Agriculture Reference
In-Depth Information
5.1.4 Independent Variable
conditions of the road, and conditions of the
car. Thus, factors like age, gender, academic
background, conditions of the road, conditions of
the car, etc., are the extraneous factors/variables in a
relational study of driving performance and salary.
Extraneous variables are those variables whose
information is obtained from the outside a periphery
of study. Generally the values of these variables are
not directly obtained from the system under study,
but these variables may affect the dependent/
response/predicted variables. Extraneous variables
may again be of two types: (a)
In any relational analysis, variables which help to
predict the dependent variable using the func-
tional relationship are known as independent
variables. In the above example, in Sect.
5.1.3
,
x
1
...
x
8
are said to have no association among
themselves and are termed as independent
variables. These variables independently help in
predicting the dependent variable. Though there
is no silver lining between the predictor variable
and the independent variables, generally in
regression analysis, predictor variables are syn-
onymous to independent variables. But it is not
necessary that the predictor variable must be an
independent once.
participant variables
and (b)
. Participant variables
are extraneous variables which are related to the
individual characteristics of each participant. Thus,
in the above example, age, gender, and academic
background are the participant variables. On the
other hand, situational variables or extraneous
variables are related to environmental factors like
conditions of the road and climatic conditions etc.
Generally under experimental research conditions,
extraneous variables are controlled by researchers.
These are more pertinent in the case of a system of
simultaneous equations model; the information
about these variables is taken from the outside of
the system to solve the model.
situational variables
5.1.5 Explanatory Variables
Independent variables are sometimes known as
explanatory variables. Any variable which
explains the response/dependent/predicted vari-
able is known as explanatory variable. In a sim-
ple regression analysis, there are only one
predictor and one response variable. In a multiple
regression analysis generally, there is one
response or predicted variable with more than
one predictor/explanatory/independent variables.
In the case of system of simultaneous equations
model, there may be more than one response
variable and more than one predictor/indepen-
dent/explanatory variable. Moreover, the res-
ponse variable(s) in one equation may be the
explanatory variable in the other equation.
5.1.7 Stimulus Variable
The idea of stimulus and response variables is
familiar in agriculture, socioeconomic, and clini-
cal studies. A stimulus is a type of treatment
applied to the respondents to record their res-
ponse. In clinical studies generally, the doses,
concentrations, different chemicals, etc., form a
stimulus, whereas the response may be in the form
of
or quantitative response.
When a stimulus is applied to a record response,
the response may be either-or type, or it may be
measurable. In an either-or type of response, a
respondent will either respond or not respond
after being applied with the stimulus. The differ-
ent concentrations of a particular chemical in
controlling a particular pest of a particular crop
may kill the pest or may not; the response is either
to kill or not to kill. On the other hand,
applications of insulin at a particular dose can
quantal response
5.1.6 Extraneous Variable
In a relational analysis, independent and dependent
variables are not only the variables present in the
system; some other variables known as extraneous
variables may have an impact on the relationship
between the independent and dependent variables.
For example, the relationship between the perfor-
mance in driving and salary may be influenced by
factors like age, gender, academic background,
