STATISTICAL FACTOR ANALYSIS Meaning and
Definition
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Statistical Factor Analysis is a multivariate statistical technique used to uncover underlying factors in a set of observed variables and explain the relationships among them. It is a powerful tool for data reduction and dimensionality reduction.
In this analysis, a large number of observed variables are analyzed to identify a smaller number of latent variables or factors that explain the patterns within the data. These factors are constructs that cannot be directly measured but are inferred from the observed variables. Each observed variable can be seen as a linear combination of the factors, which represent the common underlying information.
The goal of Statistical Factor Analysis is to identify the factors that account for most of the variance in the observed variables. It aims to find a simple structure that explains the data with the least number of factors. The process involves estimating factor loadings, which represent the strength of the relationship between each observed variable and the underlying factors.
Factor Analysis assumes that the observed variables are influenced by a smaller number of unobservable factors. By examining the interrelationships among the observed variables, it seeks to understand the underlying structure and organization of the data. This aids researchers in identifying key underlying dimensions and grouping variables based on their similarities.
Statistical Factor Analysis has applications in various fields such as psychology, sociology, finance, marketing, and economics. It helps researchers gain insights into the latent dimensions and underlying structure of complex data, facilitating the development of theories and models in these disciplines.
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