1. Variable and scale
Chapter 6

2. Variables
• An image, perception or concept that is
capable of measurement – hence capable
of taking onddifferent values – is called a
variable. In other words, a concept that
can be measured is called a variable.
• According to Kerlinger, ‘A variable is a
property that takes on different values.

3. The difference between a
concept and a variable
• Measurability is the main difference
between a concept and a variable.
• Concepts are mental images or
perceptions and therefore their meanings
vary markedly from individual to individual,
whereas variables are measurable,
though, of course, with varying degrees of
accuracy.

4. Concepts Variables
Effectiveness
Satisfaction
Impact
Excellent
High achiever
Self esteem
Rich
Domestic violence
etc
Gender (male / female
Attitude
Age (X year)
Income ( Rs…)
Weight( -----kg)
Height (---- cm)
Religion
etc
If you are using a concept in your study, you need to
consider its operationalisation – that is, how it will be
measured. In most cases, to operationalise a concept you
first need to go through the process of identifying
indicators – a set of criteria reflective of the concept –
which can then be converted
into variables.

5. Converting concept into variable

6. Types of variable
• A variable can be classified in a number of
ways. The classification developed here
results from looking at variables in three
different ways
• the causal relationship;
• the study design;
• the unit of measurement.

7. In studies that attempt to investigate a causal
relationship or association, four sets of variables
may operate
1. change variables, which are responsible for
bringing about change in a phenomenon,
situation or circumstance;
2. outcome variables, which are the effects,
impacts or consequences of a change variable;
3. variables which affect or influence the link
between cause-and-effect variables;
4. connecting or linking variables, which in
certain situations are necessary to complete the
relationship between cause-and-effect variables.

8. • In research terminology, change variables
are called independent variables,
outcome/effect variables are called
dependent variables, the unmeasured
variables affecting the cause-and-effect
relationship are called extraneous
variables and the variables that link a
cause-and-effect relationship are called
intervening variables. Hence:

9. 1. Independent variable – the cause supposed to be
responsible for bringing about change(s) in a phenomenon
or situation.
2. Dependent variable – the outcome or change(s) brought
about by introduction of an independent variable.
3. Extraneous variable – several other factors operating in a
real-life situation may affect changes in the dependent
variable. These factors, not measured in the study, may
increase or decrease the magnitude or strength of the
relationship between independent and dependent variables.
1. Intervening variable – sometimes called the confounding
variable (Grinnell 1988: 203), it links the independent and
dependent variables. In certain situations the relationship
between an independent and a dependent variable cannot
be established without the intervention of another variable.
The cause, or independent, variable will have the assumed
effect only in the presence of an intervening variable.

11. Types of variable in a causal
relationship

12. Independent, dependent and
extraneous variables in a causal
relationship

13. Categorical/continuous and
quantitative/qualitative variables

14. Measurement and scale
• Types of measurement scale
The most widely used classification of
measurement scales are:
(a)nominal scale;
(b) ordinal scale;
(c) interval scale; and
(d) ratio scale.

15. Nominal scale :
• Nominal scale is simply a system of
assigning number symbols to events in
order to label them. Nominal scale is the
least powerful level of measurement. It
indicates no order or distance relationship
and has no arithmetic origin. A nominal
scale simply describes differences
between things by assigning them to
categories. Nominal data are, thus,
counted data.

16. Ordinal scale:
• The lowest level of the ordered scale that is
commonly used is the ordinal scale. The ordinal
scale places events in order, but there is no
attempt to make the intervals of the scale equal in
terms of some rule. Rank orders represent ordinal
scales and are frequently used in research relating
to qualitative phenomena.
• Since the numbers of this scale have only a rank
meaning, the appropriate measure of central
tendency is the median. A percentile or quartile
measure is used for measuring dispersion.
Correlations are restricted to various rank order
methods. Measures of statistical significance are
restricted to the non-parametric methods.

17. Interval scale:
In the case of interval scale, the intervals are adjusted in terms
of some rule that has been established as a basis for making the
units equal. The units are equal only in so far as one accepts the
assumptions on which the rule is based.
Interval scales can have an arbitrary zero, but it is not possible to
determine for them what may be called an absolute zero or the
unique origin.
The primary limitation of the interval scale is the lack of a true
zero; it does not have the capacity to measure the complete
absence of a trait or characteristic.
Interval scales provide more powerful measurement than ordinal
scales for interval scale also incorporates the concept of equality
of interval. As such more powerful statistical measures can be
used with interval scales. Mean is the appropriate measure of
central tendency, while standard deviation is the most widely
used measure of dispersion. Product moment correlation
techniques are appropriate and the generally used tests for
statistical significance are the ‘t’ test and ‘F’ test.

18. Ratio scale:
Ratio scales have an absolute or true zero of measurement. The
term ‘absolute zero’ is not as precise as it was once believed to
be. We can conceive of an absolute zero of length and similarly
we can conceive of an absolute zero of time.
Ratio scale represents the actual amounts of variables.
Measures of physical dimensions such as weight, height,
distance, etc. are examples. Generally, all statistical techniques
are usable with ratio scales and all manipulations that one can
carry out with real numbers can also be carried out with ratio
scale values. Multiplication and division can be used with this
scale but not with other scales mentioned above. Geometric and
harmonic means can be used as measures of central tendency
and coefficients of variation may also be calculated.