Understanding data can be complicated.
Unlimited sets of numbers and words here and there that can be interpreted in unlimited ways.
The best way to give sense to your data is by correlating it. It sounds complicated, right? No worries, you will not do that with a pen and paper.
Luckily for you we are in 2021 and correlation matrixes exist!
What is correlation?
First things first! Before we start digging into the meaning of the correlation matrix, let’s start by understanding correlation.
The term correlation is used in various fields such as biology, electronics, or the field of statistics. What the correlation has in common in these different sectors is that we are always talking for a connection between two elements.
In a nutshell, correlation represents the degree to which two variables are connected to one another.
What is a correlation matrix?
The correlation matrix or correlation table is an analysis tool that brings together correlation coefficients between an x-axis and a y-axis where we find different variables.
The correlation matrix can lead to 3 different results:
- A positive correlation: The two variables or elements move in the same direction, so there is a link between these two variables.
- A neutral correlation: There is no link between the two variables.
- A negative correlation: The two variables move in the opposite direction.
The advantage of using a correlation matrix is that it allows you to have a global view of the more or less strong relationship between several variables.
Advantages of the correlation matrix
- The correlation matrix makes the absence or presence of a relationship between two variables clear. This makes it more relevant.
- The correlation matrix helps to predict the evolution of the relationship between the variables.
- The correlation matrix allows you to have a global view of the more or less strong relationship between several variables.
- It’s always easier to understand the data when it’s presented in a visual way
Correlation matrix = Correlation table reports in Feedier
Correlation Table Reports in Feedier gives you the ability to compare your satisfaction across different variables such as locations, departments, cohorts, or any other variable that important for you with a sample report.
Using our existing Context Attributes features, you can compare any 2 context attributes and see the relationship between them mapped out in a visual way.
Why this feature is so powerful is because whether you have 100 feedbacks or 100,000, you will be able to compare data across multiple variables easily and get to the root cause of your experience issue.