MANAGEMENT RESEARCH
A. RELIABILITY
Reliability refers to the consistency of a measure. Psychologists consider three types of
consistency: over time (test-retest reliability), across items (internal consistency), and across
different researchers (inter-rater reliability).
1. Test-Retest Reliability
When researchers measure a construct that they assume to be consistent across time, then the
scores they obtain should also be consistent across time. Test-retest reliability is the extent to
which this is actually the case. For example, intelligence is generally thought to be consistent
across time. A person who is highly intelligent today will be highly intelligent next week. This
means that any good measure of intelligence should produce roughly the same scores for this
individual next week as it does today. Clearly, a measure that produces highly inconsistent
scores over time cannot be a very good measure of a construct that is supposed to be consistent.
Again, high test-retest correlations make sense when the construct being measured is assumed
to be consistent over time, which is the case for intelligence, self-esteem, and the Big Five
personality dimensions. But other constructs are not assumed to be stable over time. The very
nature of mood, for example, is that it changes. So a measure of mood that produced a low test-
retest correlation over a period of a month would not be a cause for concern.

2. Internal Consistency
A second kind of reliability is internal consistency, which is the consistency of people’s
responses across the items on a multiple-item measure. In general, all the items on such
measures are supposed to reflect the same underlying construct, so people’s scores on those
items should be correlated with each other. On the Rosenberg Self-Esteem Scale, people who
agree that they are a person of worth should tend to agree that that they have a number of good
qualities. If people’s responses to the different items are not correlated with each other, then it
would no longer make sense to claim that they are all measuring the same underlying construct.
This is as true for behavioural and physiological measures as for self-report measures. For
example, people might make a series of bets in a simulated game of roulette as a measure of
their level of risk seeking. This measure would be internally consistent to the extent that
individual participants’ bets were consistently high or low across trials.
Perhaps the most common measure of internal consistency used by researchers in psychology is
a statistic called Cronbach’s α (the Greek letter alpha). Conceptually, α is the mean of all
possible split-half correlations for a set of items. For example, there are 252 ways to split a set
of 10 items into two sets of five. Cronbach’s α would be the mean of the 252 split-half
correlations. Note that this is not how α is actually computed, but it is a correct way of
interpreting the meaning of this statistic. Again, a value of +.80 or greater is generally taken to
indicate good internal consistency.
3. Interrater Reliability
Many behavioural measures involve significant judgment on the part of an observer or a
rater. Inter-rater reliability is the extent to which different observers are consistent in their
judgments. For example, if you were interested in measuring university students’ social skills,
you could make video recordings of them as they interacted with another student whom they are
meeting for the first time. Then you could have two or more observers watch the videos and rate
each student’s level of social skills. To the extent that each participant does in fact have some
level of social skills that can be detected by an attentive observer, different observers’ ratings
should be highly correlated with each other. Inter-rater reliability would also have been
measured in Bandura’s Bobo doll study. In this case, the observers’ ratings of how many acts of
aggression a particular child committed while playing with the Bobo doll should have been
highly positively correlated. Interrater reliability is often assessed using Cronbach’s α when the

judgments are quantitative or an analogous statistic called Cohen’s κ (the Greek letter kappa)
when they are categorical.
B. Validity
Validity is the extent to which the scores from a measure represent the variable they are
intended to. But how do researchers make this judgment? We have already considered one
factor that they take into account—reliability. When a measure has good test-retest reliability
and internal consistency, researchers should be more confident that the scores represent what
they are supposed to. There has to be more to it, however, because a measure can be extremely
reliable but have no validity whatsoever. As an absurd example, imagine someone who believes
that people’s index finger length reflects their self-esteem and therefore tries to measure self-
esteem by holding a ruler up to people’s index fingers. Although this measure would have
extremely good test-retest reliability, it would have absolutely no validity. The fact that one
person’s index finger is a centimetre longer than another’s would indicate nothing about which
one had higher self-esteem.
Discussions of validity usually divide it into several distinct “types.” But a good way to
interpret these types is that they are other kinds of evidence—in addition to reliability—that
should be taken into account when judging the validity of a measure. Here we consider three
basic kinds: face validity, content validity, and criterion validity.
1. Face Validity
Face validity is the extent to which a measurement method appears “on its face” to measure the
construct of interest. Most people would expect a self-esteem questionnaire to include items
about whether they see themselves as a person of worth and whether they think they have good
qualities. So a questionnaire that included these kinds of items would have good face validity.
The finger-length method of measuring self-esteem, on the other hand, seems to have nothing to
do with self-esteem and therefore has poor face validity. Although face validity can be assessed
quantitatively—for example, by having a large sample of people rate a measure in terms of
whether it appears to measure what it is intended to—it is usually assessed informally.
Face validity is at best a very weak kind of evidence that a measurement method is measuring
what it is supposed to. One reason is that it is based on people’s intuitions about human
behaviour, which are frequently wrong. It is also the case that many established measures in
psychology work quite well despite lacking face validity. The Minnesota Multiphasic

