What is Factor Analysis?
Talking in simple terms Factor Analysis is check for Correlation among given set of variable. Factor Analysis is done for more than more than 5 Variables.
Note: Given variable should be either in Ordinal or scale format, it should strictly not be in Nominal scale.
Factor analysis is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables.
There are basically two types of factor analysis: exploratory and confirmatory.
· Exploratory factor analysis (EFA) attempts to discover the nature of the constructs influencing a set of responses.
· Confirmatory factor analysis (CFA) tests whether a specified set of constructs is influencing re- sponses in a predicted way.
Objective of EFA:
· The primary objectives of an EFA are to determine
1. The number of common factors influencing a set of measures.
2. The strength of the relationship between each factor and each observed measure.
· Some common uses of EFA are to
◦ Identify the nature of the constructs underlying responses in a specific content area.
◦ Determine what sets of items “hang together” in a questionnaire.
◦ Demonstrate the dimensionality of a measurement scale. Researchers often wish to develop scales that respond to a single characteristic.
◦ Determine what features are most important when classifying a group of items.
◦ Generate “factor scores” representing values of the underlying constructs for use in other analyses.
Objective of CFA:
The primary objective of a CFA is to determine the ability of a predefined factor model to fit an observed set of data.
· Some common uses of CFA are to
◦ Establish the validity of a single factor model.
◦ Compare the ability of two different models to account for the same set of data.
◦ Test the significance of a specific factor loading.
◦ Test the relationship between two or more factor loadings.
◦ Test whether a set of factors are correlated or uncorrelated.
· Assess the convergent and discriminate validity of a set of measures
Applying Factor Analysis on SPSS 15:
Step 1: Analyze
Step 2: Data Reduction
Step 3: Factor
Z-score is obtained by Dividing Variance by the factor. When Average of Z-score is taken it should
Round off to “Zero” or else the value obtained is not Accurate.
And standard Deviation of z-score should be “One”.
Application of z-score:
In Six Sigma parlance, z-score and process sigma are used interchangeably and are
sometimes called z-equivelents. Strictly speaking, the process sigma and z-
equivalents are loosely tied to the statistical z-score. The statistical z-score
has very strict definitions derived from the rules of the normal distribution.
For most applications in Six Sigma, ignoring some of those constraints is
innocuous. In usability testing the benefit of the standardization from process
sigmas allow us to meaningfully compare disparate measures like task completion
and time on task.
Name : Abhijit Naik
Marketing Group 6