Titanic has become one of the most famous ships in history. It was very sad and tragic that the “unsinkable” ship was built in three years but sank within three days after its departure. Passengers were from the wealthiest people in the world to the middle class to emigrants.
Many times, researchers are interested in predicting categorical variables or specific outcomes. An example of a categorical variable, would be students passing an exam (e.g., pass or fail). In this case of passing an exam, there would be two categories, either students pass or fail an exam. In situations like this, researchers may be interested in determining specific factors that predict or explain categories within a categorical variable of interest. Using our example of passing an exam, researchers may be interested in determining the factors that most influence whether a student passes or fails an exam. Time spent studying, previous exam scores, and time spent working a tutor are all examples of variables that might influence whether or not a student passes an exam.
Cholesterol is a very importance substance in our body for digesting foods, producing hormones, and generating Vitamin D. This blog includes an analysis of an cholesterol dataset retrieved from MASH at The University of Sheffield. This dataset contains a mixture of Between-Subjects (type of margarine) as well as within-subjects factors (length of intervention). This leaves us room to make many comparisons but we will begin with the most straightforward comparison of whether participation in these interventions lead to a change in cholesterol.
Another method of inspecting the data is to use an Analysis of Variance. As with all analysis, the appropriate model depends on the question you are asking. To begin, we will ask “do the levels of our independent variable have an effect on our dependent variable?”.
So far all of our analyses have asked questions about the manipulation of a single independent variable. The t-test can compare two groups/levels while the ANOVA can ask about the differences between multiple levels. But, what do we do when there is more than one independent variable being manipulated within our experiment? What if we want to know how these factors interact with each other to produce our final result? To demonstrate how you could assess these questions we’ll again go through our Cholesterol.csv dataset.