Personality Inventory-2 (MMPI-2) measures many personality characteristics and disorders by
having people decide whether each of over 567 different statements applies to them—where
many of the statements do not have any obvious relationship to the construct that they measure.
For example, the items “I enjoy detective or mystery stories” and “The sight of blood doesn’t
frighten me or make me sick” both measure the suppression of aggression. In this case, it is not
the participants’ literal answers to these questions that are of interest, but rather whether the
pattern of the participants’ responses to a series of questions matches those of individuals who
tend to suppress their aggression.
2. Content Validity
Content validity is the extent to which a measure “covers” the construct of interest. For
example, if a researcher conceptually defines test anxiety as involving both sympathetic nervous
system activation (leading to nervous feelings) and negative thoughts, then his measure of test
anxiety should include items about both nervous feelings and negative thoughts. Or consider
that attitudes are usually defined as involving thoughts, feelings, and actions toward something.
By this conceptual definition, a person has a positive attitude toward exercise to the extent that
he or she thinks positive thoughts about exercising, feels good about exercising, and actually
exercises. So to have good content validity, a measure of people’s attitudes toward exercise
would have to reflect all three of these aspects. Like face validity, content validity is not usually
assessed quantitatively. Instead, it is assessed by carefully checking the measurement method
against the conceptual definition of the construct.
3. Criterion Validity
Criterion validity is the extent to which people’s scores on a measure are correlated with other
variables (known as criteria) that one would expect them to be correlated with. For example,
people’s scores on a new measure of test anxiety should be negatively correlated with their
performance on an important school exam. If it were found that people’s scores were in fact
negatively correlated with their exam performance, then this would be a piece of evidence that
these scores really represent people’s test anxiety. But if it were found that people scored
equally well on the exam regardless of their test anxiety scores, then this would cast doubt on
the validity of the measure.
A criterion can be any variable that one has reason to think should be correlated with the
construct being measured, and there will usually be many of them. For example, one would
expect test anxiety scores to be negatively correlated with exam performance and course grades

and positively correlated with general anxiety and with blood pressure during an exam. Or
imagine that a researcher develops a new measure of physical risk taking. People’s scores on
this measure should be correlated with their participation in “extreme” activities such as
snowboarding and rock climbing, the number of speeding tickets they have received, and even
the number of broken bones they have had over the years. When the criterion is measured at the
same time as the construct, criterion validity is referred to as concurrent validity; however,
when the criterion is measured at some point in the future (after the construct has been
measured), it is referred to as predictive validity (because scores on the measure have
“predicted” a future outcome).
Criteria can also include other measures of the same construct. For example, one would expect
new measures of test anxiety or physical risk taking to be positively correlated with existing
measures of the same constructs. This is known as convergent validity.
4. Discriminant Validity
Discriminant validity, on the other hand, is the extent to which scores on a measure
are notcorrelated with measures of variables that are conceptually distinct. For example, self-
esteem is a general attitude toward the self that is fairly stable over time. It is not the same as
mood, which is how good or bad one happens to be feeling right now. So people’s scores on a
new measure of self-esteem should not be very highly correlated with their moods. If the new
measure of self-esteem were highly correlated with a measure of mood, it could be argued that
the new measure is not really measuring self-esteem; it is measuring mood instead.
When they created the Need for Cognition Scale, Cacioppo and Petty also provided evidence of
discriminant validity by showing that people’s scores were not correlated with certain other
variables. For example, they found only a weak correlation between people’s need for cognition
and a measure of their cognitive style—the extent to which they tend to think analytically by
breaking ideas into smaller parts or holistically in terms of “the big picture.” They also found no
correlation between people’s need for cognition and measures of their test anxiety and their
tendency to respond in socially desirable ways. All these low correlations provide evidence that
the measure is reflecting a conceptually distinct construct.

LEVELS OF MEASUREMENT :
They are
 Nominal–Latin for name only (Republican, Democrat, Green, Libertarian)
 Ordinal–Think ordered levels or ranks (small–8oz, medium–12oz, large–32oz)
 Interval–Equal intervals among levels (1 dollar to 2 dollars is the same interval as 88 dollars to
89 dollars)
 Ratio–Let the “o” in ratio remind you of a zero in the scale (Day 0, day 1, day 2, day 3, …)
 NOMINAL LEVEL
The first level of measurement is nominal level of measurement. In this level of
measurement, the numbers in the variable are used only to classify the data. In this level of
measurement, words, letters, and alpha-numeric symbols can be used. Suppose there are data
about people belonging to three different gender categories. In this case, the person belonging to
the female gender could be classified as F, the person belonging to the male gender could be
classified as M, and transgendered classified as T. This type of assigning classification is
nominal level of measurement.
 ORDINAL LEVEL
The second level of measurement is the ordinal level of measurement. This level of
measurement depicts some ordered relationship among the variable’s observations. Suppose a
student scores the highest grade of 100 in the class. In this case, he would be assigned the first
rank. Then, another classmate scores the second highest grade of an 92; she would be assigned
the second rank. A third student scores a 81 and he would be assigned the third rank, and so on.
The ordinal level of measurement indicates an ordering of the measurements.
 INTERVAL LEVEL
The third level of measurement is the interval level of measurement. The interval level of
measurement not only classifies and orders the measurements, but it also specifies that the
distances between each interval on the scale are equivalent along the scale from low interval to
high interval. For example, an interval level of measurement could be the measurement of
anxiety in a student between the score of 10 and 11, this interval is the same as that of a student
who scores between 40 and 41. A popular example of this level of measurement is temperature
in centigrade, where, for example, the distance between 940
C and 960
C is the same as the
distance between 1000
C and 1020
C.

