Factor Analysis - Group B - Ashim Abhinav Ekka (14133)

Group B

Written by Ashim Abhinav Ekka (14133)

Factor Analysis: It is used as a method of data reduction and summarization. It is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.

Many variables are taken and are explained with a few factors. Correlated variables are grouped together and separated from other variables with low or no correlation.

Why Factor Analysis is used?

• Identify underlying dimensions called factors that explain the correlations among a set of variables.

• Identify a new, smaller set of uncorrelated variables to replace the original set of correlated variables for subsequent analysis.

Factor analysis is used for studying various processes, like psychology, economics, etc, where there appear to be a large number of variables affecting the process. By analyzing and studying the variables statistically, factor analysis help to separate out few core variables, known as factors, which control the rest. Concentrating on the factors makes analyzing the process much simpler.

Types of Factor Analysis

• Exploratory FA: EFA is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables.

• Confirmatory FA: It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor). As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model.

Terminology:

• Scree Plot: It is used for determining the number of factors. It is a plot of the eigen values against the number of factors. Typically, the plot has a distinct break with a trailing off with the rest of the factors. This trailing off is referred to as Scree.

• Varimax Rotation: It is a change of coordinates used in factor analysis that maximizes the sum of the variances of the squared loadings. That is, it seeks a basis that most economically represents each individual, so that each individual can be well described by a linear combination of only a few basic functions.

Example of Factor Analysis:

 

Marketers and researchers who study a product, service, or industry professionally sometimes perceive many more distinctions within their category than do their consumers. This can lead to questionnaires containing attribute lists that consumers see as somewhat or largely synonymous. Factor analysis tells you how many different core factors the consumers perceived out of the list of attributes they rated.

References:

http://www.wikipedia.org/