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Factor analysis

3:21

Factor Loadings - What do they Mean? Factor Analysis; PCA; Eigenvalues

1:46

Overview of Factor Analysis course

2:10

What is FACTOR ANALYSIS? What does FACTOR ANALYSIS mean? FACTOR ANALYSIS explanation.

2:30

Jordan Peterson: Using Factor Analysis to Derive the Big 5 Traits

3:48

6 Factor Analysis - Examining Factor Loadings

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors, plus "error" terms. Factor analysis aims to find independent latent variables. The theory behind factor analytic methods is that the information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Factor analysis is commonly used in biology, psychometrics, personality theories, marketing, product management, operations research, and finance. Proponents of factor analysis believe that it helps to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables. It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality.
    • Statistical model 

    • Practical implementation 

    • In psychometrics 

    • Exploratory factor analysis versus principal components analysis 

    • In marketing 

    • In physical and biological sciences 

    • In microarray analysis 

    • Implementation