 RATIO LEVEL
The fourth level of measurement is the ratio level of measurement. In this level of
measurement, the observations, in addition to having equal intervals, can have a value of zero as
well. The zero in the scale makes this type of measurement unlike the other types of
measurement, although the properties are similar to that of the interval level of measurement.
In the ratio level of measurement, the divisions between the points on the scale have an
equivalent distance between them.

3.2 CONCEPT OF SCALE
Scaling is the procedure of measuring and assigning the objects to the numbers according to the
specified rules. In other words, the process of locating the measured objects on the continuum, a
continuous sequence of numbers to which the objects are assigned is called as scaling.Scaling is
considered as the extension of measurement. All the scales used in scaling techniques can be
explained in terms of four basic characteristics., Viz. Description, Order, Distance, and
origin. These characteristics collectively define the Levels of Measurement of scale. The level
of measurement indicates that what properties of an object are measured or not measured by the
scale.
RatingScale
The Itemized Rating Scale is an Ordinal Scale that has a brief description or numbers associated
with each category, ordered in terms of scale positions. The respondents are asked to select the
category that best describes the stimulus object being rated.
Rating scales are
1. LIKERT SCALE
2. SEMANTIC DIFFERENTIAL SCALE
3. CONSTANT SUM SCALE
4. GRAPHIC RATING SCALE
1. LIKERTSCALE
Definition: A Likert Scale is a scale used to measure the attitude wherein the respondents are
asked to indicate the level of agreement or disagreement with the statements related to the
stimulus objects.
The Likert Scale was named after its developer, Rensis Likert. It is typically a five response
category scale ranging from “strongly disagree” to “strongly agree”. The purpose of a Likert
scale is to identify the attitude of people towards the given stimulus objects by asking them the
extent to which they agree or disagree with them.
Often, the respondents are presented with questionnaires containing the set of statements to rate
their attitude towards the objects. For example, the respondents might be asked to rate their
purchase experience with shoppers stop by assigning the score as (1= strongly disagree, 2=

disagree, 3= neither agree nor disagree, 4= agree, 5= strongly agree) to the series of
statements given below:
 Shoppers stop sell high-quality merchandise.
 I like to shop from shoppers stop.
 It offers several credit schemes.
 It charges fair prices.
 I like the way shoppers stop advertises its products.
The data obtained from the Likert Scale are typically treated as the interval. Thus, we can say
that Likert scale possesses description, order and distance characteristics. Description means
the unique labels or tags designated to each value of the scale; Order means the relative
position of the descriptor and Distance implies that the absolute differences between the
descriptors is known and can be expressed in units.
For the purpose of analysis, each statement is allotted a numerical score ranging from either 1 to
5 or -2 to +2. The analysis could be done item wise, or a total score can be computed by
summing up all the items for each respondent. One of the advantages of a Likert scale is that it
is easy to construct and administer.
The major limitation of this scaling technique is that it is time-consuming and requires much
more time as compared to other itemized scaling techniques. This is because each respondent is
required to read every statement given in a questionnaire before assigning a numerical value to
it. Another limitation of a Likert scale is that it could be misunderstood at times, especially
when the responses are unfavorable.
2. SEMANTICDIFFERENTIALSCALE
Definition: The Semantic Differential Scale is a seven-point rating scale used to derive the
respondent’s attitude towards the given object or event by asking him to select an appropriate
position on a scale between two bipolar adjectives (such
as “warm” or “cold”, “powerful” or “weak”, etc.)

For example, the respondent might be asked to rate the following five attributes of shoppers
stop by choosing a position on a scale between the adjectives that best describe what really the
shoppers stop means to him.
The respondent will place a mark anywhere between the two extreme adjectives, representing
his attitude towards the object. Such as, in the above example, the shoppers stop is evaluated as
organized, cold, modern, reliable and simple.
Sometimes the negative adjectives are placed on the right and sometimes on the left side of a
scale. This is done to control the tendency of the respondents, especially those with either very
positive or negative attitudes, to mark the right or left-hand sides of a scale without reading the
labels.
The items on a semantic differential scale can be scored on either a numerical range of -3 to +3
or 1 to 7. The data obtained are analyzed through profile analysis. In profile analysis, the
means and medians of the scale values are found out and then are compared by plotting or
statistical analysis. Through this method, it is possible to compare the overall similarities and
differences among the objects.
The versatility of the semantic differential scale increases its application in the marketing
research. It is widely used in comparing the brand, company image, and product. It also helps in
developing an advertising campaign and promotional strategies in new product development
studies.
3. CONSTANT SUM SCALING
Definition: The Constant Sum Scaling is a technique wherein the respondents are asked to
allocate a constant sum of units, such as points, dollars, chips or chits among the stimulus
objects according to some specified criterion.

