Business Research

Measurement and Scaling

14-11-2022
Potential Sources of Error in Research Designs
 Errors in Research
• Total error is the variation between the true mean value in the
population of the variable of interest and the observed mean value
obtained in the research project.
• Random sampling error is the variation between the true mean value
for the population and the true mean value for the original sample.
• Non-sampling errors can be attributed to sources other than
sampling, and they may be random or nonrandom: including errors
in problem definition, approach, scales, questionnaire design,
interviewing methods, and data preparation and analysis. Non-
sampling errors consist of non-response errors and response errors.
 Errors in Research
• Non-response error arises when some of the respondents included
in the sample do not respond.

• Response error arises when respondents give inaccurate answers,

or their answers are misrecorded or misanalyzed.
 Measurement and Scaling Characteristics
Measurement

• The assignment of numbers or other symbols to characteristics of objects

according to certain prespecified rules.
• E.g. perceptions, attitudes, preferences, or other relevant characteristics.

Scale

• The generation of a continuum (range) upon which measured objects are

located. Scaling may be considered an extension of measurement.
• E.g. consider a scale from 1 to 100 for locating consumers according to the characteristic
“attitude toward department stores.
 Measurement and Scaling Characteristics
•Description: the unique labels or descriptors that are used to designate each value of
the scale. All scales possess descriptions.

•Order: the relative sizes or positions of the descriptors. Order is denoted by

descriptors such as greater than, less than, and equal to.

•Distance: The characteristic of distance means that absolute differences between the
scale descriptors are known and may be expressed in units.

•Origin: The origin characteristic means that the scale has a unique or fixed beginning
or true zero point.
Primary Scales of Measurement
 Scale Characteristics
Permissible Statistics
Scale Basic Characteristics Common Examples Marketing Examples Descriptive Inferential

Nominal Numbers identify and Social Security Brand numbers, store Percentages, mode Chi-square, binomial
classify objects numbers, numbering of types, sex classification test
football players
Ordinal Numbers indicate the Quality rankings, Preference rankings, Percentile, median Rank-order correlation,
relative positions of the rankings of teams in a market position, social Friedman, ANOVA
objects but not the tournament class
magnitude of differences
between them
Interval Differences between objects Temperature Attitudes, opinions, Range, mean, Product-moment
can be compared; zero point (Fahrenheit, index numbers standard deviation correlations, t-tests,
is arbitrary centigrade) ANOVA, regression,
factor analysis
Ratio Zero point is fixed; ratios of Length, weight, area, Age, income, costs, Geometric mean, Coefficient of variation
scale values can be volume, pressure sales, market shares harmonic mean
computed
Primary Scales of Measurement Nominal Scale
• The numbers serve only as labels or tags for identifying and classifying objects.

• When used for identification, there is a strict one-to-one correspondence

between the numbers and the objects.

• The numbers do not reflect the amount of the characteristic possessed by the
objects.

• The only permissible operation on the numbers in a nominal scale is counting.

• Only a limited number of statistics, all of which are based on frequency counts,
are permissible, e.g., percentages and mode.
Primary Scales of Measurement Ordinal Scale
• A ranking scale in which numbers are assigned to objects to indicate the
relative extent to which the objects possess some characteristic.

• Can determine whether an object has more or less of a characteristic than some
other object, but not how much more or less.

• Any series of numbers can be assigned that preserves the ordered relationships
between the objects.

• In addition to the counting operation allowable for nominal scale data, ordinal
scales permit the use of statistics based on centiles, e.g., percentile, quartile,
median.
Primary Scales of Measurement Interval Scale
• Numerically equal distances on the scale represent equal values in the
characteristic being measured.
• It permits comparison of the differences between objects.
• The location of the zero point is not fixed. Both the zero point and the units of
measurement are arbitrary.
• It is not meaningful to take ratios of scale values.
• Statistical techniques that may be used include all of those that can be applied
to nominal and ordinal data, and in addition the arithmetic mean, standard
deviation, and other statistics commonly used in marketing research.
Primary Scales of Measurement Ratio Scale
• Possesses all the properties of the nominal, ordinal, and interval scales.

• It has an absolute zero point.

