Discriminant Analysis – Things You Must Know About It

Knowing things essential to conduct a discriminant analysis is important, especially if a researcher gets stuck in classifying large data sets into different manageable groups. Thus, this article aims to explain discriminant analysis by describing its importance, types and 6-step procedure.

Introduction:

Discriminant analysis is a statistical method mostly use in market research which aims to classify observations into different categories or groups. Stated in another way, discriminant analysis is important as it helps researchers in assigning objects to a group among a number of known groups. As a matter of course, it is simply a statistical technique that classifies observations into groups based on scores of one or more quantitative as well as predictable variables. For instance, if a doctor wants to classify a patient into low or high-risk groups for stroke, then the best tool that will be extremely useful for the purpose is discriminant analysis. This analysis will help the doctor classify patients into a low and high-risk group based on personal attributes, such as body weight, cholesterol, lifestyle behaviour, and patient family history.

Importance Of Discriminant Analysis:

There is a long list of benefits of using the discriminant analysis process. Firstly, it helps in determining predictor variables that can easily be related to dependent variables. Secondly, it is also very important to predict certain facts about the dependent variables. Thirdly, market researchers use this analysis method in the perpetual mapping creation. Fourthly, it involves the use of a perceived distancing technique that differentiates this method from other classical statistical analysis methods.

Apart from all previously mentioned benefits, another important quality of discriminant analysis is that it can also be used in combination with other statistical analysis methods, such as cluster analysis. In a nutshell, discriminant statistical analysis is important if the purpose of research is to determine the effect of the predictor variables on dependent variables, predict facts about the dependent variables, classify subjects into different groups based on personal properties, take into account the perceived distancing technique, and create perpetual maps for marketing research.

Types Of Discriminant Analysis:  

Before deciding whether this analysis will be useful for a research proposal or whether other options need to be considered, it is important to know about different types of discriminate statistical analysis. Basically, this statistical method can be divided into four major types:

Linear Discriminate Statistical Analysis:

The linear discriminate statistical analysis is the simplest form of analysis. Its sole purpose is to reduce the number of available features before starting classification. It is mainly used in machine learning and market research, where the linear combination for specifications is differentiated based on two or more objects and events. Furthermore, the linear analysis can be performed with and without a stepwise selection of variables.

Multiple Discriminate Statistical Analysis:

This type of analysis is slightly difficult to perform as compared to linear and canonical analysis methods. This is because it aims to find discriminant functions that can minimise differences within groups and maximise differences between different groups with the use of canonical functions. In simple terms, the multiple discriminate statistical analysis is helpful in compressing the multi-variate signal, so that a low dimensional signal or category can be produced.

Quadratic Discriminate Statistical Analysis:

Unlike other types of analysis, this analysis classifies observation in the form of groups that has the least squared distance. Furthermore, in this method, researchers are not allowed to reduce the squared distance into linear functions.

Canonical Discriminate Statistical Analysis:

The Canonical discriminate statistical analysis or CDA is a multi-variate technique important to determine relationships between a set of independent variables and categorical variables. It includes canonical correlation and Principle Component Analysis in order to perform the dimension reduction.

Discriminant Analysis Is A 6-Step Procedure:

Following the brief description of steps vital to performing discriminant analysis for research:

Select A Research Problem:

In many cases, a research problem is a statement, a question, or a claim to be verified by choosing an extensive research process. However, for a research problem to be analysed by discriminant analysis, it must contain more than one variable that needs to be classified for extracting inferences. This step also includes the selection of objectives, evaluation of group differences on a multi-variate profile, categorisation of factors into different groups, and finding dimensions of discrimination within and between different groups.

Select Research Design Issues:

Issues in designing a research may include the identification of variables whose correlation or interconnectedness needs to be calculated via the research process. Thus, the selection of independent variables, calculation of sample size, creation of analysis, and holdout samples are the major tasks necessary to perform at step number 2 of this analysis process.

Review Assumptions:

Reviewing assumptions and setting research as per its requirement is the fourth step in discriminant statistical analysis. Some of these assumptions include linearity in relationships among variables, normality of independent variables, selection of equal dispersion matrices, and lack of multi-collinearity. Once the researcher has confirmed that the independent, as well as dependent variables, fulfil all these assumptions, then the process of discriminant analysis can be performed.

Estimate Discriminant Functions:

It is the stage of finding discriminant functions and their significance. Furthermore, the determination of optimal cutting scores, criteria for accessing hit ratio, and calculation of statistical significance of predictive accuracy can further help in estimating discriminant functions and their significance.

Interpret  Discriminant Functions:

Interpretation of discriminant functions refers to knowing what discriminant functions tell about the relationship and classification of variables into different groups.

Validate Discriminant Results:

Last but not least is the step to validate all findings of the discriminant analysis process. Unlike other types of analysis processes, techniques important to validate results of the discriminant analysis include ‘split-sample or cross validation’ and ‘profiling group difference’.

Concluding Remarks:

In a nutshell, discriminant analysis is a type of statistical analysis important to classify different objects into different groups based on their personal properties. This type of analysis can be applied to any type of research project, including marketing, medical, engineering, public relationships, mathematics, and many more. Its types include linear discriminate statistical analysis, multiple discriminate statistical analysis, canonical discriminate statistical analysis, and quadratic discriminate statistical analysis. In the end, by following the above mentioned six simple steps, discriminate analysis can easily be applied to any research project. However, if you are facing any issues in conducting this analysis, you must seek help from Dissertation Writers UK.

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