MBA
MK-01: MARKETING RESEARCH
Unit
– 3
Q1.
What do you mean by Measurement? Why is measurement important in marketing
research?
Ans.
Measurement
is the assignment of a number to a characteristic of an object or event, which
can be compared with other objects or events. The scope and application of a
measurement is dependent on the context and discipline. In the natural sciences
and engineering, measurements do not apply to nominal properties of objects or
events, which is consistent with the guidelines of the International vocabulary
of metrology published by the International Bureau of Weights and Measures.
However, in other fields such as statistics as well as the social and
behavioral sciences, measurements can have multiple levels, which would include
nominal, ordinal, interval, and ratio scales.
Measurement is a
cornerstone of trade, science, technology, and quantitative research in many
disciplines. Historically, many measurement systems existed for the varied
fields of human existence to facilitate comparisons in these fields. Often
these were achieved by local agreements between trading partners or
collaborators. Since the 18th century, developments progressed towards
unifying, widely accepted standards that resulted in the modern International
System of Units (SI). This system reduces all physical measurements to a
mathematical combination of seven base units. The science of measurement is
pursued in the field of metrology.
Importance
of Measurement in Marketing Research:
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.
Q2.
What are the different types of scales? Discuss each with suitable example?
Ans.
There are four measurement scales (or types of
data): nominal, ordinal, interval
and ratio. These are simply ways to categorize different
types of variables. This topic is
usually discussed in the context of academic teaching and less often in the
“real world.” If you are brushing up on
this concept for a statistics test, thank a psychologist researcher named
Stanley Stevens for coming up with these terms.
These four measurement scales (nominal, ordinal, interval, and ratio)
are best understood with example, as you’ll see below
1.
Nominal
Scale :
Let’s
start with the easiest one to understand.
Nominal scales are used for labeling variables, without any quantitative
value. “Nominal” scales could simply be
called “labels.” Here are some examples,
below. Notice that all of these scales
are mutually exclusive (no overlap) and none of them has any numerical
significance. A good way to remember all
of this is that “nominal” sounds a lot like “name” and nominal scales are kind
of like “names” or labels. Examples of Nominal Scales:
What
is your gender?
·
Male
·
Female
Note: a sub-type of
nominal scale with only two categories (e.g. male/female) is called
“dichotomous.” If you are a student, you
can use that to impress your teacher.
2.
Ordinal
Scale :
With
ordinal scales, it is the order of the values is what’s important and
significant, but the differences between each one is not really known. Take a look at the example below. In each case, we know that a #4 is better
than a #3 or #2, but we don’t know–and cannot quantify–how much better it
is. For example, is the difference
between “OK” and “Unhappy” the same as the difference between “Very Happy” and
“Happy?” We can’t say.
Ordinal
scales are typically measures of non-numeric concepts like satisfaction,
happiness, discomfort, etc. “Ordinal” is easy to remember because is sounds
like “order” and that’s the key to remember with “ordinal scales”–it is the
order that matters, but that’s all you really get from these. Advanced note:
The best way to determine central tendency on a set of ordinal data is to use
the mode or median; the mean cannot be defined from an ordinal set. For Example
:-
How
do you feel today?
·
1. Very Unhappy
·
2. Unhappy
·
3. Ok
·
4. Happy
·
5. Very Happy
3.
Interval
Scale :
Interval
scales are numeric scales in which we know not only the order, but also the
exact differences between the values.
The classic example of an interval scale is Celsius temperature because
the difference between each value is the same.
For example, the difference between 60 and 50 degrees is a measurable 10
degrees, as is the difference between 80 and 70 degrees. Time is another good example of an interval
scale in which the increments are known, consistent, and measurable. Interval
scales are nice because the realm of statistical analysis on these data sets
opens up. For example, central tendency
can be measured by mode, median, or mean; standard deviation can also be
calculated.
Like
the others, you can remember the key points of an “interval scale” pretty
easily. “Interval” itself means “space
in between,” which is the important thing to remember–interval scales not only
tell us about order, but also about the value between each item.
Here’s
the problem with interval scales: they don’t have a “true zero.” For example, there is no such thing as “no
temperature.” Without a true zero, it is
impossible to compute ratios. With
interval data, we can add and subtract, but cannot multiply or divide. Confused?
