Multivariate data analysis refers to the outcome or the result of analyzing different data or variants. Here, multivariate means various dependent variables that have the same outcome. For instance, if you want to find out the weather of a city throughout the year, you need complete variants to find a single result. You will figure out the precipitation, humidity, pollution, etc. This article will help you understand what techniques you can use for multivariate data analysis and its benefits.
Understanding the Concept of Multivariate Data Analysis
We can understand the concept of Multivariate Data analysis by supposing that we want to predict the sales of 2021. It would be illogical to say that a single factor affects the company’s sales for 2021. Instead, you will consider all the aspects that support the impact on sales. To analyze all the variants that affect sales, you need to rely on the multivariate analysis technique.
We already know that various elements work together to affect sales. That includes marketing techniques, geographical location, consumer’s choice, cost of the product or service, production capacity, and other similar variables. You can implement this technique in any field of study and determine results by understanding the relationship between the elements.
Pros and Cons of Multivariate Data Analysis
- When you calculate and analyze different factors instead of finding the influence of all the elements for the variable, you will get an accurate result
- You can find real results that have a real-life impact
- When you analyze every factor, you are aware of the situation as a whole
- The process is descriptive, that shows that your answer will be true and exact
- The process is detailed and time-consuming because you need to collect and observe the data of different factors
- For a satisfactory conclusion, you need to go through complex procedures and calculations
Techniques of Multivariate Data Analysis
Before you follow a multivariate data analysis technique, you need to gather relevant data about the major factor you want to study. The data can be metric or nonmetric, but make sure to gather high-quality data. Consequently, your analysis depends on the data you use. Furthermore, when you find some of the data, you need to keep an eye on the missing data while analyzing. You need to know if the data is essential or you will be able to find the result without that data.
- Multiple Regression Analysis
This technique is most commonly used. In this multivariate data analysis technique, you will examine how two or more independent metric variables relate to a single dependent metric variant. Analysts also use multiple regression for forecasting the variable.
- Logistic Regression Analysis
Another name for this is ‘choice models.’ This technique helps to predict the event. For instance, you can find what choice the customer will make when they have different options. To show the classification of the observations, you create a contingency table.
- Discriminant Analysis
The discriminant analysis helps classify the observations correctly in homogeneous groups. With this tool, you can analyze and categorize different people, such as non-buyers and buyers. In this technique, independent variables should be metric and include a high level of normality.
- Multivariate Analysis of Variance (MANOVA)
This technique will analyze the relationship between two or more than two dependent metric variables and independent variables. You need to determine the mean of the vector for different groups. The metric is the dependent variable, and the categorical is the independent variable.
- Factor Analysis
When you have many variables to design the research plan, you can reduce some variables in different smaller factors. In this technique, you will not find any dependent variable. This technique’s sample size should be more than 50 observations, and in every five variables, there should be at least three observations.
- Cluster Analysis
Cluster analysis helps in subgrouping the objects or individuals from large data on the principle of “like attract like”.. With characteristic analysis, you can simplify objects into different sets and groups. Cluster analysis helps you in market segmentation. You can choose from three types of clustering methods. There are:
- Combination of both methods
- Multidimensional Scaling (MDS)
This technique helps to transform the judgments of the consumer into distances in a multidimensional space. You can recognize the dimensions of a product and discover its comparative evaluations when you have no factor to compare. You can interpret dimensions by allowing the respondent to identify them, and then the researcher can analyze the data from the respondent’s identification.
- Canonical Correlation
This technique associates various dependent variables and independent variables. This is a powerful technique and includes independent metric variables such as usage levels, satisfaction levels, and sales. You can also use non metric variables. Among many multivariate data analysis techniques, this one has lesser restrictions.
- Structural Equation Modeling
In this technique, you need to examine the relationship between different sets of variables. This technique includes further techniques such as confirmatory factor analysis, latent variable analysis, and LISREL. You can use this technique to develop summated scales and evaluate the scaled attributes.
Multivariate data analysis helps you generate a summary or a table to analyze multiple variants’ relationship. The more complicated a business problem, the more variables you need to compute the accurate result. Measurement of multiple performances helps analysts and managers relate and measure the metrics that help them make the right and informed decisions. All the methods and techniques in multivariate data analysis are statistical and require huge data for the investigation.
Medium and large businesses are using multivariate data analysis for business researches and understand the data closely. With advancements in technology such as big data, we are able to gather huge data about customer behavior and their activities, tasks, and taste. With proper utilization of data, we can understand the market and thrive in this competitive economy.