In other words, a scaling technique that involves the assignment of a fixed number of units to
each attribute of the object, reflecting the importance a respondent attaches to it, is called as
constant sum scaling. For example, Suppose a respondent is asked to allocate 100 points to the
attributes of a body wash on the basis of the importance he attaches to each attribute. In case he
feels any attribute being unimportant can allocate zero points and in case some attribute is twice
as important as any other attribute can assign it twice the points. The sum of all the points
allocated to each attribute should be equal to 100.
Once the points are allocated, the attributes are scaled by counting the points as assigned by the
respondents to each attribute and then dividing it by a number of respondents under analysis.
Such type of information cannot be obtained from rank order data unless it is transformed into
interval data. The constant sum scaling is considered as an ordinal scale because of its
comparative nature and lack of generalization.
One of the advantages of the constant sum scaling technique is that it allows a proper
discrimination among the stimulus objects without consuming too much time. But however, it
suffers from two serious limitations. First, the respondent might allocate more or fewer units
than those specified. Second, there might be a rounding error, in case too few units are
allocated. On the other hand, if a large number of units are used then it might be burdensome on
the respondents and causes confusion and fatigue.
4. GRAPHIC RATING SCALE
Graphic Rating Scale is a type of performance appraisal method. In this method traits or
behaviours that are important for effective performance are listed out and each employee is
rated against these traits. The rating helps employers to quantify the behaviours displayed by its
employees.
Ratings are usually on a scale of 1-5, 1 being Non-existent, 2 being Average, 3 being Good, 4
being Very Good and 5 being Excellent.
Example of a Graphic Rating Scale question:
How would you rate the individual in terms of quality of work, neatness and accuracy?
1. Non-Existent: Careless Worker. Tends to repeat similar mistakes
2. Average: Work is sometimes unsatisfactory due to untidiness
3. Good: Work is acceptable. Not many errors

4. Very Good: Reliable worker. Good quality of work. Checks work and observes.
5. Excellent: Work is of high quality. Errors are rare, if any. Little wasted effort.
Advantages:
• The method is easy to understand and is user friendly.
• Standardization of the comparison criteria’s
• Behaviours are quantified making appraisal system easier
Disadvantages:
• Judgemental error: Rating behaviours may or may not be accurate as the perception of
behaviours might vary with judges
• Difficulty in rating: Rating against labels like excellent and poor is difficult at times even
tricky as the scale does not exemplify the ideal behaviours required for a achieving a rating.
• Perception issues: Perception error like Halo effect, Recency effect, stereotyping etc. can
cause incorrect rating.
• They are good at identifying the best and poorest of employees. However, it does not help
while differentiating the average employees.
• Not effective in understanding the strengths of employees. Different employees have different
strong characteristics and these might quantify to the same score.

RANKORDERSCALING
Definition: The Rank Order Scaling is a yet another comparative scaling technique wherein the
respondents are presented with numerous objects simultaneously and are required to order or
rank these according to some specified criterion.
The Rank order scaling is often used to measure the preference for the brand and attributes. The
ranking data is typically obtained from respondents in the conjoint analysis (a statistical
technique used to determine how the brand and the combination of its attributes such as
features, functions, and benefits, influences the decision making of a person), as it forces the
respondents to discriminate among the stimulus objects. The Rank order scaling results in the
ordinal data.
With respect to the paired comparison scaling, the Rank order scaling resembles more closely to
the shopping environment, and also it takes less time and eliminates all the intransitive
responses (not object-directed). Such as, if there are ‘n’ stimulus objects, then only ‘n-1’scaling
decisions are to be made in case of Rank order scaling, while in the case of paired comparison
scaling ‘[n (n-1) /2]’ scaling decisions are required. Moreover, the rank order scaling is an easy
method to understand. But, however, the major limitation of this process is that it results only in
ordinal data.
1. PAIREDCOMPARISONSCALING
Definition: The Paired Comparison Scaling is a comparative scaling technique wherein the
respondent is shown two objects at the same time and is asked to select one according to the
defined criterion. The resulting data are ordinal in nature.
The paired Comparison scaling is often used when the stimulus objects are physical products.
The comparison data so obtained can be analyzed in either of the ways. First, the researcher can
compute the percentage of respondents who prefer one object over another by adding the
matrices for each respondent, dividing the sum by the number of respondents and then
multiplying it by 100. Through this method, all the stimulus objects can be evaluated
simultaneously.
Second, under the assumption of transitivity (which implies that if brand X is preferred to
Brand Y, and brand Y to brand Z, then brand X is preferred to brand Z) the paired comparison

