Introduction to Multivariate Data Analysis

What is Multivariate Data Analysis?

Data analysis is as old as data itself, however very early forms were limited due to the manual nature of analysis and later forms by computing power limitations. The explosion in data coincided with build-up of computer processing capacities and advanced data analysis techniques became accessible to governments, then organizations, and later, to anyone with a modern day personal computer.

Multivariate statistics involves observation and analysis of multiple statistical variables at a time. The objective is to detect patterns and relationships between several variables simultaneously, and draw conclusions from them. The conclusions are usually in the form of establishing relationships that allow us to predict the effect of a change in one variable on other variables. Researchers typically use multivariate procedures when they are interested in studying more than one outcome simultaneously (also known as the dependent or phenomenon of interest), more than one impacting variable (also known as an independent or predictor), or both. This gives multivariate analysis a decisive advantage over other forms of analysis. This type of analysis is also desirable because researchers often hypothesize that a given outcome of interest is influenced by more than one variable. There are many statistical techniques for conducting multivariate analysis, and the most appropriate technique for a given study varies with the type of study and the key research questions.

Applications and techniques of Multivariate Data Analysis

Applications of Multivariate Data Analysis include:

● Multivariate hypothesis testing
● Dimensionality reduction
● Latent structure discovery
● Cluster analysis
● Multivariate regression analysis
● Classification and discrimination analysis
● Variable selection
● Multidimensional Scaling
● Data mining

Examples of Multivariate Data Analysis

Example 1: Healthcare. A dietician collects patient data on cholesterol, blood pressure, sugar levels and weight. She also collects data on dietary habits. Using Multivariate Data Analysis, she can determine how much each element of diet influences health outcomes.
Example 2: Academics. A researcher has collected data on three demographic variables, and four academic variables (let’s say standardized test scores) for 1,000 students along with the programmes they are enrolled for. The researcher wants to determine how demographics and academic variables are related and the choice of program.

FORE School of Management, New Delhi’s FDP on Multivariate Data Analysis

FORE School of Management, New Delhi has been designing, developing and conducting innovative Executive Education (EE), Management Development Programmes (MDPs), and Faculty Development Programmes (FDPs) for working executives and academicians in India for over three decades. These programmes meet the need for faculty, researchers, and business managers to continuously update themselves about changing business paradigms and innovative business practices to stay ahead of competition.
The purpose of FORE School of Management, New Delhi’s Multivariate Data Analysis FDP is to provide faculty, research scholars, and business executives with the tools to gain a better understanding of the available techniques for data analysis with a focus on multivariate techniques. This program also aims to provide hands-on training on SPSS, AMOS software for analysing the data and interpreting outcomes of various analyses.
The programme will be conducted by Subject Matter Experts and is designed with an appropriate blend of conceptual and experiential learning.
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