Day 8-Factor Analysis-Team B- Gopalkrishnan Iyer

Day 8- Team B- Gopalkrishnan Iyer

Factor Analysis

      Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. It is mainly used to reduce the number of variables. Also, to determine common underlying theme in the components or factors i.e. group of Variables. The factors are grouped together taking into account the correlated between them .i.e. the variables correlated to each other are placed together.

 It aims to describe a large number of variables or questions by only using a reduced set of underlying variables, called factors. It explains a pattern of similarity between observed variables. We also learnt about variance, covariance, standard deviation, the Z-score and its use in the factor analysis.

Type of factor analysis

Exploratory factor analysis (EFA) is used to uncover the underlying structure of a relatively large set of variables. The researcher's prior assumption is that any indicator may be associated with any factor. This is the most common form of factor analysis.

Confirmatory factor analysis (CFA) seeks to determine if the number of factors and the loadings of measured (indicator) variables on them conform to what is expected on the basis of pre-established theory. Indicator variables are selected on the basis of prior theory and factor analysis is used to see if they load as predicted on the expected number of factors. 

Advantages:

§  Both objective and subjective attributes can be used provided the subjective attributes can be converted into scores.

§  Factor analysis can identify latent dimensions or constructs that direct analysis may not.

§  It is easy and inexpensive.

Disadvantages:

§  Usefulness depends on the researchers' ability to collect a sufficient set of product attributes. If important attributes are excluded or neglected, the value of the procedure is reduced.

§  If sets of observed variables are highly similar to each other and distinct from other items, factor analysis will assign a single factor to them. This may obscure factors that represent more interesting relationships.

Factor Analysis in  Marketing:

The basic steps include:

§  Identifying the salient attributes consumers use to evaluate products in this category.

§  Use quantitative marketing research techniques such as surveys, to collect data from a sample of potential customers concerning their ratings of all the product attributes.

§  Input the data into a statistical program and run the factor analysis procedure. The computer will yield a set of underlying attributes (or factors).

§  Use these factors to construct perceptual maps and other product positioning devices.

Scree Plot:

§         A plot in the descending order of magnitude, of the eigenvalues of a correlation matrix.

§         In the context of factor analysis or principal components analysis a scree plot helps the analyst visualize the    relative importance of the factors — a sharp drop in the plot signals that subsequent factors are ignorable.