data can be converted into a rank order. To determine the rank order, the researcher identifies
the number of times the object is preferred by adding up all the matrices.
The paired comparison method is effective when the number of objects is limited because it
requires the direct comparison. And with a large number of stimulus objects the comparison
becomes cumbersome. Also, if there is a violation of the assumption of transitivity the order in
which the objects are placed may bias the results.
2. Forced Ranking Scale
A ranking system, also known as the vitality curve, forced distribution or rank and yank, grades
a workforce based on the individual productivity of its members.
Members, most often employees but sometimes managers, are graded into groups A, B, or C. A
employees are the most engaged, passionate, charismatic, open to collaboration and committed.
B workers do not display as many of the positive qualities of A employees but are crucial to the
organisation’s success because they are so abundant. In contrast, C employees are commonly
non-producing procrastinators.
Forced ranking is a controversial technique because it focuses on making relative comparisons
between a company’s best and worst employees using subjective criteria. It’s effectiveness also
tends to peter out after a few years because C employees will often leave the company once
they realise where they have been ranked, resulting in a smaller concentration each time the
grading is carried out.
Despite the criticism, forced ranking is popular within large organisations because it’s a
relatively easy and economical way to improve worker efficiency.

3.3/3.4 TYPES OF DATA
Collection of data is a statistical requirement. Statistics are a set or series of numerical data that
acts as a facilitating factor of policy-making. In other words, numerical data establishes
Statistics. Numerical data undergoes processing and manipulations before it aids the process of
decision making. Hence, numerical data are the raw materials to statistics. These raw materials
can originate from various sources. Statisticians and analysts collect these data in different
methods.
There are 2 types of data. Discussed below are the types of data.
1. Primary Data – refers to the data that the investigator collects for the very first time. This type
of data has not been collected either by this or any other investigator before. A primary data will
provide the investigator with the most reliable first-hand information about the respondents.
The investigator would have a clear idea about the terminologies uses, the statistical units
employed, the research methodology and the size of the sample. Primary data may either be
internal or external to the organization.
2. Secondary Data – refers to the data that the investigator collects from another source. Past
investigators or agents collect data required for their study. The investigator is the first
researcher or statistician to collect this data. Moreover, the investigator does not have a clear
idea about the intricacies of the data. There may be ambiguity in terms of the sample size and
sample technique. There may also be unreliability with respect to the accuracy of the data.
Sources of Primary Data
The sources of primary data are primary units such as basic experimental units, individuals,
households. Following methods are used to collect data from primary units usually and these
methods depends on the nature of the primary unit. Published data and the data collected in the
past is called secondary data.
 Personal Investigation
The researcher conducts the experiment or survey himself/herself and collected data from it.
The collected data is generally accurate and reliable. This method of collecting primary
data is feasible only in case of small scale laboratory, field experiments or pilot surveys and
is not practicable for large scale experiments and surveys because it take too much time.

 Through Investigators
The trained (experienced) investigators are employed to collect the required data. In case of
surveys, they contact the individuals and fill in the questionnaires after asking the required
information, where a questionnaire is an inquiry form having a number of questions
designed to obtain information from the respondents. This method of collecting data is
usually employed by most of the organizations and its gives reasonably accurate information
but it is very costly and may be time taking too.
 Through Questionnaire
The required information (data) is obtained by sending a questionnaire (printed or soft form)
to the selected individuals (respondents) (by mail) who fill in the questionnaire and return it
to the investigator. This method is relatively cheap as compared to “through investigator”
method but non-response rate is very high as most of the respondents don’t bother to fill in
the questionnaire and send it back to investigator.
 Through Local Sources
The local representatives or agents are asked to send requisite information who provide the
information based upon their own experience. This method is quick but it gives rough
estimates only.
 Through Telephone
The information may be obtained by contacting the individuals on telephone. Its a Quick and
provide accurate required information.
 Through Internet
With the introduction of information technology, the people may be contacted through
internet and the individuals may be asked to provide the pertinent information. Google
survey is widely used as online method for data collection now a day. There are many paid
online survey services too.
It is important to go through the primary data and locate any inconsistent observations before
it is given a statistical treatment
Advantages
 Research is oriented for specific goals and purpose, cutting out possibility of wasting
resources.
 The researchers can change the course of study whenever needed, and choose platforms
for observation well-suited for projects.

 Gives original research quality, and does not carry bias or opinions of third parties.
Disadvantages
 Primary research may ask for huger expense than secondary research.
 The procedure is more time consuming, and costs a lot of assets.
 The outcome from research audience may not be always feasible.
SOURCES OF SECONDARY DATA
Secondary data are second hand informations. They are not collected from the source as the
primary data. In other words, secondary data are those which have already been collected. So
they may be relatively less accurate than the primary data. Secondary data are generally used
when the time of enquiry is short and the accuracy of the enquiry can be compromised to some
extent. Secondary data can be collected from a number of sources which can broadly be
classified into two categories.
i) Published sources
ii) Unpublished sources
Published Sources:
Mostly secondary data are collected from published sources. Some important sources of
published data are the following.
1. Published reports of Central and State Governments and local bodies.
2. Statistical abstracts, census reports and other reports published by different ministries of the
Government.
3. Official publications of the foreign Governments.
4. Reports and Publications of trade associations, chambers of commerce, financial institutions
etc.
5. Journals, Magazines and periodicals.

