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# Factor Analysis Assignment Help

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## What is Factor Analysis ?

Factor analysis is a statistical method for describing variability among associated variables in terms of a smaller number of unobserved variables known as factors.For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables. Factor analysis looks for joint fluctuations in response to hidden variables that aren't visible. Factor analysis can be regarded as a special form of errors-in-variables models since the observed variables are described as linear combinations of the potential factors plus "error" terms.

Factor analysis is a method for reducing a large number of variables into a smaller number of factors. This method takes the largest common variation from all variables and converts it to a single score. We can use this score as an index of all variables for further investigation. Factor analysis is a subset of the general linear model (GLM), and it makes certain assumptions: there is a linear relationship, no multicollinearity, relevant variables are included in the analysis, and there is real correlation between variables and factors. There are other ways to choose from, but principal component analysis is the most popular. Factor analysis is commonly used in psychometrics, personality psychology, biology, marketing, product management, operations research, finance, and machine learning.

### Types of Factor analysis

• Exploratory factor analysis

• Confirmatory factor analysis

Exploratory factor analysis : Exploratory factor analysis (EFA) is a method to explore the underlying structure of a set of observed variables, and is a crucial step in the scale development process. The first step in EFA is factor extraction.

Confirmatory factor analysis : The process helps to show the factors that affect the structure of a given data without any predefined set of the output device. Confirmatory factor analysis (CFA) is a more advanced method for determining whether or not the items are linked to certain factors. CFA uses structural equation modelling to test a measurement model in which factor loading allows for the assessment of correlations between observed and unobserved variables. Structural equation modelling techniques are less restrictive than least-squares estimates and can allow measurement error. The study would provide loadings of observable variables on the latent variables (factors), as well as the correlation between the latent variables, when hypothesised models are evaluated against actual data.

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