Ok, consider this: 10 degrees + 10 degrees = 20 degrees. No problem there. 20 degrees is not twice as hot as 10 degrees,
however, because there is no such thing as “no temperature” when it comes to
the Celsius scale. I hope that makes
sense. Bottom line, interval scales are
great, but we cannot calculate ratios, which brings us to our last measurement
scale…
4.
Ratio
Scale :
Ratio
scales are the ultimate nirvana when it comes to measurement scales because
they tell us about the order, they tell us the exact value between units, AND
they also have an absolute zero–which allows for a wide range of both
descriptive and inferential statistics to be applied. At the risk of repeating myself, everything
above about interval data applies to ratio scales + ratio scales have a clear definition
of zero. Good examples of ratio
variables include height and weight.
Ratio
scales provide a wealth of possibilities when it comes to statistical
analysis. These variables can be
meaningfully added, subtracted, multiplied, divided (ratios). Central tendency can be measured by mode,
median, or mean; measures of dispersion, such as standard deviation and
coefficient of variation can also be calculated from ratio scales. This Device
Provides Two Examples of Ratio Scales (height and weight)
In
summary, nominal variables are used to “name,” or label a series of
values. Ordinal scales provide good
information about the order of choices, such as in a customer satisfaction
survey. Interval scales give us the
order of values + the ability to quantify the difference between each one. Finally, Ratio scales give us the
ultimate–order, interval values, plus the ability to calculate ratios since a
“true zero” can be defined.
Q3.
What are the major sources of errors in measurement?
Ans.:
Measurement should be precise and unambiguous in an ideal research study.
However, this objective is often not met with in entirety. As such, the
researcher must be aware about the sources of error in measurement. Following
are listed the possible sources of error in measurement.
Sources
of Error in Measurement
a)
Respondent:
At
times the respondent may be reluctant to express strong negative feelings or it
is just possible that he may have very little knowledge, but may not admit his
ignorance. All this reluctance is likely to result in an interview of
‘guesses.’ Transient factors like fatigue, boredom, anxiety, etc. may limit the
ability of the respondent to respond accurately and fully.
b)
Situation:
Situational
factors may also come in the way of correct measurement. Any condition which
places a strain on interview can have serious effects on the
interviewer-respondent rapport. E.g., if someone else is present, he can
distort responses by joining in or merely by being present. If the respondent
feels that anonymity is not assured, he may be reluctant to express certain
feelings.
c)
Measurer:
The
interviewer can distort responses by rewording or reordering questions. His
behavior, style and looks may encourage or discourage certain replies from
respondents. Careless mechanical processing may distort the findings. Errors
may also creep in because of incorrect coding, faulty tabulation and/or
statistical calculations, particularly in the data-analysis stage.
d)
Instrument:
Error
may arise because of the defective measuring instrument. The use of complex
words, beyond the comprehension of the respondent, ambiguous meanings, poor
printing, inadequate space for replies, response choice omissions, etc. are a
few things that make the measuring instrument defective and may result in measurement
errors.
Hence, researcher must
know that correct measurement depends on successfully meeting all of the issues
mentioned above. He must, as far as possible, try to eliminate, neutralize or
otherwise deal with all the possible sources of error so that the final results
may not be contaminated.
Q4.
What factors would you take in account while developing a marketing measure?
Ans.
Healthy revenue and profit margins are crucial
to any company. However, monitoring your bottom line is only one part of the
formula. It’s essential that you determine the factors critical to your
company’s success, measure those metrics and put into place a system for
continually improving performance. Here are ten guidelines for helping you
develop your company’s process.
1.
Define
Your Goals: Determine your measures for success.
Make your goals challenging, but achievable. Do you want to increase customer
retention, improve market share, penetrate a new market segment, change a
perception, generate more store traffic, reduce customer complaints? Be
specific and make your objectives measurable. For example, by what percentage
do you want to increase sales?
2.
Determine
the Metrics to Measure Your Company’s Performance: Compile
a list of factors that are important in your industry. Criteria may include:
Marketing:
sales growth; market share; distribution methods; sales force size,
effectiveness and training; advertising budget and effectiveness; inventory
levels, delivery time; product quality; customer retention rates
Production:
plant capacity, locations and age; age of equipment; ability to expand
capacity; skill and turnover of labor force; union relations; quality control;
supplier retention; raw material sources
Administrative:
employee turnover, age of facilities
Management: experience, depth and turnover of top, middle
and supervisory managers; effectiveness of communication systems; access
to information; cohesiveness of top management
ranks; compensation plans; decision-making speed; strategic planning ability
Technology/Research
& Development: age of R&D facilities; age of production technology; production patterns; basic innovation;
engineering abilities; experience of R&D team; R&D budget; R&D project timelines
3. Develop Methods to Collect and Organize
Data: Determine a process for tracking and reporting all relevant data. Report on
trends that emerge from your findings on a regular basis.
