Measurement

The assignment of numbers or other symbols to characteristics of objects according to certain pre-
specified rules.

Note that what we measure is not the object but some characteristic of it. Thus, we do not measure
consumers, only their perceptions, attitudes, preferences or other relevant characteristics. In marketing
research, numbers are usually assigned for one of two reasons. First, numbers permit statistical analysis
of the resulting data. Second, numbers facilitate a universal and transparent communication of
measurement rules and results

Scaling
Scaling may be considered an extension of measurement. Scaling involves creating a continuum upon
which measured objects are located. To illustrate, consider a scale for locating consumers according to
the characteristic ‘attitude towards visiting a cinema’. Each participant is assigned a number indicating
an unfavorable attitude (measured as 1), a neutral attitude (measured as 2) or a favourable attitude
(measured as 3). Measurement is the actual assignment of 1, 2 or 3 to each participant. Scaling is the
process of placing the participants on a continuum with respect to their attitude towards visiting a
cinema. In our example, scaling is the process by which participants would be classified as having an
unfavorable, neutral or positive attitude.

Scales Characteristics
All the scales used in marketing research can be described in terms of four basic characteristics. These
characteristics are description, order, distance and origin.

Description

By description, we mean the unique labels or descriptors that are used to designate each value of the
scale. Some examples of descriptors are as follows: 1. Female, 2. Male; 1 = Strongly disagree, 2 =
Disagree, 3 = Neither agree nor disagree, 4 = Agree and 5 = Strongly agree; and the numbers of euros
earned annually by a household. ‘Female’ and ‘male’ are unique descriptors used to describe values 1
and 2 of the gender scale. All scales possess this characteristic of description, i.e. all scales have unique
labels or descriptors that are used to define the scale values or response option.

Order

By order, we mean the relative sizes or positions of the descriptors. There are no absolute values
associated with order, only relative values. Order is denoted by descriptors such as ‘greater than’, ‘less
than’ and ‘equal to’. For example, a participant’s preference for art forms that they visit can be
expressed by the following order, with the most-preferred art form being listed first and the least-
preferred last:
Cinema

Theatre

Pop concert

For this participant, the preference for the cinema is greater than the preference for the theatre.
Likewise, the preference for a pop concert is less than the preference for the theatre.

Distance

The characteristic of distance means that absolute differences between the scale descriptors are known
and may be expressed in units. A five-person household has one person more than a four-person
household, which in turn has one person more than a three-person household.

Origin

The origin characteristic means that the scale has a unique or fixed beginning, or trues zero point. Thus,
an exact measurement of income by a scale such as what is the annual income of your household before
taxes?

Primary Scale of measurement

There are four primary scales of measurement: nominal, ordinal, interval and ratio.2 These scales are
illustrated in the following figure:
1. Nominal scale: A scale where numbers are used to level or tag the objects for identifying
them. For example- social security numbers, football players’ numbers, brand numbers.

Examples in Marketing Research:

o Demographics: Gender (Male, Female, Other)

o Brands: Preferred brand of a product (e.g., Nike, Adidas, Puma)
o Regions: Customer’s location (North, South, East, West)
o Product Categories: Types of products purchased (e.g., Electronics, Clothing,
Grocery)

Ordinal Scale A scale that categorizes and ranks data into an order or hierarchy, but the
differences between ranks are not uniform. Data can be ranked, but the intervals between ranks
are unknown. It is used to understand preferences, attitudes, or levels of satisfaction of customer.

Examples in Marketing Research:

 Customer Satisfaction:
o 1 = Very Dissatisfied, 2 = Dissatisfied, 3 = Neutral, 4 = Satisfied, 5 = Very
Satisfied
 Purchase Intent:
o Would you buy this product? (Definitely Not, Probably Not, Neutral, Probably
Yes, Definitely Yes)
  Ranking Preferences: Rank these features of a product (e.g., Price, Quality, Brand
Name, Packaging).

Interval Scale Data is measured in equal intervals, but there is no true zero point. It is used to
measure consumer perceptions, opinions, or attitudes on a numerical scale.

 Examples in Marketing Research:

o Likert Scale:
 "On a scale of 1 to 5, how likely are you to recommend this product?"
o Temperature Ratings: Perception of a campaign’s effectiveness (e.g., 0 to 100
scale).
o Ad Effectiveness: "Rate this advertisement’s impact from 1 to 10."

Ratio Scale Data is measured with equal intervals and has a true zero point. It is used to
measure quantitative variables like sales, revenue, or time.

 Examples in Marketing Research:

o Sales Data: Total number of units sold (e.g., 0, 50, 100 units).
o Revenue: Income generated from a product (e.g., $0, $5000, $10,000).
o Ad Impressions: Number of views or clicks (e.g., 0 views, 1000 views).
o Customer Age: Age of customers (e.g., 0 years, 25 years).

Scaling techniques/ types of scaling techniques

The scaling techniques commonly employed in marketing research can be classified into comparative
and non-comparative sales.

