GRE (Graduate Record Examinations) Psychology Measurement and Methodology-Statistical Procedures Study Material (Page 23 of 25)

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Factor Analysis: FA and PCA

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FA and PCA

FA and PCA Factor Analysis & Principal Component Analysis

When there are no defining hypotheses, exploratory factor analysis is used to find the underlying factors. The factors in factor analysis are “real world” variables such as depression, anxiety, and intelligence. In contrast in principal components analysis (PCA) , components are simply geometrical abstractions that may or may not map to any real word constructs.

Principal Component Analysis

PCA՚s approach to data reduction is to create one or more index variables from a larger set of m…

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Factor Analysis: Principal Component Analysis (PCA)

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Principal Component Analysis (PCA)

Mechanics of PCA

PCA is a procedure for identifying a smaller number of uncorrelated variables, called “principal components,” from a large set of data to explain the maximum amount of variance with the fewest number of principal components.

Principal components can be defined as a linear combination of optimally-weighted observed variables such that they explain maximum variance in the variables. “Linear combination” means that scores on a component are created by adding together scores on the observed variables being analyzed. “Optimally weighted” refers to the fact that the observed variables are …

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