4.
Compare yourself to the
Competition: You can glean a lot by doing your
homework, including shopping your
competitors. Also check:
Annual reports on public companies
Internet search engines by
competitors' names or key words
Trade associations and publications
Business and general press as well
as press releases
Government agencies
Private research firms, including online computer
databases
5.
Conduct
Research: When you need specific information about your
customers and prospects that doesn’t exist, conduct your own primary research.
There
are two types of research: qualitative and quantitative. Qualitative research
is used to understand why customers behave as they do or to develop hypotheses
about that behavior. Personal interviews and focus groups (a meeting of 8-12
carefully selected people) are two examples of this semi-structured type of
survey. Quantitative research is a highly structured form that attempts to
answer how much. Numbers can be projected to the universe that the sample
represents. Telephone, online and mail surveys are example.
6.
Understand
Your Strengths and Weaknesses: Rate your company on
your developed list of metrics in comparison to your competitors. Look for
clusters of strength that may give you a competitive advantage.
7.
Focus
on Customer Retention: Customer retention is a matter of
business survival, as getting a new customer is five times more expensive than
retaining a current one. Work on core product and service attributes to build
customer loyalty (such as treating each customer as a valued individual).
Businesses must focus on such issues as instilling a helpful staff attitude,
delivering on advertising promises, developing a favorable return policy and
providing accurate product information. Use your success with current customers
to attract new referral business, but also remember that not every customer is
worth keeping. You cannot be all things to all people. Sometimes, you have to
let customers go and train energies on those clients who are the best fit.
8.
Measure
Marketing Effectiveness:
Effective measurement lays the groundwork for future plans, so keeping
track of results is the only way to improve your marketing efforts. The key is
determining which data should be collected. Your marketing results may be
measured in sales (dollars or units), market share, store traffic, number of
inquiries or reduced complaint rates, or other metrics. Tracking can also be
based on surveys that assess customer perceptions.
9.
Track
Employees: Having top employees who are motivated is critical
to your company’s success. Track the effectiveness of your recruitment methods
and retention levels as well as employee satisfaction and performance.
10. Apply
the Information: Analyze the intelligence you’ve collected, draw
conclusions and make recommendations based on it. Develop a plan for seeking
out opportunities to demonstrate your company’s strengths. If weaknesses are
critical drawbacks to your company’s success, develop a plan for overcoming
them.
Q5.
Explain the criteria of a good scale?
Ans.
In
case of measurement scale, it is
important to make sure that the instrument that we develop to measure a
particular concept is indeed accurately measuring the variable, and in fact, we
are actually measuring the concept that we set out to measure. This ensures
that in operationally defining perceptual and attitudinal variables, we have
not overlooked some important dimensions and elements or included some
irrelevant ones. The scales developed could often be imperfect and errors are
prone to occur in the measurement of attitudinal variables. The use of better
instruments will ensure more accuracy in results, which in turn, will enhance
the scientific quality of their search. Hence, in some way, we need to assess
the "goodness" of the measure developed.
What should be the
characteristics of a good measurement? An intuitive answer to this question is
that the tool should be an accurate indicator of what we are interested in
measuring. In addition, it should be easy and efficient to use. There are three major criteria for evaluating
a measurement tool: validity, reliability and sensitivity.
- Validity
Validity is the ability
of an instrument (for example measuring an attitude) to measure what it is
supposed to measure. That is, when we ask a set of questions (i.e. develop a
measuring instrument) with the hope that we are tapping the concept, how can we
be reasonably certain that we are indeed measuring the concept we set out to do
and not something else? There is no quick answer.
Researchers have
attempted to assess validity in different ways, including asking questions such
as "Is there consensus among my colleagues that my attitude scale measures
what it is supposed to measure? "and "Does my measure correlate with
others' measures of the `same' concept?" and "Does the behavior
expected from my measure predict the actual observed behavior?"
Researchers expect the answers to provide some evidence of a measure's
validity.
What is relevant
depends on the nature of the research problem and the researcher's judgment.
