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Factor Analysis an Overview

Factor Analysis is used in cases where observed variables which are collated have some similarity in the way they are responded to on account of a certain variable which maybe the underlying reason. It finds widespread application in cases requiring investigation relationship amongst variables for complex concepts such as in the field of psychology, socio-economic studies and health related studies.

The key underlying assumptionin relation to a factor analysis problem is that the multiple observed variables are linked with an underlying variable which may not be measured and hence exhibit a certain similarity in responses. To cite and example, people may respond to question related to choice of school, restaurants and occupation as they may be associated with the latent variable of socioeconomic status.

In every factor analysis, the number of factors will equal the number of variables. Further, each factor will reflect an amount of overall variance in the observed values and the factors will be listed based on the amount of variation they cause. This measure is known as eigenvalue that is the amount of variance of the observed value explained by a factor.

In addition to understanding how much variance in the observed values can be attributed to the factors, factor analysis also facilitates reduction of variables. It simplifies the data. Factor analysis finds widespread application in the fields of Psychology and Education and is also used in the construction of indices. As it explains the relationship of factors to the observed variables it is used to assign weightages to each variable in the index.

Key Concepts for using Factor Analysis

A learner of factor analysis would need to develop a uunderstandconcepts such as interpretation of factor loadings, specific variances, and communalities. They also need to understand methods such as the application of principal component and maximum likelihood for estimating parameters of a factor model. Other important concepts encompass factor rotation and interpretation of rotated factor loadings.

Topics covered in Factor Analysis

Given the standard requirements of our learners we have designed our course such that it covers the following topics in detail.

• Introduction to basic concepts
• Assumptions for using Factor Analysis
• Principal Component Method
• Communalities
• Maximum Likelihood Estimation Method
• Goodness-of-Fit
• Factor Rotations
• Varimax Rotation
• Estimation of Factor Scores

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