(1) current
level of interest in course – I chose this variable because an individual’s
interest in a course and/or subject matter could affect his or her overall
level of success.
(2) current
level of knowledge in course - I chose this variable because comprehension of
material and/or subject matter could measure varying levels that may affect
overall success.
(3) current level
of motivation to succeed - I chose this variable because altering levels of
individual motivation can be measured and might definitely affect overall
success in the course.
My expected results are that the
three predictor variables above will predict a particular value for my chosen
criterion variable. This is mainly because these three variables can be
valuable factors to determine an individual’s overall success in a course. I
also believe that the two strongest predictor variables would be one’s current
level of interest in the course and level of motivation to succeed. This is
because if a person is interested in a course, then it may peak a desire to
learn. The individual will then choose what motivational level he or she will
use to carry out the overall learning process. Since this is true, I think that
variable 3 would also be the most important of all three variables. This is
because if an individual is motivated enough to learn, then he or she can
develop the interest and knowledge that is needed to become a success.
In order to develop a multiple
regression equation that represents my criterion variable and/or an initial
hypothesis, I will first need to determine what all of the values will be. I
will acquire the predictor variable values (X) by using a 1-10 rating system
with all current students who are in the course. 10 will represent the highest
predictive value while 1 will represent the lowest. An (a) will equal the value
that represents the regression constant, (b1) (b2) and (b3) will represent
regression coefficients for the three predictor variables and (X1) (X2) and
(X3) will represent a student’s scores on the three predicted variables.
According to (Aron., Aron., Coups.
2009. p. 506), (Y) or the criterion variable “is the regression constant, plus
the regression coefficient for the first predictor variable multiplied by the
person’s score on the first predictor variable, plus the regression coefficient
for the second predictor variable multiplied by the person’s score on the
second predictor variable, plus the regression coefficient for the third
predictor variable multiplied by the person’s score on the third predictor
variable.” Once, all of this data is provided, the completed equation will then look like this:
“Y or predictive
success = a + (b1)(X1) + (b2)(X2) + (b3)(X3).”
After reviewing the above equation
with all pertinent data applied, a predicted value for the criterion variable
can then be determined.
Reference:
Aron, Aron, & Coups (2009). Statistics for Psychology. Pearson/Prentice-Hall.