One way to approach this question is to organize the answer according to
measure-relevant types of validity. One widely accepted classification consists
of three major types of validity: (1) content validity, (2) criterion-related
validity, and (3) construct validity.
(1)
Content Validity
The content
validity of a measuring instrument (the composite of measurement scales) is the
extent to which it provides adequate coverage of the investigative questions
guiding the study. If the instrument contains a representative sample of the
universe of subject matter of interest, then the content validity is good. To
evaluate the content validity of an instrument, one must first agree on what
dimensions and elements constitute adequate coverage. To put it differently,
content validity is a function of how well the dimensions and elements of a
concept have been delineated. Look at the concept of feminism which implies a
person's commitment to a set of beliefs creating full equality between men and
women in areas of the arts, intellectual pursuits, family, work, politics, and
authority relations. Does this definition provide adequate coverage of the
different dimensions of the concept? Then we have the following two questions
to measure feminism:
1. Should men and women get equal pay for
equal work?
2. Should men and women share household
tasks?
These two questions do not provide coverage to all the
dimensions delineated earlier. It definitely
falls short of adequate content validity for measuring feminism.
A panel of persons to judge how well the instrument meets
the standard can attest to the content
validity of the instrument. A panel independently assesses the test items for a
performance test. It judges each
item to be essential, useful but not essential, or not necessary in assessing performance of a relevant behavior.
Face validity is considered as a basic and very minimum index
of content validity. Face validity
indicates that the items that are intended to measure a concept, do on the face
of it look like they measure the
concept. For example a few people would accept a measure of college student math ability using a question
that asked students: 2 + 2 = ? This is not a valid measure of college-level math ability on the face of it.
Nevertheless, it is a subjective agreement
among professionals that a scale logically appears to reflect accurately what
it is supposed to measure.
When it appears evident to experts that the measure provides adequate coverage of the concept, a measure has
face validity.
(2)
Criterion-Related
Validity
Criterion
validity uses some standard or criterion to indicate a construct accurately.
The validity of an indicator is verified by comparing it with another measure
of the same construct in which research has confidence. There are two subtypes
of this kind of validity.
Concurrent
validity: To have concurrent validity, an indicator must be associated with
a pre existing indicator that
is judged to be valid. For example we create a new test to measure intelligence. For it to be concurrently valid,
it should be highly associated with existing IQ tests (assuming the same definition of intelligence is used). It
means that most people who score
high on the old measure should also score high on the new one, and vice versa.
The two measures may not be
perfectly associated, but if they measure the same or a similar construct, it is logical for them to
yield similar results.
Predictive validity: Criterion
validity whereby an indicator predicts future events that are logically related to a construct is
called a predictive validity. It cannot be used for all measures. The measure and the action predicted must be
distinct from but indicate the same construct.
Predictive measurement validity should not be confused with prediction in hypothesis testing, where one variable
predicts a different variable in future. Look at the scholastic assessment tests being given to candidates seeking
admission in different subjects.
These are supposed to measure the scholastic aptitude of the candidates the ability to perform in institution as well
as in the subject. If this test has high predictive validity, then candidates who get high test score will
subsequently do well in their subjects. If
students with high scores perform the same as students with average or low
score, then the test has low predictive
validity.
(3)
Construct
Validity
Construct
validity is for measures with multiple indicators. It addresses the question:
If the measure is valid, do the various indicators operate in consistent
manner? It requires a definition with clearly specified conceptual boundaries.
In order to evaluate construct validity, we consider both theory and the
measuring instrument being used. This is assessed through convergent validity
and discriminant validity.
Convergent Validity: This
kind of validity applies when multiple indicators converge or are associated with
one another. Convergent validity means that multiple measures of the same
construct hang together or operate in similar ways. For example, we construct
"education" by asking people how much education they have completed,
looking at their institutional records, and asking people to complete a test of
school level knowledge. If the measures do not converge (i.e. people who claim
to have college degree but have no record of attending college, or those with
college degree perform no better than high school dropouts on the test), then
our test has weak convergent validity and we should not combine all three
indicators into one measure.
Discriminant Validity: Also called divergent validity, discriminant
validity is the opposite of convergent
validity. It means that the indicators of one construct hang together or converge, but also diverge or are
negatively associated with opposing constructs. It says that if two constructs A and B are very different,
then measures of A and B should not be associated.
