Factor Analysis vs Principal Component Methods

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What is the focus of principal component methods?

To ignore the attributes

To maintain the same number of attributes

To reduce the number of attributes (correct)

To increase the number of attributes

In the context provided, what is the primary purpose of factor analysis?

To increase the number of attributes

To reduce the number of attributes (correct)

To create more attributes

To ignore the attributes

What is the main goal of using factor analysis in data analysis?

To simplify the data structure (correct)

To introduce noise in the data

To complicate the data structure

To keep all attributes intact

Which method focuses on reducing the number of dimensions for modeling purposes according to the text?

Principal Component Methods Signup and view all the answers

How does factor analysis differ from principal component methods based on the text provided?

Factor analysis increases dimensions while principal component methods decrease them Signup and view all the answers

What is the primary purpose of Principal Component Analysis?

To predict another variable in multi-variate data Signup and view all the answers

What is the initial step required before conducting cluster analysis according to the text?

Principal Component Analysis Signup and view all the answers

How is the choice between using covariance matrix or correlation matrix affected by data non-normality in Principal Component Analysis?

It has no effect on the results Signup and view all the answers

In what way does Principal Component Analysis help in multi-variate data?

It improves prediction of another variable Signup and view all the answers

What does Factor Analysis measure according to the text?

Relationship between variables Signup and view all the answers

What is the main objective of factor analysis?

To identify the number of factors that explain a large proportion of the total variance Signup and view all the answers

In factor analysis, what is the significance of determining the number of factors?

It helps in understanding which factors account for most of the variability in the data Signup and view all the answers

How does factor analysis differ from principal component analysis?

Factor analysis focuses on identifying latent variables, while PCA focuses on maximizing variance explained by orthogonal components Signup and view all the answers

Which statement best describes the role of factor analysis?

Factor analysis is a dimensionality reduction technique that uncovers hidden patterns in data Signup and view all the answers

What is one of the key advantages of conducting factor analysis?

Identifying underlying constructs that explain observed correlations among variables Signup and view all the answers

Factor Analysis vs Principal Component Methods

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