6. Periodic Publications of Government organizations like Central Statistical Organization (C.
S. O.), National Sample Survey Organization (NSSO).
7. Reports submitted by Economists, Research Scholars, Bureaus etc.
8. Published works of research institutions and Universities etc.
Unpublished Sources:
Statistical data can also be collected from various unpublished sources. Some of the important
unpublished sources from which secondary data can be collected are:
1. The research works carried out by scholars, teachers and professionals.
2. The records maintained by private firms and business enterprises. They may not like to
publish the information considering them as business secret.
3. Records and statistics maintained by various departments and offices of the Central and State
Governments, Corporations, Undertakings etc.
Secondary data are already collected informations. They might have been collected for some
specific purposes. So they must be used with caution. It is generally very different to verify
such information to find out inconsistencies, errors, omissions etc. Therefore scrutiny of
secondary data is essential. Because the data might be inaccurate, unsuitable or inadequate.
Thus it is very risky to use statistics collected by other people unless they have been thoroughly
edited and found reliable, adequate and suitable for the purpose.
It is the information that someone has already researched on, prepared, and analyzed. The
results are available for use, and can help other future researchers in referring the data for
studies. Some of the examples of secondary researches are government consensus, public
agency annual reports, magazines, newspapers, journals, online databases etc.
Advantages
 Cost-effect, ready made observations, less time spent on gathering information.
 Statistically reliable, less requirement of expertise from internal team.
 Trustable and ethical practices existing to support or organize other researches.

Disadvantages
 Information may be unsuitable for current research project.
 The data may lack details that fulfill goal of the client at present.
 Not customized, may require intensive study to judge validity of data.
PRECAUTIONS IN USING SECONDARY DATA
As you know, the secondary data has been collected and analysed by someone else. Therefore,
while using it you should be very careful. You have to study the data carefully because it may
be unsuitable or may be inadequate in the context of your study. It is never safe to take
published statistics at their face value without knowing their meaning and limitations. You
should always keep in mind the following precautions before using secondary data :
1) Reliability of Data : Secondary'data should only be utilised if they are found reliable. The
reliability can be tested by examining the following aspects :
i) Who collected the data?
ii) What were the sources of data?
iii) Were they collected in a proper manner?
iv) At what time were they collected?
V) Was the compiled biased?
vi) What level of accuracy was desired? Was it achieved?
If the collecting agency happens to be some government institution or international
organisation or other competent authority, the secondary data can be taken as more reliable
compared to the data collected by individuals or by some private organisation that is not well
reputed. Secondary data collected from published sources of,govemment departments and
corporations established under the Act of Parliament, and international institutions are reliable.
2) Suitability of Data : The data which may be suitable in one enquiry may not necessarily
be suitable in another enquiry. S o you should examine whether the data is suitable for your
study or not. If the available'data is found to be unsuitable, it should not be used. So, you
must carefully scrutinise the definition of vaf ous terms and units of data collected.
Similarly, the object, scope and tlature of the original enquiry must also be studied. If these
aspects are not found sound, the data will not be suitable for the relevant enquiry and
should not be used. For example, you are conducting a survey on wage levels including

allowances of workers. If the secondary data is available or!y on basic wages, such data is
not suitable for the present enquiry.
3) Adequacy of Data : Adequacy of the data has to be judged in the light of the
requirements of the &wey and the geographical area covered by the available secondary
data. For example, if our object is to study the wage rate of workers in cotton textile
industry of India and the published reports provide the data on wage rates of workers in all
industries together, then the data would not serve the purpose. The question of adequacy
may also be considered in the light of thetime period for which the data are available. For
example, for studying price trends we may require data for the last 20 years but the
secondary data is available for the last 4 years only. Here the available data would be
inadequate and would not serve our object. Similarly, if the level of accuracy achieved in a
given data is found inadequate for the purpose of a relevant enquiry, such data should not
be used by the investigator.
.
Thus, we should,use given.secondary data if it is reliable, suitable and adequate. If secondary
data is.available from authentic sources and also suitable and adequate for the
particular study, it will not be economical to spend time, energy and money in organising field 1
survey for collecting primary data. Thus, if the suitable secondary data is available, as
discussed above should be utilised with due precaution.

3.5 QUESTIONNAIRECONSTRUCTIONPROCESS
Definition: Questionnaire is a systematic, data collection technique consists of a series of
questions required to be answered by the respondents to identify their attitude, experience, and
behavior towards the subject of research.
One of the most critical parts of the survey is the creation of questions that must be framed in
such a way that it results in obtaining the desired information from the respondents. There are
no scientific principles that assure an ideal questionnaire and in fact, the questionnaire design is
the skill which is learned through experience.
QUESTIONNAIRECONSTRUCTIONPROCESS
The following steps are involved in the questionnaire construction process:
1. Specify the Information Needed: The first and the foremost step in designing the
questionnaire is to specify the information needed from the respondents such that the objective
of the survey is fulfilled. The researcher must completely review the components of the
problem, particularly the hypothesis, research questions, and the information needed.
2. Define the Target Respondent: At the very outset, the researcher must identify the target
respondent from whom the information is to be collected. The questions must be designed
keeping in mind the type of respondents under study. Such as, the questions that are appropriate
for serviceman might not be appropriate for a businessman. The less diversified respondent
group shall be selected because the more diversified the group is, the more difficult it will be to
design a single questionnaire that is appropriate for the entire group.
3. Specify the type of Interviewing Method: The next step is to identify the way in which the
respondents are reached. In personal interviews, the respondent is presented with a
questionnaire and interacts face-to-face with the interviewer. Thus, lengthy, complex and varied
questions can be asked using the personal interview method. In telephone interviews, the
respondent is required to give answers to the questions over the telephone. Here the respondent
cannot see the questionnaire and hence this method restricts the use of small, simple and precise
questions.
The questionnaire can be sent through mail or post. It should be self-explanatory and contain all
the important information such that the respondent is able to understand every question and
gives a complete response. The electronic questionnaires are sent directly to the mail ids of the
respondents and are required to give answers online.

