The two-way ANOVA is also called the two-factor analysis of variance. It is used in several fields of activity and is a strategic analysis that allows a better understanding of the complexity of certain data.
Two-way ANOVA: definition
The two-way ANOVA method is a technique that falls within the scope of statistics. It makes it possible to study two different factors in a field and to determine whether there are common points, interferences between these two factors. The main advantage of this method is that it can be used in many fields and is quite accurate.
Unlike the single-factor analysis of variance, which also exists in the field of statistics, this one focuses on two variants that are independent.
This statistical study highlights two factors on a dependent variable and analyzes their interactions.
What is more commonly known as a dependent variable is a piece of data that is continuous, such as turnover, weight or height.
Let's take a simple example to understand:
In the field of education and more specifically in teaching, a teacher wants to try to define whether students' overall results and averages can be influenced by gender (girl/boy) and by the learning method (traditional/online).
In this case, two groups are formed, one that will learn in a traditional way and another in an interactive way. After a few months, we compare the grades obtained for boys and girls.
Several hypotheses are put forward: does the gender of the students have an influence on the averages? The same question applies to learning methods. Is it possible that there is a link between the method and the gender of the students?
Challenges and objectives of the two-way ANOVA
The ANOVA two-way statistical development method has several objectives and challenges.
First, it is a question of understanding how the starting variants can interact with the main variable.
The primary objective is to save time in analysis and in studies, which is why we take two factors simultaneously.
However, this method requires great rigor, and it has certain limitations that should not be overlooked.
It requires a large amount of data available, which can increase the time spent on basic searches, even if there is a time saving on the analysis of the results.
In addition, this method can be complex in many ways, and if the studies are not precise enough, it can lead to skewed results and conclusions.
Areas of use
ANOVA can, as mentioned above, be used in a wide range of sectors.
Science, for example, is the preferred field for this type of statistics. Medicine, but also mathematics can be conducive to this type of analysis. As we have seen above, it is possible to use it in the education and learning sector.
Companies can use the two-way ANOVA method to make a campaign more efficient and to limit the margins of error. For example, how the prices offered to consumers and the advertising carried out online on social networks can affect sales and a fortiori on the company's turnover.
When and how to use ANOVA Two-Way
This method is used when you want to compare data based on a variable. For example, can sports performance be influenced by factors such as the duration of the session and/or the time at which it is performed?
The method is used in a comparative manner. Data is first collected, such as the performance measure or the average of students in the case of learning. They are then analyzed using the two-way ANOVA statistical method and the results will be interpreted to reach a conclusion.
Therefore, it is possible to define four different steps in how to use this method of statistics.
The first step is to understand the basic question, to define it in accordance with the initial subject. We therefore identify the question and the variable factors.
We therefore collect the different data, and it is also a question of checking whether they agree with the subject and the basic hypotheses.
Software then performs the analysis, and the final step is to interpret the results.
Methodology
The initial data:
Define the main data, and the two factors on which the variable will be based.
Initially, the data must be roughly balanced, otherwise it will distort the result of the analysis.
The initial hypotheses:
The study will start from a starting postulate, which are called hypotheses.
In the example taken on learning above, one of the possible hypotheses could be that distance learning on girls in the group gives more results than on boys.
The analysis:
Using specialized software, perform the analysis, this will shed light on the different values, the interactions between the variants and the variable and the possibility of errors during the process.
The statistics are then calculated for each of the variants.
It will then be possible to interpret the results and make comparisons if the assumptions appear to be significant.
Follow-up:
Depending on the field of activity, a follow-up will be necessary, to verify that the interactions between the hypotheses are still valid over time.
Indeed, let us think of fields such as medicine, or the social sciences, which can evolve according to trends or developments in research, among other things.
Conclusion
The two-track ANOVA is a comprehensive statistical analysis that is relevant in a wide range of areas. It allows us to highlight two hypotheses on a variant and therefore extends the analysis that is done with ANOVA to a single track.
However, it is a complex analysis that requires a very structured methodology to arrive at relevant conclusions in the long term. In many areas, it is a question of monitoring the results to ensure that the initial assumptions are still valid.
In the current context, two-way ANOVA is used mainly to understand and study the interactions between variables, and to understand how certain factors will be able to interact, even if they are unrelated to each other. This method of statistics can be useful in determining why and how one factor may interact with another, having effect depending on the starting variable.
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