• It is meaningful to compute ratios of scale values.

• Only proportionate transformations of the form y = bx, where b is a positive

constant, are allowed.

• All statistical techniques can be applied to ratio data.

 Measurement and Scaling:
Comparative &Non-comparative Scaling
Techniques
Comparison of Scaling Techniques
• Comparative scales involve the direct comparison of
stimulus objects. Comparative scale data must be
interpreted in relative terms and have only ordinal or rank
order properties.
• In noncomparative scales, each object is scaled
independently of the others in the stimulus set. The
resulting data are generally assumed to be interval or ratio
scaled.
Classification of Scaling Techniques
Advantages of Comparative Scales
• Small differences between stimulus objects can be detected.
• Same known reference points for all respondents.
• Easily understood and can be applied.
• Involve fewer theoretical assumptions.
• Tend to reduce halo or carryover effects from one judgment
to another.
Relative Disadvantages of Comparative Scales
• Ordinal nature of the data
• Inability to generalize beyond the stimulus objects scaled
Paired Comparison Scaling
• A respondent is presented with two objects and asked to
select one according to some criterion.
• The data obtained are ordinal in nature.
• Paired comparison scaling is the most widely used
comparative scaling technique.
• With n brands, [n(n − 1) /2] paired comparisons are
required.
• Under the assumption of transitivity, it is possible to
convert paired comparison data to a rank order.
Obtaining Shampoo Preferences Using Paired
Comparisons
Comparative Scaling Techniques Rank Order
Scaling
• Respondents are presented with several objects
simultaneously and asked to order or rank them according
to some criterion.
• It is possible that the respondent may dislike the brand
ranked 1 in an absolute sense.
• Furthermore, rank order scaling also results in ordinal data.
• Only (n − 1) scaling decisions need be made in rank order
scaling.
Preference for Toothpaste Brands Using Rank Order
Scaling
 Comparative Scaling Techniques
Constant Sum Scaling
• Respondents allocate a constant sum of units, such as 100
points to attributes of a product to reflect their importance.
• If an attribute is unimportant, the respondent assigns it
zero points.
• If an attribute is twice as important as some other attribute,
it receives twice as many points.
• The sum of all the points is 100. Hence, the name of the
scale.
Importance of Toilet Soap Attributes Using a
Constant Sum Scale
 Noncomparative Scaling Techniques
• Respondents evaluate only one object at a time, and for this
reason non-comparative scales are often referred to as
monadic scales.
• Non-comparative techniques consist of continuous and
itemized rating scales.
 Continuous Rating Scale
• Respondents rate 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.
• The form of the continuous scale may vary considerably.
Continuous Rating Scale
 Itemized Rating Scales
• 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.
• The commonly used itemized rating scales are the Likert,
semantic differential, and Stapel scales.
 Likert Scale
• The Likert scale requires the respondents to indicate a
degree of agreement or disagreement with each of a series
of statements about the stimulus objects.
Blank Strongly Disagree Neither agree Agree Strongly
disagree nor disagree agree

