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• Exploratory Factor Analysis
• The Kaiser-Guttam Rule
• Scree Plot
• Confirmatory Factor Analysis (CFA)

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## Exploratory Factor Analysis

Exploratory factor analysis aims to reduce data into summarized variables that can be used to explain the underlying theoretical structure of an event. This type of statistical technique explains the relationship structure of a variable and a respondent. Two methods can be used to perform this type of analysis:

### R-type factor analysis

This method of factor analysis calculates factors from the correlation matrix. R-type factor analysis explores the relationship between measured variables. It strives to explain how the variables group together and are associated.

### Q-type factor analysis

The Q-type factor analysis computes factors from individual respondents. The object sorts out the population with regards to their simultaneous reaction to all variables.

## The Kaiser-Guttam Rule

The Kaiser-Guttam rule recommends that the researcher should retain components that are based on eigenvalues that are greater than one. This rule is founded on the concept of above-average components are represented by an eigenvalue that is bigger than one, given that the sum of the eigenvalues is p. You can use the SCREE test to examine the plot of eigenvalues.

## Scree Plot

A scree plot is a common method in principal component analysis. In a graphical representation, a scree plot can help us determine the number of principal components to be retained. This plot is described by a line segment plot that highlights the eigenvalues for every single PCs. The graph’s y-axis highlights the eigenvalues while the x-axis depicts the number of factors. A scree plot is always in the form of a downward curve.

## Confirmatory Factor Analysis (CFA)

CFA is a multivariate method in statistics that is used to explain how well constructs are represented by measured variables. Unlike in exploratory factor analysis where all the variables measured are related to every hypothetical variable, analysis using CFA can specify the exact number of factors that they need in the data. In simple terms, we can define confirmatory factor analysis as a tool that confirms or rejects the theory of measurement.