There’s no denying it: beginners who have just entered the business world may find most of the topics in the field a bit confusing, such as the various types of variables — which is completely understandable. Some topics are easier than they seem, and over time, you’ll master them.
Variables are information or characteristics that you want to measure in a given group of people during market research or data analysis . Therefore, in this post, we will explain in a simple way what variables are, their different types and series, in addition to showing how these concepts can be essential for your success. Check it out!
Types of variables in data analysis
The concept of variables is simple, but it can be scary for some people, as they originated in the field of Statistics. To understand how they work, it is necessary to know that there are two groups of variables: quantitative and qualitative. Check them out:
What is a quantitative variable?
The quantitative variable was created to explain numerical quantities and is the specific value or characteristic of a variable that is sought to be analyzed in a study. It is divided into 2 groups:
1. Discrete variable
This group corresponds to numbers without decimals, that is, whole or natural numbers. The discrete variable is only used when the person wants to know the exact value of something. Example:
How many bathrooms does your house have? Answer: 3.
2. Continuous variable
In the continuous variable group, the numbers correspond to decimals , that is, they are separated by commas. Below is an example of a question for this group:
How tall are you? Answer: 1.86.
What is a qualitative variable?
Technically, qualitative variables are represented by words and worked with a quality, being the opposite of what was seen previously with the other group. Here, it is also divided into 2 groups:
1. Nominal variable
There is no hierarchy between possible categories, such as gender, age, marital status, or who comes last and who comes first . Example:
There is no difference between someone who has a disease and someone who does not have a disease.
2. Ordinal variable
This type is a variation of the previous one, since it hierarchizes an order according to the characteristics of the group. Organizing in this way is great for allowing a deep level of applied study and makes it easier to detail market research in the near future. Example:
Individuals’ titles: no degree, graduate, master, doctor and post-doctor.
3. Dichotomous variable
A dichotomous variable is classified as a correspondence between only one answer and two alternatives — in this case, the person responding cannot choose more than one option.
4. Polytomous variable
This type of variable is completely different from the previous one. Here, there are several options for just one answer. Check out an example:
Is your car brand Fiat, Toyota, KIA, Hyundai ? Answer: Toyota.
What are the types of data series and analysis?
For accurate data analysis, it is important that the types of series are taken into account, namely categorical, geographic and temporal:
- Categorical series: this series represents statistics that can be separated into specific categories, such as market sectors and product types;
- Geographic series: as the name suggests, this group covers different geographic locations, such as data by location;
- Time series: With monthly or annual sales, a time series is one in which data is collected and recorded at regular intervals of time.
Other types of variables
There are other types of variables, but it is essential to know that the most common variables are classified as qualitative and quantitative, as seen previously. For greater understanding, a classification can also occur with other types of variables that are not under analysis. Understand:
1. Independent variable
This type of variable may undergo changes throughout the study and is related to factors that can be controlled by the market, such as the price of products, the implementation of promotions and the contribution of elements to the result .
2. Dependent variable
Dependent variables are the result of independent variables. Examples of this include increased purchase conversion, large number of transactions, profit and inflation.
3. Moderating or mediating variable
Acting between the independent and dependent variables, the moderating variable ends up being influenced by these two classes. For example, this type of variable is used when the objective of the study is to better understand the customer’s attitude, as well as the intentions behind their purchase.
How important is it to correctly classify variable types?
Firstly, being able to correctly identify the type of variable in focus and its series is essential to execute precise data analysis techniques and ensure that your establishment has market intelligence .
This identification has a positive impact on all stages of the process, from data collection and validation to the choice of modeling techniques and presentation of results through clear and effective visualization.
Secondly, the relationship between series and variables means that identifying patterns and trends in the data can provide insights for important decision-making . When there is an understanding that these two groups are interconnected, it is possible to use specific techniques to take the efficiency of data analysis to another level. In short:
- Perform good data visualization, modeling and processing techniques;
- Ensure that the popularity and accuracy of data analysis happens;
- Extract the most important insights and make the most of available data to ensure accurate decision-making.
Mastering variable types and series: why is it so important?
To obtain effective and consistent data analysis, it is essential to master the types of variables and series. These two practices are essential for relevant and profitable results. With the expertise to know how to extract important insights for the business, the reward in the decision-making of organizations will be a success.
Understanding the different types of variables and series can make your data analysis more successful. Identifying and applying the types of variables is essential for performing detailed analyses and understanding more accurately the trends and patterns that appear in the data.
