Independent and dependent variables are key concepts in experiments. An independent variable is manipulated to observe its effect on a dependent variable. For example, in a study of test performance, revision time and intelligence were independent variables that may affect the dependent variable of test score. A moderating variable influences the relationship between an independent and dependent variable. An intervening variable facilitates understanding this relationship by showing the link or mechanism between them. Continuous variables can have any value within a range, while discrete variables take values from a limited set of possibilities.

1. CONCEPT: VARIABLE
• A variable is anything that can take on differing or
varying values.
• The values can differ at various times for the same
object or person, or the values can differ at the
same time for different objects or persons.
• Examples are exam scores, absenteeism, and
motivation

2. Dependent and Independent Variables
An independent variable, sometimes called an experimental or predictor variable, is a variable that is
being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes
called an outcome variable.
Imagine that a tutor asks 100 students to complete a math’s test. The tutor wants to know why some
students perform better than others. While the tutor does not know the answer to this, she thinks that
it might be because of two reasons: (1) some students spend more time revising for their test; and (2)
some students are naturally more intelligent than others. As such, the tutor decides to investigate the
effect of revision time and intelligence on the test performance of the 100 students. The dependent and
independent variables for the study are:
Dependent Variable: Test Mark (measured from 0 to 100)
Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score)
The dependent variable is simply that, a variable that is dependent on an independent variable(s).

3. Moderating Variables
A moderating variable is one that has a strong effect on the independent
variable-dependent variable relationship. That is, the presence of a third
variable (the moderating variable) modifies the original relationship
between the independent and the dependent variable.
For example, a strong relationship has been observed between the quality
of library facilities (X) and the performance of the students (Y).
Although this relationship is supposed to be true generally, it is
nevertheless contingent on the interest and inclination(desired to do
something) of the students. It means that only those students
who have the interest and inclination to use the library will show
improved performance in their studies.
In this relationship interest and inclination is moderating variable i.e.
which moderates the strength of the association between X and Y
variables

4. 4. Intervening Variables
An intervening variable facilitates a better understanding of the relationship
between the independent and dependent variables when the variables appear
to not have a definite connection
A basic causal relationship requires only independent and dependent variable. A third
type of variable, the intervening variable, appears in more complex causal relationships.
It comes between the independent and dependent variables and shows the link or
mechanism between them. Advances in knowledge depend not only on documenting
cause and effect relationship but also on specifying the mechanisms that account for the
causal relation.
A theory of suicide states that married people are less likely to commit suicide than
single people. The assumption is that married people have greater social integration
(e.g. feelings of belonging to a group or family). Hence a major cause of one type of
suicide was that people lacked a sense of belonging to group (family). Thus this theory
can be restated as a three-variable relationship: marital status (independent variable)
causes the degree of social integration (intervening variable), which affects suicide
(dependent variable). Specifying the chain of causality makes the linkages in theory
clearer and helps a researcher test complex relationships.

5. continuous variable
A continuous variable can be of any value or number between two specific values or
numbers. For example, if you lined up all the students in a classroom to see how many
students were between 4 feet 6 inches tall and 6 feet 4 inches tall, you would have a set
of continuous variables. A student height can take any value, as long as it is between
the shortest height and the tallest height specified.
For example, the weight, height, and age of respondents in a survey would represent
continuous variables; in industrial or medical applications, survival/failure times are
also continuous variables
Discrete variables
Discrete variables are also called categorical variable A discrete variable takes a value
from a small or finite set of conditions or states. The dice from a board game is one
example of a discrete variable. Dice have a finite set of only six numbers: 1, 2, 3, 4, 5,
and 6. There cannot be a side with 4.5 dots.
However, a person's gender, occupation, or marital status are categorical or discrete
variables: either a person is male or female, never married, married, or divorced, etc.