4. Determine the Content of Individual Questions: Once the information needed is specified
and the interviewing methods are determined, the next step is to decide the content of the
question. The researcher must decide on what should be included in the question such that it
contribute to the information needed or serve some specific purpose.
In some situations, the indirect questions which are not directly related to the information
needed may be asked. It is useful to ask neutral questions at the beginning of a questionnaire
with intent to establish respondent’s involvement and rapport. This is mainly done when the
subject of a questionnaire is sensitive or controversial. The researcher must try to avoid the use
of double-barreled questions. A question that talks about two issues simultaneously, such as Is
the Real juice tasty and a refreshing health drink?
5. Overcome Respondent’s Inability and Unwillingness to Answer: The researcher should not
presume that the respondent can provide accurate responses to all the questions. He must
attempt to overcome the respondent’s inability to answer. The questions must be designed in a
simple and easy language such that it is easily understood by each respondent. In situations,
where the respondent is not at all informed about the topic of interest, then the researcher may
ask the filter questions, an initial question asked in the questionnaire to identify the prospective
respondents to ensure that they fulfil the requirements of the sample.
Despite being able to answer the question, the respondent is unwilling to devote time in
providing information. The researcher must attempt to understand the reason behind such
unwillingness and design the questionnaire in such a way that it helps in retaining the
respondent’s attention.
6. Decide on the Question Structure: The researcher must decide on the structure of questions to
be included in the questionnaire. The question can be structured or unstructured.
The unstructured questions are the open-ended questions which are answered by the
respondents in their own words. These questions are also called as a free-response or free-
answer questions.
While, the structured questions are called as closed-ended questions that pre-specify the
response alternatives. These questions could be a multiple choice question, dichotomous (yes or
no) or a scale.

7. Determine the Question Wording: The desired question content and structure must be
translated into words which are easily understood by the respondents. At this step, the
researcher must translate the questions in easy words such that the information received from
the respondents is similar to what was intended.
In case the question is written poorly, then the respondent might refuse to answer it or might
give a wrong answer. In case, the respondent is reluctant to give answers, then
“nonresponse” arises which increases the complexity of data analysis. On the other hand, if the
wrong information is given, then “ response error” arises due to which the result is biassed.
8. Determine the Order of Questions: At this step, the researcher must decide the sequence in
which the questions are to be asked. The opening questions are crucial in establishing
respondent’s involvement and rapport, and therefore, these questions must be interesting, non-
threatening and easy. Usually, the open-ended questions which ask respondents for their
opinions are considered as good opening questions, because people like to express their
opinions.
9. Identify the Form and Layout: The format, positioning and spacing of questions has a
significant effect on the results. The layout of a questionnaire is specifically important for the
self-administered questionnaires. The questionnaires must be divided into several parts, and
each part shall be numbered accurately to clearly define the branches of a question.
10.Reproduction of Questionnaire: Here, we talk about theappearance of the
questionnaire, i.e. the quality of paper on which the questionnaire is either written or printed.
In case, the questionnaire is reproduced on a poor-quality paper; then the respondent might feel
the research is unimportant due to which the quality of response gets adversely affected.
Thus, it is recommended to reproduce the questionnaire on a good-quality paper having a
professional appearance. In case, the questionnaire has several pages, then it should be
presented in the form of a booklet rather than the sheets clipped or stapled together.
11.Pretesting: Pretesting means testing the questionnaires on a few selected respondents or a
small sample of actual respondents with a purpose of improving the questionnaire by
identifying and eliminating the potential problems. All the aspects of the questionnaire must be
tested such as question content, structure, wording, sequence, form and layout, instructions, and
question difficulty. The researcher must ensure that the respondents in the pretest should be
similar to those who are to be finally surveyed.