For example, we have 10 items that measure political conservatism. People answer all 10 in similar ways. But we have
also put 5 questions in the same questionnaire that
measure political liberalism. Our
measure of conservatism has discriminant validity if the 10 conservatism items hang together and are negatively
associated with 5liberalism ones.
- Reliability
The reliability of a
measure indicates the extent to which it is without bias (error free) and hence
ensures consistent measurement across time and across the various items in the
instrument. In other words, there liability of a measure is an indication of
the stability and consistency with which the instrument measures the concept
and helps to assess the `goodness" of measure.
Stability
of Measures
The ability of the
measure to remain the same over time despite uncontrollable testing
conditions or the state of the respondents themselves is indicative of its
stability and low vulnerability to changes in the situation. This attests to
its "goodness" because the concept is stably measured, no matter when
it is done. Two tests of stability are test-retest reliability and
parallel-form reliability.
(1) Test-retest Reliability: Test-retest
method of determining reliability involves administering the same scale to the
same respondents at two separate times to test for stability. If the measure is
stable over time, the test, administered under the same conditions each time,
should obtain similar results. For example, suppose a researcher measures job
satisfaction and finds that 64 percent of the population is satisfied with
their jobs. If the study is repeated a few weeks later under similar
conditions, and there searcher again finds that 64 percent of the population is
satisfied with their jobs, it appears that them measure has repeatability. The
high stability correlation or consistency between the two measures at time 1
and at time 2 indicates high degree of reliability. This was at the aggregate
level; the same exercise can be applied at the individual level. When the measuring
instrument produces unpredictable results from one testing to the next, the
results are said to be unreliable because of error in measurement.
There are two problems with measures of
test-retest reliability that are common to all longitudinal studies. Firstly,
the first measure may sensitize the respondents to their participation in a
research project and subsequently influence the results of the second measure.
Further if the time between them measures is long, there may be attitude change
or other maturation of the subjects. Thus it is possible for a reliable measure
to indicate low or moderate correlation between the first and the second administration,
but this low correlation may be due an attitude change over time rather than to
lack of reliability.
(2) Parallel-Form Reliability: When responses
on two comparable sets of measures tapping the same construct are highly
correlated, we have parallel-form reliability. It is also called
equivalent-form reliability. Both forms have similar items and same response
format, the only changes being the wording and the order or sequence of the
questions. What we try to establish here is the error variability resulting
from wording and ordering of the questions. If two such comparable forms are
highly correlated, we may be fairly certain that the measures are reasonably
reliable, with minimal error variance caused by wording, ordering, or other
factors.
Internal
Consistency of Measures
Internal consistency of measures is indicative of
the homogeneity of the items in the measure that tap the construct. In other
words, the items should `hang together as a set,' and be capable of
independently measuring the same concept so that the respondents attach the
same overall meaning to each of the items. This can be seen by examining if the
items and the subsets of items in the measuring instruments are highly
correlated. Consistency can be examined through the inter-item consistency
reliability and split-half reliability.
1)
Inter-item
Consistency reliability: This is a test of consistency of
respondents' answers to all the items in a measure. To the degree that items
are independent measures of the same concept, they will be correlated with one
another.
2)
Split-Half
reliability: Split half reliability reflects the
correlations between two halves of an instrument. The estimates could vary
depending on how the items in the measure are split into two halves. The
technique of splitting halves is the most basic method for checking internal
consistency when measures contain a large number of items. In the split-half
method the researcher may take the results obtained from one half of the scale
items (e.g. odd-numbered items) and check them against the results from the
other half of the items (e.g. even numbered items). The high correlation tells
us there is similarity (or homogeneity) among its items.
It is important to note that reliability
is a necessary but not sufficient condition of the test of goodness of a
measure. For example, one could reliably
measure a concept establishing high stability and consistency, but it may not
be the concept that one had set out to measure. Validity ensures the ability of
a scale to measure the intended concept.
Sensitivity
The sensitivity of a scale is an important
measurement concept, particularly when changes in attitudes or other
hypothetical constructs are under investigation. Sensitivity refers to an
instrument's ability to accurately measure variability in stimuli or responses.
A dichotomous response category, such as "agree or disagree," does
not allow the recording of subtle attitude changes. A more sensitive measure,
with numerous items on the scale, may be needed. For example adding
"strongly agree,""mildly agree,""neither agree nor
disagree,""mildly disagree," and "strongly disagree"
as categories increases a scale's sensitivity.
