Introduction
There has been mixed reaction on the determinants of GPA scores among students. Several groups have argued that the number of hours of study affect the GPA score attained by a student. Therefore, they suggest that students should study for more in order to improve their GPA scores. On the other hand, other groups maintain that the number of hours students spend studying do not affect the GPA scores of students.
I think the number of hours study is a significant determinant of GPA. This is because from experience, I have noticed students who spend more hours studying tend to have a higher GPA. Other factors that could affect GPA are; the number of units the student is undertaking, participating in extra-curricular activities, motivation and age.
Study Question
The study question will be;
Is the number of hours of study a significant determinant of GPA Scores?
Hypotheses
Null hypothesis (H0):
The number of hours spent studying is not a significant determinant of GPA Scores
Alternate hypothesis (HA):
The number of hours spent studying is a significant determinant of GPA Scores
Definition and measurement of variables
The dependent variable will be GPA scores.GPA is an ordinal variable. The independent variable for this study will be the hours spent studying. The number of hours spent studying is a measure of time. Therefore, the number of hours of study is a scale variable.
The unit of observation was students.
Data type and source
This research will use primary and secondary data. Data regarding the number of hours spent studying will be collected from primary sources. Survey method will be used to collect data pertaining to the number of hours studied directly from students. Data regarding the GPA scores will be collected from secondary sources. Academic records of all students will be used to extract the GPA scores of the sampled students.
Model
The GPA scores of a student is determined by number of hours of study. This will be illustrated with the model below;
Y= αX + e
Where;
Y represents the dependent variable which is the GPA score attained by a student
X represents the independent variable which is hours that a student studied during the semester
α is the coefficient of the independent variable
e is the error term
Heteroskedasticity
I anticipate problems with the error term because the students are in different subgroups in terms of gender, race, and religious affiliation among others. Therefore, the variables maybe heteroskedastic White test will be used to test for non-constant variance of error in the regression model.
Data analysis
Regression analysis will be used to analyze the data collected. Regression analysis will evaluate the nature of the correlation between GPA scores and the number of hours study. It will also ascertain the explanatory power of the model. T-tests for the regression slope were used to ascertain whether the coefficient of the independent variable is statistically significant. The p-value that will be obtained from the t-test for regression will be compared with the p-critical at 5% significance level (0.05). If the p-value that will be obtained will be higher than the 0.05, then the independent variable will be statistically significant. However, if the p-value will be lower than the 0.05, the independent variable will not statistically significant.
Conclusion
The explanatory power of the model will be used to determine whether the model can be used to make predictions about the GPA scores of students. The result of this study will be relevant to education consultants, education policy makers, education officials, teachers and students.
