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It is used to determine if there is an interaction effect between 3 independent variables on continuous dependent variables.
These are called between subjects’ factors and three-way ANOVA is also called factorial ANOVA. There can be other factors in it which can be split into multiple levels.
The procedure makes use of many different types of assumptions about the data to meet the requirement of the method to give you a valid outcome.
It can happen that the data does not meet the assumptions at some point when conducting the evaluations - there are certain methods used in the technique to transform it, or to use an alternative figure, to overcome it.
It is widely used in gathering and analyzing health-related or medical data.
For example - a specific type of physical exercise can be recommended to different groups of people categorized based on gender (male or female), age and body weight, and the method can be used to produce a relationship between the three factors.
There are six assumptions made in such statistical calculations are –
Firstly, the dependent variables are measured at a continuous level or specific interval level or ratio variable.
Secondly, the three variables should be consistent and belong to categorical unrelated groups (like male and female).
There should be no relationship between the observations related to each group.
There should be no significant outliner (outliner means the figures that do not follow the usual pattern).
The dependent data is normally distributed for each group of 3.
There should be homogeneity with the involved factors for the combination of the groups of the 3 independent factors.
Similar theories can be applied in financial risk management or to get commodity substitution bias or to know the best thing to do with savings etc.
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