Scaling techniques

Comparative scales Non-comparative scales

Paired Rank Constant Q-sort & other Continuous Itemized rating

comparison sum procedure rating scales scales
Order

likert semantic stapel

1. Comparative scaling techniques: Comparative scales involve the direct comparison of stimulus
objects. For example – respondents might be asked whether they prefer sensation or Black Panther. It
includes-

a. Paired comparison: In paired comparison scaling a respondent is presented with two objects and
asked to select one according to some criterion. Paired comparison scales are frequently used when the
stimulus objects are physical objects.

b. Rank order scaling: A comparative scaling techniques in which respondents are presented with
several objects simultaneously and asked to order or rank them according to some criterion.

c. Constant scaling: In constant sum scaling respondents allocate a constant sum of units, such as points,
dollars, or chips, among a set of stimulus objects with respects to some criterion. For example- rank-1,
coca-cola, brand value 69.39 core tk. and rank-2, Microsoft, brand value 61.37 core tk.

2. Non-comparative scaling: In non-comparative scaling each stimulus objects is scaled independently of

the other objects in the stimulus set. It is divided into two parts-

a. Continuous rating scale: In a continuous rating scale also referred to as a graphic rating scale,
respondents rare the objects by placing a mark at the appropriate position on a line that runs from one
extreme of the criterion variable to the other. Here the respondents are not restricted to sleeting marks
previously set by the researcher.
b. Itemized rating scale: Here the respondents are provided with a scale that has a number or brief
description associated with each category. The categories are ordered in terms of scale position and the
respondents are required to select the specified category that best describes the object being rated. it
includes-

i. Likert scale: Here the respondents have to indicate a degree of agreements or dis agreement with
each of a series of statements about the stimulus objects. A measurement scale with five response
categories ranging from “strongly disagrees” to “strongly agree” which requires the respondents to
indicate a degree.

ii. Semantic differential scale: It is a 7-point rating scale with endpoints associated with bipolar labels
that have semantic meaning.

iii. Staple scale: A scale for measuring attitudes that consist of a single adjective in the middle of an even
numbered range of values from -5 to +5 without a neutral point zero. Respondents are asked to indicate
how accurately or inaccurately each term describes the object by selecting an appropriate numerical
response category. The higher the number the more accurately the term describes the object.

Multi-item scale
A multi-item scale consists of multiple items, where an item is a single question or statement to be
evaluated. A multi-item scale is a tool consisting of two or more questions (items) that
collectively measure a single construct or concept. These items are often combined into a
composite score for analysis. It is used in surveys, questionnaires, and research to ensure validity
and reliability. The scale development process are given below:
Measurement accuracy
A measurement is a number that reflects some characteristic of an object. A measurement is not the
true value of the characteristic of interest but rather an observation of it. A variety of factors can cause
measurement error, which results in the measurement or observed score being different from the true
score of the characteristic being measured.

The true score model provides a framework for understanding the accuracy of measurement.

According to this model,

Note that the total measurement error includes the systematic error, XS, and the random error, XR.
Systematic error affects the measurement in a constant way. It represents stable factors that affect the
observed score in the same way each time the measurement is made, such as mechanical factors.
Random error, on the other hand, is not constant. It represents transient factors that affect the
observed score in different ways each time the measurement is made, such as short-term transient
personal factors or situational factors.

Scale evaluation
Scale evaluation is the process of assessing whether a scale accurately and reliably measures the
intended concept. In marketing research, evaluating a scale ensures it meets the required standards for
reliability, validity, and generalisability. This step is critical for ensuring high-quality data collection and
analysis.

1. Reliability

Reliability refers to the consistency and stability of a scale's measurement. A reliable scale produces the
same results under consistent conditions. Approaches for assessing reliability include the test–retest,
alternative-forms and internal consistency methods.

a) Test–retest reliability An approach for assessing reliability, in which participants are administered
identical sets of scale items at two different times, under as nearly equivalent conditions as possible.

b) Alternative-forms reliability An approach for assessing reliability that requires two equivalent forms
of the scale to be constructed and then the same participants to be measured at two different times.

c) Internal consistency reliability An approach for assessing the internal consistency of a set of items,
where several items are summated in order to form a total score for the scale.

2. Validity

Determines whether a scale measures what it is intended to measure. It ensures the scale is accurate
and meaningful. Researchers may assess content validity, criterion validity or construct validity.
a) Content validity, sometimes called face validity, is a subjective but systematic evaluation of how well
the content of a scale represents the measurement task at hand.

b) Criterion validity reflects whether a scale performs as expected in relation to other selected variables
(criterion variables) as meaningful criteria.

c) Concurrent validity is assessed when the data on the scale being evaluated (e.g. loyalty scale) and the
criterion variables (e.g. repeat purchasing) are collected at the same time. construct validity includes
convergent, discriminant and nomological validity.

- Convergent validity is the extent to which the scale correlates positively with other measurements of
the same construct.

- Discriminant validity is the extent to which a measure does not correlate with other constructs from
which it is supposed to differ.

- Nomological validity is the extent to which the scale correlates in theoretically predicted ways with
measures of different but related constructs.

3. Generalizability

Generalizability refers to the extent to which one can generalize from the observations at hand to a
universe of generalizations. The set of all conditions of measurement over which the investigator wishes
to generalize is the universe of generalization.