In this summary, Mouritsen and Jones' (2012) article on ANOVA analysis of student daily test scores in multi-Day Test Periods is discussed through the application of data analysis. Thus, this article is aimed at establishing the reason why average test scores of apprentices that take exams at the end of the multi-day test period are lower as compared to learners who take the assessment during the earlier periods of testing.
Section1: Data File Description
In the study, the data which is analyzed is the results of students that sit for exams for multi-day testing of two courses that are examined by two different lecturers for several semesters (Mouritsen & Jones, 2012). The test period taken is of 4 days, in which in this period the students were expected to choose when to take the tests during this period.
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Section 2: Testing Assumptions
The assumptions that are made for this model using one-way fixed effects ANOVA are; there is mutual independence concerning individual observations, that the data that is provided adhere to an additive statistical model that is comprised of fixed effects and random errors, the random errors from the data are normally distributed and that the random errors in the data have homogenous variances.
Section 3: Research Question, Hypotheses, and Alpha Level
The reason for this research was based on the lecturers being concerned with why the students who sit for tests later in the assessment period in a multiple-day test perform poorly even though they are considered to have a benefit over the scholars who take the exams early in the examination retro which may be as a result of information leakage. Thus, this prompted to use of one-way ANOVA analysis which find out that the mean test scores of the students who take exams late in the multiple-day test period decreases gradually (Mouritsen & Jones, 2012).
In determining the mean test score difference throughout 4 days test, the ANOVA model has a null hypothesis that states that " there is no overall mean test score difference between test days exists." The research hypothesis however, states that “there is an overall mean test score difference between the test scores”
Section 4: Interpretation
If the null hypothesis is failed to be rejected, then the mean scores results acquired from the research do not vary based on which day the students sat for the exam. In the case where the null hypothesis is rejected, the alternate hypothesis is ultimately accepted. This therefore means that the test scores differ based on the day of the test period. In this article, the null hypothesis was rejected and hence there is statistically significant difference in the average test results for the period of the 4-day test. This is evident in Exhibit 3 of the article, from the ANOVA procedure which displays great differences between mean test scores. Moreover, the F value is larger than the F critical which therefore means that we reject the null hypothesis. In which the more the number of students that are taking exams increase from the second day to the fourth day, descriptive statistics shows that the standard deviation increases, which means there is more variation. This is to be interpreted as each day results in different exam results due to the differences in each day.
Section 5: Conclusion
ANOVA procedures are suitable for testing homogenous variances, and since the data which is generated results to non-homogenous variances, the best method for analyzing this kind of data is the use of the Brown-Forsyth test analysis. This is evident in exhibit 4 in which Brown Forsyth's results show statistical differences for the period of 4 multi-day results account for the unequal variances and an unequal number of students who took the test each of the days.
References
Deniz, M., Tras, Z. & Adygan, D. (2009). An Investigation of Academic Procrastination, Locus of Control, and Emotional Intelligence. Educational Sciences: Theory & Practice, 9(2), 623-632.
Mouritsen Mathew, Davis Jefferson & Jones Steven. (2012). ANOVA Analysis of Student Daily Test Scores In Multi-Day Test Periods. Journal of Learning in Higher Education. Volume 12 isuue2, 73-82.
