Factor Analysis Group C

Factor Analysis

                   By Group C, Operations Batch

Prince Kumar

Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. Factor analysis can also be used to generate hypotheses regarding causal mechanisms or to screen variables for  subsequent analysis (for example, to identify collinearity prior to performing a linear regression analysis).

Factor analysis is a method of data reduction.  It does this by seeking underlying unobservable (latent) variables that are reflected in the observed variables (manifest variables).  There are many different methods that can be used to conduct a factor analysis (such as principal axis factor, maximum likelihood, generalized least squares, unweighted least squares), There are also many different types of rotations that can be done after the initial extraction of factors, including orthogonal rotations, such as varimax and equimax, which impose the restriction that the factors cannot be correlated, and oblique rotations, such as promax, which allow the factors to be correlated with one another.  You also need to determine the number of factors that you want to extract.  Given the number of factor analytic techniques and options, it is not surprising that different analysts could reach very different results analyzing the same data set.  However, all analysts are looking for simple structure.  Simple structure is pattern of results such that each variable loads highly onto one and only one factor. 

There are two main classes of factor score computation methods: refined and non-refined. Non-refined methods are relatively simple, cumulative  procedures to provide information about individuals’ placement on the factor distribution.

The simplicity lends itself to some attractive features, that is, non-refined methods are both easy to compute and easy to interpret. Refined computation methods create factor  scores using more sophisticated and technical  approaches. They are more exact and complex than non-refined methods and provide estimates that are  standardized scores.

Non-refined Methods:

Non-refined methods are simple to use. Under the class of non-refined methods, various methods exist

produce factor scores. The most frequently used methods are described below.

1.       Sum Scores by Factor

2.       Sum Scores – Above a Cut-off Value

3.       Sum Scores - Standardized Variables

4.       Weighted Sum Scores

Refined Methods:

Refined procedures may be applied when both principal components and common factor extraction methods are  used with EFA. Resulting factor scores are linear combinations of the observed Variables which consider what is shared between the item and the factor.