ANCOVA and MANOVA
ANCOVA or MANOVA
A multivariate analysis of variance (MONOVA) is a statistical procedure for comparing a the population means of several groups and when two or more dependent variables exist ("StatSoft," 2011). One of the strengths of a MONOVA is that individual p-values are produced for each dependent variable. This enables a comparison of the levels of significance of the variables, and can show interactions among the dependent variables ("StatSoft," 2011). It is also possible to determine if changes to the independent variables will create significant effects on the dependent variables ("StatSoft," 2011).
If we consider the question about the influence of course delivery methods on the academic performance of students enrolled in statistics classes, we may find a MONOVA approach to be of assistance in our analysis. Recall that the three course delivery methods are: online, face-to-face, and hybrid. The course delivery methods are the independent variable and the statistical learning of the students is the dependent variable. Generally, when the topic of a research study is student learning -- not simply performance on a single assessment -- we are interested in pre-test and post-test scores. Adding a repeated measures component to the research design can clarify the extent of learning that students achieved. Another independent variable added to this analysis is college major, and number of mathematics courses taken previously. Moreover, a second dependent variable is tutoring requests during the course, and a third dependent variable is feelings about the course (distress or confidence) as rated by a Likert scale administered as an add-on to each test. Through this addition, the MANOVA can assist the analysis of the three types of course delivery with regard to effectiveness with students at various levels of competency. With these changes -- to a MANOVA approach and the addition of another independent variable (entry level of statistics knowledge) -- we are ready to articulate the research question and hypotheses.
RQ1: How are the various course delivery methods related to students' pre- and post- statistics skills?
The corresponding null and alternative hypotheses would be:
H0: there are no discernable differences in pre- and post- statistics scores for course delivery methods
H1: there are significant differences in pre- and post- statistics scores for course...
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