Struggling to find out the relationships between the collected data samples? If yes, then using the two famous techniques, bivariate and multivariate analyses, is the solution to your struggle. Bivariate analysis is the technique that looks at two variable issues. However, the multivariate analysis looks at two or more variables. From this, you can infer that both are different analysis techniques. Is it the only difference between them? Not at all. There are a lot of differences between these two data analysis techniques. In today’s article, we will go deeper into those differences. Before that, let’s define both of them.
What is bivariate analysis?
The bivariate analysis technique is the technique that investigates the relationship between two variables. The variables of the study could be dependent or independent. This analysis’s most visual technique is the plotting of a scatterplot, where you plot an x-value against a y-value. Along with finding the relationship between variables, this analysis also gives a measure of the strength of the correlation that exists between them.
What is multivariate analysis?
Multivariate analysis is the technique by which you examine more than two variables. The word “multi” explains the situation pretty well. This word itself means that there will be various variables in the study that you will be analysing. To analyse the relationships in the case of three variables, you can use a 3-D model. However, if you have more than three variables, then you need some specialised software to do the analysis and develop the relationship.
Differences between bivariate and multivariate analyses
The definitions given above must have given you an initial idea of bivariate analysis and multivariate analysis. However, there does not exist only a difference in their definitions. The differences between these two analysis techniques go beyond their definitions. Hence, a brief description of the dissimilarities is as follows:
1. The difference in the analysis method
The first difference between bivariate and multivariate analysis lies in their performance. Which method is suitable for bivariate analysis, and which is suitable for multivariate? As far as bivariate analysis is concerned, the most commonly used analysis methods are t-tests and chi-squared tests. These tests can effectively give a true representation of the relationship between the variables. On the other hand, multivariate analysis employs multiple regression and multivariate analysis of variance as the analysis methods. However, you cannot perform these tests, do not waste time and visit any dissertation writing service.
2. The difference in the data representation
You have performed the analysis and got the results; what is next now? Yes, you are right. The next step is to present the data in an organised way to your audience. What is the way to present the information correctly? Well, in the case of bivariate analysis, it is making a scatterplot. The graphical method is also used to show the relationship. In the meantime, being three-dimensional data, multivariate analysis results make use of 3-D models. If you do not know how to make a 3-D model, don’t hesitate to take help from dissertation writers UK.
Pros and cons of bivariate & Multivariate analyses
After having discussed the top differences in both analysis techniques, let’s now look at their pros and cons. Hence, a brief description of the pros and cons is as follows:
Pros of bivariate analysis
- It helps you determine to what extent predicting the value of the dependent variable is easier based on the independent variable.
- It also allows you to determine the strength of the relationship that exists between the study variables.
- The bivariate analysis usually helps to determine the hypothesis of casualty and association.
Cons of bivariate analysis
- This analysis technique does not tell anything about how one variable is influencing the other in the study.
- It does not give a detailed description of the relationship that exists between the variables.
Pros of multivariate analysis
- The first benefit of multivariate analysis is that it allows the researchers to quantify the relationship between the variables.
- Using cross-tabulation or partial correlation methods, researchers can control the association between variables.
- It gives a more realistic picture of the relationship than bivariate analysis.
Cons of multivariate analysis
- The first and most important con of this analysis is that it is a hectic, complex, and very arduous analysis technique. Dealing with more than two variables is not easy.
- The results of this analysis are not always for students to interpret. Therefore, the difficulties in the interpretation of the results add to its cons.
Conclusion
Conclusively, bivariate analysis and multivariate analysis are the two different techniques used to examine the relationships between two or more than two variables. Their analysis methods are different, and their ways of representing the results are also different. So, whenever you think of performing these analyses, do not forget to remind yourself of the differences.