The sensitivity of a scale based on a single
question or single item can also be increased by adding additional questions or
items. In other words, because index measures allow for greater range of
possible scores, they are more sensitive than single item.
Practicality:
The scientific requirements of a project call for
the measurement process to be reliable and valid, while the operational
requirements call for it to be practical. Practicality has been defined as economy,
convenience, and interpretability.
Q6.
What do you mean by Attitude? What are the main components of Attitude? Discuss
the limitations of measurement of attitude?
Ans.
In
psychology, an attitude is an expression of favor or disfavor toward a person,
place, thing, or event (the attitude object'). Prominent psychologist Gordon
All port once described attitudes "the most distinctive and indispensable
concept in contemporary social psychology."
Attitude can be formed
from a person's past and present. Key topics in the study of attitudes include
attitude measurement, attitude change, consumer behavior, and attitude-behavior
relationships. Attitude is a reflection of your mind as the way it attends to a
problem. This is a relative term, because it changes as per situation. Whether
it is positive or negative depends upon its suitability to the attitude of the
receiver, and the ultimate result of the decisions taken. All relative! It is
influenced by your formative strengths & weaknesses, grooming back-ground,
maturity, and thorough knowledge of the event.
A pre disposition or a
tendency to respond positively or negatively towards a certain idea, object,
person, or situation is known as attitude. Attitude influences an individual's
choice of action, and responses to challenges, incentives, and rewards
(together called stimuli).
Four
major components of attitude are :
(1)
Affective: emotions or feelings.
(2)
Cognitive: belief or opinions held consciously.
(3)
Conative: inclination for action.
(4)
Evaluative: positive or negative response to stimuli.
(a)
Cognitive component: Cognitive component of attitude is
related to value statement. It consists of belief, ideas, values and other
information that an individual may possess or has faith in. Quality of working
hard is a value statement or faith that a manager may have. For example, he says smoking is injurious to
health.
(b)
Affective component: Affective component of attitude is
related to person’s feelings about another person, which may be positive, negative
or neutral. I do not like Madan because he is not hard working, or I like
Manmohan because he is hard working. It is an expression of feelings about a
person, object or a situation.
For example, in an
organization a personal report is given to the general manager. In report he
point out that the sale staff is not performing their due responsibilities. The
general manager forwards a written notice to the marketing manager to negotiate
with the sale staff.
(c)
Behavioral component: Behavioral component of attitude is
related to impact of various situations or objects that lead to individual’s
behavior based on cognitive and affective components. I do not like Madan
because he is not hard working is an affective component, I therefore would
like to disassociate myself with him, is a behavioral component and therefore I
would avoid Madan. Development of favorable attitude and good relationship with
Manmohan is but natural. Individual’s favorable behavior is an outcome of the
fact that Manmohan is hardworking. Cognitive and affective components are bases
for such behavior. Former two components cannot be seen; only the behavior
component can be seen.
For example, before the
production and launching process the product. Report is prepared by the
production department which consists of their intention in near future and long
run and this report is handed over to top management for the decision.
Former is important
because it is a base for formation of attitude.
However, Limitations
of Attitude Measurement may be as under:
a) The attitude is
intangible and not subject to visual observations.
b) The consumer
attitude is a complex affair due to multiple influences. Hence, we cannot say
with certainty how a person will react.
c) Measuring attitude
lacks proper scale. Marketing Research has no instruments device to measure
attitude correctly.
Q7.
Explain the construction criteria of any of the following attitude measurement
scale :
a. Thurstone Equal Appearing Interval
Scale
b. Likert Summated Rating Scale
c. Semantic Differential Scale
Ans.
Attitude Measurement Scales : With a view to
assessing the degree of attitudes possessed by persons and to be able to study
a large number of people, the scaling technique was introduced into attitude
measurement. Various scales of attitude measurement have been developed. Here
we shall only broadly discuss the characteristics of some prevalent attitude
scales so as to get acquainted with the general steps involved in their
construction and use. It is likely that, in spite of numerous scales being
available, you do not find one handy or suitable when you take up a particular
study. Knowledge of how to develop an attitude scale will obviate such a
crippling situation and help you have an instrument tailor-made for a given
study. For a detailed discussion of how to construct an attitude scale, you may
refer to Allen L. Edwards' (1957) Techniques of Attitude Scale Construction.
