判別分析市場研究

What is Discriminant Analysis?

Discriminant Analysis (DA) is a technique used in Statistics to classify findings. They place these findings into non-overlapping groups based on scores on one or more Quantitative predictor variables. As its name suggests, it’s a way to discriminate or classify outcomes. Three people share the credit for giving birth to DA. These are Mahalanobis in India, Fisher in the UK, and Hotelling in the US.

A doctor can perform a DA to find patients at high or low risk for stroke. The analysis might classify them into high- or low-risk groups. It can base these groups on personal attributes such as body mass or cholesterol level. Or, they can base it on lifestyle behaviors. For example, it can classify them based on the number of cigarettes per day or weekly exercise sessions.

You can use DA for predictive or descriptive objectives. The steps involved are as follows:

• Find the problem
• Estimate the discriminant function coefficients
• Figure out the importance of these discriminant functions
• Interpret the results
• Assess the validity of the results

Why is Discriminant Analysis Important?

Researchers use the Discriminant Analysis process to help them understand the connection between a “dependent variable” and one or more “independent variables.” A dependent variable is one that a researcher tries to explain or predict from the values of the independent variables. Sometimes the dependent variable is divided into several categories (categorical variable).

DA takes continuous independent variables and develops a relationship or predictive equations. Researchers use these equations to place the dependent variables into groups.

When the dependent variable has two categories, the type used is two-group Discriminant Analysis. If it has three or more categories, the kind used is multiple Discriminant Analysis. There’s also Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).

Here’s the primary distinction between the types of Discriminant Analysis. For two groups, it’s only possible to derive one Discriminant Function. This type of DA is known as Discriminant Function Analysis. In the case of multiple discriminant analyses, you can compute more than one Discriminant Function.

DA aims to answer the following questions:

• Where do the expected and observed groupings differ?
• How statistically significant is the gap between the two?

It is a robust research and analysis tool, especially when researchers try to understand how groups (age cohorts, customers) or items (items, brands) differ.

Why Businesses Need Discriminant Analysis

Discriminant Analysis has many benefits, especially today in the age of data. This method is a proven advantage. Because of it, many companies can now drive growth, profits, and competitiveness.

Many examples can explain when DA fits. For example, you can use it to figure out how light, medium, or heavy users of soft drinks differ in their consumption of frozen foods.

It is a popular tool because it has widespread use across different industries. Businesses can use it to analyze specific problems. They must determine which independent variable has the most significant outcome on a dependent variable.

About Discriminant Analysis

You can compare Discriminant Analysis to Regression Analysis for how it pinpoints the degree to which objects adhere to the conditions of certain groups.

You can also compare it to Cluster Analysis, which is unsupervised learning. In contrast, DA is supervised learning. The researcher establishes the object category before starting it.

DA has many uses. For example, you can leverage it to determine which predictor variables are related to the dependent variable. You can also use DA to predict the latter’s value and develop perceptual maps.

SIS International Research assists businesses all over the globe in their strategic planning. We offer market research services, and we use Discriminant Analysis across industries. We can help with your Qualitative and Quantitative Market Research. SIS also provides UX and Strategy Market Research and Competitive Analyses.