1. Wal-Mart sells high-quality 1 2 3 4 5

merchandise.
2. Wal-Mart has poor in-store service 1 2 3 4 5
3. I like to shop at Wal-Mart. 1 2 3 4 5
Likert Scales
 Semantic Differential Scale
• The semantic differential is a seven-point rating scale with end points
associated with bipolar labels that have semantic meaning.
WAL-MART IS:
• Powerful --:--:--:--:-X-:--:--: Weak
• Unreliable --:--:--:--:--:-X-:--: Reliable
• Modern --:--:--:--:--:--:-X-: Old-fashioned
• The negative adjective or phrase sometimes appears at the left side of the scale and sometimes
at the right.
• This controls the tendency of some respondents, particularly those with very positive or very
negative attitudes, to mark the right- or left-hand sides without reading the labels.
• Individual items on a semantic differential scale may be scored on either a −3 to +3 or a 1 to 7
scale.
A Semantic Differential Scale for Measuring Self-
Concepts, Person Concepts, and Product Concepts
1) Rugged :---:---:---:---:---:---:---: Delicate
2) Excitable :---:---:---:---:---:---:---: Calm
3) Uncomfortable :---:---:---:---:---:---:---: Comfortable
4) Dominating :---:---:---:---:---:---:---: Submissive
5) Thrifty :---:---:---:---:---:---:---: Indulgent
6) Pleasant :---:---:---:---:---:---:---: Unpleasant
7) Contemporary :---:---:---:---:---:---:---: Obsolete
8) Organized :---:---:---:---:---:---:---: Unorganized
9) Rational :---:---:---:---:---:---:---: Emotional
10) Youthful :---:---:---:---:---:---:---: Mature
11) Formal :---:---:---:---:---:---:---: Informal
12) Orthodox :---:---:---:---:---:---:---: Liberal
13) Complex :---:---:---:---:---:---:---: Simple
14) Colorless :---:---:---:---:---:---:---: Colorful
15) Modest :---:---:---:---:---:---:---: Vain
 Stapel Scale
• The Stapel scale is a unipolar rating scale with ten categories
numbered from -5 to +5, without a neutral point (zero). This scale is
usually presented vertically.
+5 +5
+4 +4
+3 +3
+2 +2X
+1 +1
HIGH QUALITY POOR SERVICE
-1 -1 The data obtained by using a
-2 -2 Stapel scale can be analyzed in the
-3 -3 same way as semantic differential
-4X -4 data.
-5 -5
 Noncomparative Scales
Scale Basic Characteristics Examples Advantages Disadvantages
Continuous Place a mark on a Reaction to TV Easy to construct Scoring can be
rating scale continuous line commercials cumbersome unless
computerized

Itemized Blank Blank Blank Blank

Rating Scales
Likert scale Degree of agreement on a Measurement of Easy to construct, More time
1 (strongly disagree) to 5 attitudes administer, and consuming
(strongly agree) scale understand

Semantic Seven-point scale with Brand, product, Versatile Controversy as to

differential bipolar labels and company whether the data are
images interval
Stapel scale Unipolar ten-point scale, Measurement of Easy to construct; Confusing and
25 to 15, without a neutral attitudes and administered over difficult to apply
point (zero) images telephone
 Summary of Itemized Scale Decisions
1. Number of categories Although there is no single, optimal number, traditional
guidelines suggest that there should be between five and nine
categories.
2. Balanced versus In general, the scale should be balanced to obtain objective data.
unbalanced
3. Odd or even number of If a neutral or indifferent scale response is possible from at least
categories some of the respondents, an odd number of categories should be
used.
4. Forced versus non- In situations where the respondents are expected to have no
forced opinion, the accuracy of data may be improved by a nonforced
scale.
5. Verbal description An argument can be made for labeling all or many scale
categories. The category descriptions should be located as close to
the response categories as possible.
6. Physical form A number of options should be tried and the best one selected.
Balanced and Unbalanced Scales
Rating Scale Configurations
Some Unique Rating Scale Configurations
Developing and design of Questionnaire for
Survey
What is a Survey
• A survey is a research method used for collecting data from a
predefined group of respondents to gain information and insights
into various topics of interest.

• Conducting a survey requires a set of questions

What is a Questionnaire
• A tool for collecting information to describe, compare, or explain an
event or situation, as well as, knowledge, attitudes, behaviors,
and/or sociodemographic characteristics on a particular target
group.

• A questionnaire is a document consisting of a set of questions and

scales to gather primary data.
Questionnaire
• A Questionnaire may form part of a survey if its end goal is for
data analysis.

• It could also be narrow in scope with a purpose of gathering just

one set of data e.g. a job application questionnaire.

• Simply put, a survey is always made up of a questionnaire but not

all questionnaires are surveys.
Questionnaire Survey
• Questionnaire surveys are a technique for gathering statistical
information about the attributes, attitudes, or actions of a
population by a structured set of questions.

• The information obtained from questionnaire surveys is

constructed through the process of designing and administering
the questionnaire and compiling the data that result.
Design of Questionnaire Survey
 Procedure for Questionnaire Development
1. Preliminary considerations
2. Question content
3. Response format
4. Question wording
5. Question sequence
6. Physical characteristics
7. Pre-test