Thurstone
Equal Appearing Interval Scale
Louis L. Thurstone and
E.J. Chave (1929) in their classic study of attitudes toward the Church
developed an interval scale by using the method of equal-appearing intervals.
To construct the Thurstone scale, a large number of statements are collected
which express various possible opinions about the issue or object of study. These
statements, after an editing for relevance and clarity, are given to judges,
who are to independently sort them into eleven sets along a continuum that
ranges from most unfavorable, through neutral, to most favorable. The eleven
sets of statements are to occupy positions in the continuum in such a way that
the positions are at equal intervals; that is, the difference between any two
adjacent positions is the same as the one between any other two adjacent
positions. For the final form of the scale, only those items are retained that
have high inter judge agreement and fall at equal intervals.
The judges are to
assign the statements to appropriate positions on the scale only on the logical
basis of how favorable or unfavorable an opinion every statement expresses by
itself and not how far the judges personally agree or disagree with the
statements. The average judged position of a statement on the eleven-point
continuum is the scale value for that statement. Thus, when a Thurstone scale
is ready, every statement in it (there are usually about twenty statements) has
a numerical value already determined. When administered, the respondent just
checks the items s/he agrees with and her/his attitude score is the mean value
of the items s/he checked.
Likert
Summated Rating Scale
For the Likert scale,
various opinion statements are collected, edited and then given to a group of
subjects to rate the statements on a five-point continuum: 1=strongly agree;
2=agree; 3=undecided; 4=disagree; and 5=strongly disagree. The subjects express
the degree (one to five) of their personal agreement or disagreement with each
of the statements. Only those items which in the analysis best differentiate
the high scorers and the low scorers of the sample subjects are retained and
the scale is ready for use. To measure the attitude of a given group of
respondents, this scale is given to them and every respondent indicates whether
s/he strongly agrees, agrees, is undecided, disagrees, or strongly disagrees
with each statement. The respondent's attitude score is the sum of her/his
ratings of all the statements. For this reason, the Likert scale is also known
as the scale of Summated Ratings.
In the Thurstone scale,
the respondent checks only those items with which s/he agrees, whereas in the Likert
scale s/he indicates her/his degree of agreement or disagreement for all the
items in the scale. Further, the development of a Likert scale does not require
a panel of judges. It may also be noted that Likert did not assume equal
intervals between the scale points. His scale is ordinal and, therefore, can
only order respondents' attitudes on a continuum; it does not indicate the
magnitude of difference between respondents.
By and large, a great
majority of researchers prefer the Likert technique to Thurstone's. In many
current research studies we come across seven-point scales being used, which
bear the appearance of the Likert scale. It must be noted that the typical
Likert technique requires an item analysis to establish that all the items in
the scale measure the same attitude -- no matter whether the scale has five or
more points.
The
Semantic Differential Scale
The now-classic
research by Osgood and his colleagues, based on extensive factor-analytic
studies across cultures, has shown that people understand, or give meaning to,
words or concepts along three dominant dimensions--the evaluative (good-bad)
dimension, the potency (strong-weak) dimension, and the activity
(active-passive) dimension. It has also been found that scores on the
evaluative dimension correlate highly with other measures of attitude toward a
particular social object.
The Semantic
Differential, developed by Osgood, Suci and Tannenbaum, can be used to measure
attitudes from the meaning (semantic = meaning or psychological significance)
which people give to a word or concept that is related to an attitude object.
This instrument consists of a series of bipolar adjectives such as fair-unfair,
pleasant-unpleasant, good-bad, clean-dirty, valuable-worthless, etc. Each pair
constitutes a continuum of seven points, the endpoints being the opposites of
the adjective pairs and the midpoint being the neutral position. A sample of
the bipolar continuum is given below:
.Fair.
1_______2_______3______4______5_______6_______7 Unfair
Valuable 1_______2_______3______4______5_______6_______7
Worthless
.Good.
1_______2_______3______4______5_______6_______7 Bad... ...
Suppose, by means of
the Semantic Differential, you want to measure an individual's attitude towards
legalised abortion. The respondent is given a set of bipolar adjectives (such
as the ones sampled above) and s/he is asked to indicate as to where for
her/him the given attitude object (legalised abortion) falls in each continuum.
The numeral corresponding to the position checked by the subject is her/his
score for that continuum. One's overall attitude score is the sum (or the mean)
of the scores on all the continua.
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