Why the author used one-way ANOVA test
According to Heiberger & Neuwirth, (2009). The one-way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups . They also note that, it is important to realize that the one-way ANOVA is an omnibus test statistic and cannot tell you which specific groups were significantly different from each other; it only tells you that at least two groups were different.
In this article, the author intends to compare student success in math courses that are offered in three modules. The modules are online, blended and face to face. Since means of three different groups are being compared, the author opts for the One-way ANOVA.
Most appropriate choice
Yes it is. Mark & Wilson, (2001) suggests that there are three common and appropriate tests of comparing means for a group with more than one sample. They are the two sample t-test, one-way ANOVA and two-way ANOVA. A two sample t-test and two-way ANOVA are the majorly used tests for investigating a significance difference when we have more than one sample. However, the t-test is only applicable when we have two and only two factors or samples. For the two way ANOVA, it compares the mean differences between groups that have been split on two independent variables (called factors). The primary purpose of a two-way ANOVA is to understand if there is an interaction between the two independent variables on the dependent variable.
In this study the research was interested and only had three samples that he wanted to compare. This means neither the two sample t-test nor the two-way ANOVA tests would be appropriate. The one-way ANOVA is best suited for this study and thus we can conclude it was the most appropriate test used in the article.
Display of data
Yes. Khan, (2016) argues that, data ought to be displayed in a form that is easily comprehensible by a lay reader. In this study, the data was displayed in tables. The first table titled demographics showed the frequencies of each module and the demographics of each which included gender, race and age. One-way ANOVA tables were then displayed on the data analysis segment to show the findings of the study. These tables were presented in a way that they are clear and exact in detail.
The results in the ANOVA table Stand alone. This is because the results were found to be significant. The researcher found out that out of the three study modules, students that took the face to face module scored highest while those who took the online module scored the least. The blended module students scored in between the face to face and the online module. The P-value of ANOVA was less than 0.05 and thus it would be concluded that study was significant. The results can be well utilized in the education sector to help put measures in place for improving content delivery to students using the online and blended modules.
Since the a large sample size was used and an appropriate test was conducted for the data and all underlying assumptions of a one-way ANOVA were meet, the results can be termed as reliable and generalized for the entire population
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