-Meaning -Types of variable -Levels of measurement

1. Submitted By -
MISS MEGHA MOHAN NIKAM-PATIL
B.COM, MBA

3. 1
• Meaning
2
• Types of variables
3
• Levels of measurement

4. • A variable is an object, event, idea,
feeling, time period or any other type of
category you are trying to measure.
• Attribute is a characteristic of an object
(person, thing etc).
• A variable is logical set of attributes.
Example : The variable gender has two
attributes
male and female.

5. Independ
ent
variables
Depende
nt
variables
Moderatin
g
variables Intervenin
g
variables
Extraneou
s
variables

6.  Independent variable is a variable that stands
alone and isn’t changed by the other variables
you are trying to measure.
 Example : Age.
 It is something that depends on other factors.
 Example : Test score.

7. Independent Variable Dependent
Variable
Education
Age
Experienc
e
Knowledg
e
Salary
Example :

8.  A moderating variable is one that has a strong
contingent effect on the independent variable-
depend variable relationship. That is, the presence
of a third variable (the moderating variable)
modifies the original relationship between the
independent and the dependent variable.
 Example : A strong relationship has been
observed between the quality of library facilities
and the performance of the students.
 The intervening comes between the independent
and dependent variables and shows the link or
mechanism between them.

9. Intervening
Variables
Independe
nt
Variables
Depende
nt
Variables
Teaching Language Performance of
the student

10.  Extraneous variables are undesirable variables
that influence the relationship between the
variables that an experimenter is examining.
 Example : If you want to measure the relationship
between the student studying hours and exam
performances . Student intelligence play as a one
of the extraneous variable in the study.

11. The relationship of the values that are
assigned to the attributes for a variable
1 2 3
Relationship
Values
Attributes
Variable
Republican Independent Democrat
Party Affiliation

12.  Helps you decide what statistical analysis is
appropriate on the values that were assigned
 Helps you decide how to interpret the data from
that variable

13. Nominal Ordinal Interval Ratio

14.  The values “name” the attribute uniquely.
 The value does not imply any ordering of the
cases, for example, jersey numbers in
football.Even though player 32 has higher
number than player 19, you can’t say from the
data that he’s greater than or more than the
other.

15.  Ordinal scales build upon nominal scales by
assigning numbers to objects to reflect a rank
ordering on an attribute in question.
 For example, assigning ID codes 1, 2 and 3 to
represent a persons response to a question
regarding use rate: 1 = use often; 2 = use
sometimes; 3 = never use. Although order does
matter in these variables (unlike nominal scale
variables), the difference between responses is
not consistent across the scale or across
individuals who respond to the question.

16.  an interval scale, numbers are assigned to
objects such that the differences (but not ratios)
between the numbers can be meaningfully
interpreted.
 For Example :Temperature (in Celsius or
Fahrenheit) represents an interval
scale variable.

17.  Ratio scales have all the attributes of interval
scale variables and one additional attribute:ratio
scales include an absolute “zero” point.
 For example, traffic density (measured in
vehicles per kilometer) represents a ratio scale.
The density of a link is defined as zero when
there are no vehicles in a link. Other ratio scale
variables include number of vehicles in a queue,
height of a person, distance traveled, accident
rate, etc.