Page 193 - Quantitative Data Analysis

Quantitative Data Analysis
Simply Explained Using SPSS

Explanatory and Predictive Variables

The prediction can be based on one or many related
variables. For instance, student’s achievement can be predicted by
one or many variables like students attendance, hours of study,
teacher’s experience or student’s grads in their previous class. On
the other hand, the explanation is the amount that is used for
prediction. Explanation is about understanding relationships and
why certain things happen and other things not. For example, if we
have one predictor variable of GRE score and one criterion variable
college GPA. On the set of data, we can predict students GPA by
considering his/her GRE score.

One can make prediction without its explanation and one
can explain phenomena without prediction. An example of a study
where a prediction without explanation is one, that students’
attendance influence academic performances (Oghuvbu, 2010).
That study can predict students’ performance by their attendance
but in general attendance does not explain students’ academic
performance. While in explanatory research, the researcher finds
the explanatory variables rather than predictive variables. For
example, parent’s income, parent’s education, and teachers’
teaching methodology explains students’ academic performance.

Suppressor Variable

Suppressor variable is defined as when two independent variables
have zero correlation or (weakest collection) but it correlates with
the dependent variable. Suppressor variable is important in
regression analysis. It tells that analysis of a correlation matrix is not
sufficient to tell the value of a variable in a regression equation.
Moreover, suppressor variable tells how strong or weak predictors

The Theory and Applications of Statistical Inferences 177