Concepts, indicators , variables/
Types of measurement scales
Definitions of a variable:
• 1-A characteristic, number or quality that that
increases or decreases over time, or takes
different values in different situations.
• 2-An image, perception or concept that can be
measured – hence capable of taking on
• 3-a label or name that represents a concept or
characteristic of the subjects that varies (e.g.,
gender, weight, achievement, etc.) .
Other definitions of variables :-
a-Conceptual definition: uses concepts to define a
• 1-Achievement: what one has learned from formal
• 2-Aptitude: one's capability for performing a particular
b-Operational definition: is an indication of the meaning
of a variable through the specification of the manner
by which it is measured:
• 1-Weschler IQ score
• 2-Income levels below and above 45,000 pounds per
• They are terms which people create for the
purpose of communication and efficiency
• Concepts are mental images or perceptions
and therefore their meaning varies markedly
from individual to individual.
• E.g. :
• personal impression about an object?
• Vary in meaning & cannot be measured
Identifying Variables from concepts:
• In a research , it is important that the
concepts used should be operationalised in
measurable terms( so that variations in
respondents’ answers are reduced ,if not
• Techniques about how to operationalise
concepts, play an important role in reducing
The difference between a concept and a variable:
• A concept cannot be measured whereas a
variable can be subjected to measurement by
crude/refined or subjective/objective units of
• It is therefore important for the concept to be
converted into variables .
• Is a computed or collated collective
characteristics of the persons under study.
• Is a set of criteria reflected from the concept.
Indicator can then be converted into variables.
• Indicators are used to show whether or not
the objectives have been achieved.
Concepts, indicators and variables:
• When using a concept in the study, researcher
need to develop indicators from the concept,
• that is, developing from these indicators,
• this is what is called operationalization ,
• operationalization is thus a process of
quantifying variables for the purpose of
measuring their: occurrence, strength and
• For example, to determine this concept ?
• the level of knowledge , concerning a specific
• The variable :poor knowledge, will assist in
determining factors influencing the problem
• Some times the research team in a particular
case decide that one indicator is not enough,
more are needed to fully measure or quantify
• E.g. Psychologists have built entire
questionnaires to measure complex concepts
such as depression, where a range of
questions on mood and emotion are then
• Then a persons depression level or ‘score’ is
Types of variables
A-Independent and dependent variables (i.e.,
cause and effect)
• is a factor that can be varied or manipulated in
an experiment under the control of the
experimenter (e.g. time, temperature, etc).
• It is usually what will affect the dependent
• Independent variables act as the "cause" in
that they precede, influence, and predict the
• There are two types of independent variable,
which are often treated differently in analyses:
• 1- quantitative variables that differ in
amounts or scale and can be ordered (e.g.
weight, temperature, time).
• 2- qualitative variables which differ in "types"
and can not be ordered (e.g. gender, method
of treatments ).
2- DEPENDENT VARIABLE
• Is the variable that is used to describe or
measure the problem under study.
• They describe or measure the factors that are
assumed to cause or at least influence the
• It can take different values in response to an
• Dependent Variable is something that might
be affected by the change in the independent
• Whether a variable is dependent or
independent, is determined by the statement
of the problem and the objectives of the
• It is therefore important when designing an
analytical study to clearly state which variable
is the dependent and which variable are the
• For example, in a study of the relationship between
smoking and lung cancer,
• 'suffering from lung cancer' ,would be the dependent
• 'smoking' (varying from not smoking to smoking more
than three packets a day) the independent variable.
• Note that if a researcher investigates why people
• 'smoking' is the dependent variable, and
• 'pressure from peers to smoke' could be an
• While In the lung cancer study ' smoking' was the
• For example, to test a hypothesis that eating
carrots improves vision?
• eating carrots is the independent
variable. Each subject’s vision would be
tested to see if carrot eating had any effect.
• vision is the dependent variable.
• The subjects assigned to eat carrots are in the
experimental group, whereas subjects not
eating carrots are in the control group.
3- confounding / or Interfering
• Confounding variables are those that vary
systematically with the independent variable
and exert influence of the dependent variable
. Confounding variables are not the principal
interest in the study. They distorts the result
of the study
• For example, not using counselors with similar
levels of experience in a study & then
comparing the effectiveness of two counseling
4- Controlled Variable
• a variable that is not changed, is also called constants
• For Example: Students of different ages were given the
same problem to solve. They were timed to see how
long it took them to finish the exercise.
• in this investigation the independent variable?
• Ages of the students( Different ages were tested by the
• the controlled variable?
• same problem to solve
• It would not have been a fair test if some had an easy
problem to solve and some had a harder problem to
• The temperature of water was measured at
different depths of a pond.
• Independent variable – depth of the water
• Dependent variable – temperature
• Controlled variable – thermometer
5-Quantitative variables &Qualitative
• 1- Qualitative variables(categorical) : are
measured and assigned to groups on the basis
of specific characteristics .Not expressed
numerically. E.g. Sex, ethnic group, Socio-
economic status, and outcome of disease
• 2- Quantitative variables(Continuous) :
Expressed numerically. E.g. Age, height,
weight blood pressure, Continuous variables
are measured on a scale that theoretically can
take on an infinite number of values
Scales of Measurement
• Measurement is the foundation of any
• Data comes in various sizes and shapes and it
is important to know about these so that the
proper analysis can be used on the data.
• There are usually four scales of measurement
that must be considered:
• 1-Nominal or classificatory scale
• classification data, e.g. m/f
• no ordering, e.g. it makes no sense to state that M > F
• enables the classification of individuals, objects or
responses into subgroups based on a common/shared
property or characteristic.
• A variable measured on a nominal scale may have one,
two or more subcategories depending upon the extent
• For example, ’water’ have only one subgroup, whereas
the variable “gender” are classified into two sub-
categories: male and female.
• The sequence in which subgroups are listed makes no
difference as there is no relationship among subgroups.
2- Ordinal or ranking scale
• Ordered but differences between values are not exactly
• Besides categorizing individuals, or a property into subgroups
the basis of common characteristic, it ranks the subgroups in a
• They are arranged either in ascending or descending order
according to the extent a
• subcategory reflects the magnitude of variation in the
• For example, ‘income’ can be measured either
quantitatively or qualitatively using
• ‘above average’, ‘average’ and ‘below
• The ‘distance’ between these subcategories
are not equal as there is no quantitative unit
• ‘Socioeconomic status’ and ‘attitude’ are
other variables that can be measured on
• ordinal scale.
• An interval scale has all the characteristics of an ordinal
scale. In addition, it uses a unit of measurement with
an arbitrary starting and terminating points.
• For example,
• Celsius scale: 0°C to 100°C
• Fahrenheit scale: 32°F to 212°F
• The variables are ordered, constant scale are used , but
there is no natural zero(no true zero)
• Here differences make sense, but ratios do not (e.g.,
30°-20°=20°-10°, but 20°/10° is not twice as hot!)
• The ratio scale is a measurement scale where the
interval between successive points can be measured
using a defined numerical scale and
• where the zero point means absence of the
characteristic being measured.
• A ratio scale has all the properties of nominal, ordinal
and interval scales plus its own property: the zero point
of a ratio scale is fixed, which means?
• This scale it has a fixed starting point.
• The measurement of variables like income, age, height
and weight are examples of this scale.
• A person who is 40 year old is twice as old as one who
is 20 year old.
• Other e.g., height, weight, age, length
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