This document discusses factor analysis, including defining it as a procedure used for data reduction and summarization. It outlines the key steps in factor analysis like constructing a correlation matrix, determining the number of factors, and rotating and interpreting the factors. Important statistics for factor analysis are also defined, such as Bartlett's Test of Sphericity, communalities, eigenvalues, factor loadings, and the Kaiser-Meyer-Olkin measure of sampling adequacy. The document provides an overview of factor analysis and the process involved.

1. FACTOR ANALYSIS
Neeraj Singh

2. Lecture Outline
 Introduction to Factor Analysis
 Important Statistics Associated with Factor
Analysis
 Steps Involved with Factor Analysis
 Example with SPSS
 Reporting of Results of Factor Analysis
 References

3. Learning Outcome
Writing of a Research Paper by Using Factor
Analysis

4. FACTOR ANALYSIS
 Factor analysis is a general name denoting a
class of procedures primarily used for data
reduction and summarization.
 Variables included In factor analysis is to be
measured on interval or ratio scale.
 Generally there should be at least 4 to 5 times
times number of respondents as the number
of variables.
 It is recommended that the factors extracted
should account for at least 60% of the
variance.

5. Statistics Associated with Factor
Analysis
 Bartlett's Test of Sphericity. Bartlett's test of sphericity is
a test statistic used to examine the hypothesis that the
variables are uncorrelated in the population. In other
words, the population correlation matrix is an identity
matrix; each variable correlates perfectly with itself (r = 1)
but has no correlation with the other variables (r = 0).
 Correlation Matrix. A correlation matrix is a lower triangle
matrix showing the simple correlations, r, between all
possible pairs of variables included in the analysis. The
diagonal elements, which are all 1, are usually omitted.

6.  Communality. Communality is the amount of variance a variable
shares with all the other variables being considered. This is also
the proportion of variance explained by the common factors.
 Eigen Value. The eigen value represents the total variance
explained by each factor.
 Factor Loadings. Factor loadings are simple correlations
between the variables and the factors.
 Factor Loading Plot. A factor loading plot is a plot of the original
variables using the factor loadings as coordinates.

7.  Kaiser-Meyer-Olkin (KMO) The Kaiser-Meyer-Olkin (KMO)
measure of sampling adequacy is an index used to examine the
appropriateness of factor analysis. High values (between 0.5 and
1.0) indicate factor analysis is appropriate. Values below 0.5
imply that factor analysis may not be appropriate.
 Percentage of Variance. The percentage of the total variance
attributed to each factor.

8. STEPS INVOLVED IN FACTOR
ANALYSIS
 Problem Identification
 Construction of Correlation Matrix
 Method of Factor Analysis
 Determination of Number of Factors
 Rotation of Factors
 Interpretation of Factors

9. REFRENCES
 Marketing Research –Naresh K. Malhotra
 Business Research Methods-Naval Bajpai
 Discovering Statistics Using SPSS-Andy Field

10. NEERAJ SINGH
Thanks