DAY 8 -TEAM G

DAY 8 – TEAM G

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 a priori assumption is that any indicator may be associated with any factor. This is the most common form of factor analysis. There is no prior theory and one uses factor loadings to intuit the factor structure of the data.

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. The researcher's a priori assumption is that each factor (the number and labels of which may be specified a priori) is associated with a specified subset of indicator variables. A minimum requirement of confirmatory factor analysis is that one hypothesizes beforehand the number of factors in the model, but usually also the researcher will posit expectations about which variables will load on which factors. The researcher seeks to determine, for instance, if measures created to represent a latent variable really belong together.

Applications of Factor Analysis

Identification of Underlying Factors:

·         clusters variables into homogeneous sets

·         creates new variables (i.e. factors)

·         allows us to gain insight to categories

Screening of Variables:

·         identifies groupings to allow us to select one variable to represent many

·         Useful in regression

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.

How to do factor analysis in SPSS

·         Go to: Analyze---Data Reduction—Factor—Select the factors

·         Select Extraction tab, ant then check the box against scree plot

·         Select Descriptive tab, and then initial solution

·         Select Rotation, and then click the radio button against Verimax

·         Then a solution will be generated, which contains the tables, and graphs which can assist in factor analysis.

The tables, and the graphs generated are

·         Communalities: In this table, the Initial column shows that the variance for the different variables and the Extraction column shows that the amount of extraction that is possible from that particular variable. The thumb rule is that if the extraction value for any variable is below 0.5, then we drop it. After this, we copy the data of one variable, suppose price to excel sheet. And find the average of the entire data and find out the variance from the average. We also find out the standard deviation of the data, which helps us to find the Z score (Variation/Std Dev). The properties of the Z score is that it retains the distribution of the data, and, their mean = 0, and Std. Dev = 1. Components are made of some amount of variance of all types. In the table ‘Total Variance’, Total resembles Variance.

·         Component matrix: It shows the relation of the component with the variable.

·         Rotated component matrix

·         Scree Plot: Scree plot helps in choosing the number of components out of the total number of variables.

      ANKIT MAHESHWARI

            TEAM G