Thus, the questionnaire design is a multistage process that requires the researcher’s attention to
many details.
PERSONAL INTERVIEWS
Face-to-face interviews can deeply probe how key stakeholders view your organization’s brand
and culture. It’s an opportunity to listen carefully and capture nuanced ideas. The people you
chose to interview and the questions you ask will change depending on your research goals.
However, personal interviews are typically used for internal leadership (executives and board
members) and sometimes customers or community leaders.
Advantages
 Interviewees can think about their responses, especially if the questions were provided
ahead of time. Trust is key. If respondents feel confident in the process, they’re likely to
fully engage with honest and insightful responses.
 The interviewer can ask unplanned, on-the-fly follow up questions to better understand a
response.
 Face-to-face interviews provide an opportunity to observe respondents’ attitudes and
behaviors, and to capture the actual language they use about your brand or product or
organization.
Disadvantages
 Personal interviews are typically more expensive and time-consuming than other research
methods.
 Scheduling a time where both the interviewee and interviewer can meet can be difficult
due to busy schedules or distance.
 Analyzing longer answers to open-ended questions is more challenging than analyzing
purely quantitative data, especially if you have many interviews. Depending on the goals
of your project, 15-20 interviews is often ample.

TELEPHONIC SURVEY INTERVIEW
On today’s market research landscape, new and innovative data collection methods are taking
advantage of the web and email to reach more samples, gather more intelligence, and generate
more actionable insights.
However, telephone interviewing is certainly still part of the mix. In fact, rather than fading
away into a dustbin of history (alongside rotary phones!), telephone interviewing for market
research is becoming more advanced in the mobile-centric era. Below, we highlight the
advantages and disadvantages.
Telephone Interviewing Advantages
 Can lead to relatively high response rates in specific markets
 Interviews can be completed fairly quickly
 Can be used to reach samples over a wide geographic area
 Virtually everyone has a land-line phone or cell/mobile phone which helps for getting a
representative sample of your audience
o Cell phone targeting for demographics is getting better than ever
 Cost effective when used to inform and impact business decisions of much larger
comparative value
 More control in targeting specific types of samples vs. other methods (i.e. face-to-face
surveys in public)
 More personal in nature and, when conducted by skilled and experienced interviewers,
can help enhance a business’s image (i.e. the experience can leave a positive impression
on current and prospective customers, ultimately leading to sales, referrals and
recommendations).
 Provided that the questions are properly formulated and the interview is professionally
administered, the quality of data generated can be high vs. other methods (e.g. surveys
delivered over mobile devices, etc.).
Telephone Interviewing Disadvantages
 Typically, questions cannot be of a complex nature. Discrete choice modeling and other
research methods with complicated questions need to be seen to be understood.
 Given the rather widespread aversion to telemarketers, samples may perceive legitimate
market research interviews as sales calls, and therefore refuse to participate.

 Unlike a face-to-face interview or focus group, interviewers – no matter how experienced
and skilled – cannot see body language. For example, if a sample furrows his brow in the
middle of a call (likely indicating a lack of understanding), interviewers will not be able
to adjust in real-time accordingly
 When the target audience is available through an online panel, telephone interviewing
often appears as a much more expensive alternative
 One of the most widely utilized survey methods, an online survey is the systematic
gathering of data from the target audience characterized by the invitation of the
respondents and the completion of the questionnaire over the World Wide Web.
ONLINE SURVEY
For the past few years, the Internet has been used by many companies in conducting all sorts of
studies all over the world. Whether it is market or scientific research, the online survey has been
a faster way of collecting data from the respondents as compared to other survey methods such
as paper-and-pencil method and personal interviews. Other than this advantage, the web-based
survey also presents other pros and benefits for anyone who wishes to conduct a survey.
However, one should consider the drawbacks and disadvantages of an online survey method.
Advantages of Online Survey
1. Ease of Data Gathering
The Internet is a vast virtual world that connects all kinds of people from around the globe. For
this reason, a survey that requires a hundred or more respondents can be conducted faster via
the Internet. The survey questionnaire can be rapidly deployed and completed by the
respondents, especially if there’s an incentive that is given after their participation.
2. Minimal Costs
Traditional survey methods often require you to spend thousands of dollars to achieve the
optimal results. On the other hand, studies show that conducting an Internet survey facilitates
low-cost and fast data collection from the target population. Sending email questionnaires and
other online questionnaires are more affordable than the face-to-face method.

3. Automation in Data Input and Handling
With online surveys, the respondents are able to answer the questionnaire by means of inputting
their answers while connected to the Internet. Then, the responses are automatically stored in a
survey database, providing hassle-free handling of data and a smaller possibility of data errors.
4. Increase in Response Rates
Online survey provides the highest level of convenience for the respondents because they can
answer the questionnaire according to their own pace, chosen time, and preferences.
5. Flexibility of Design
Complex types of surveys can be easily conducted through the Internet. The questionnaire may
include more than one type of response format in such a way that the respondents would not get
discouraged from the changes in the manner they answer the questions.
Disadvantages of Online Survey
1. Absence of Interviewer
An online survey is not suitable for surveys which ask open-ended questions because there is no
trained interviewer to explore the answers of the respondents.
2. Inability to Reach Challenging Population
This method is not applicable for surveys that require respondents who do not have an access to
the Internet. Some examples of these respondents include the elderly and people who reside in
remote areas.
3. Survey Fraud
Survey fraud is probably the heaviest disadvantage of an online survey. There are people who
answer online surveys for the sake of getting the incentive (usually in the form of money) after
they have completed the survey, not with a desire to contribute to the advancement of the study.

Business Research Methods Unit 3 notes

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Business Research Methods Unit 3 notes