logging in or signing up FACTOR ANALYSIS mail4riti Download Post to : URL : Related Presentations : Let's Connect Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 2308 Category: Business & Fin.. License: All Rights Reserved Like it (0) Dislike it (0) Added: June 02, 2009 This Presentation is Public Favorites: 1 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript FORMULATE THE PROBLEM : FORMULATE THE PROBLEM VI V2 V3 V4 V5 V6 V7 V8 V9 V10 CONSTRUCT THE CORRELATION MATRIX : CONSTRUCT THE CORRELATION MATRIX The variables must be correlated If correlation is small then factor analysis may not be appropriate and vice versa Two methods to test the correlation matrix 1) Bartlett test of sphercity 2) Kaiser Meyer Olkin measure of sampling . adequacy Output From SPSS for Correlation Matrix : Output From SPSS for Correlation Matrix BARTLETT TEST OF SPHERICITY : BARTLETT TEST OF SPHERICITY To test the null hypothesis that the variables are uncorrelated in the population A large value of test static favors the rejection of null hypothesis The approximate chi square static is 172.181 with 45 degrees of freedom, which is significant at the 0.05 level. KAISER –MEYER-OKLIN measure of Sampling Adequacy : KAISER –MEYER-OKLIN measure of Sampling Adequacy The value of KMO static is also large 0.630 (>0.5) Thus this factor analysis may be considered an appropriate technique for analyzing the correlation matrix COMMUNALITIES : COMMUNALITIES IT CAN BE DEFINED AS THE PROPORTION OF VARIANCE IN ANY ONE OF THE ORIGINALVARIABLES…. All the values should be greater than 0.5 then only the variables can gel with each other and if any less than one then they won’t gel… Variable 8 has the value close to .56 so it may be further investigated. Initial Extraction VAR00001 1.000 .941 VAR00002 1.000 .969 VAR00003 1.000 .941 VAR00004 1.000 .825 VAR00005 1.000 .871 VAR00006 1.000 .705 VAR00007 1.000 .617 VAR00008 1.000 .569 VAR00009 1.000 .818 VAR00010 1.000 .960 Extraction Method: Principal Component Analysis. DETERMINE THE METHOD OF FACTOR ANALYSIS : DETERMINE THE METHOD OF FACTOR ANALYSIS Out of the two methods of factor analysis , the Principal Component Analysis is considered This is because of the primary concern is to determine the minimum number of factors that will account for maximum variance in the provided data and the factors are thereby reduced… DETERMINING THE NUMBER OF FACTORS : DETERMINING THE NUMBER OF FACTORS FACTOR EXTRACTION PROCESS The objective is to reduce the variables to a fewer number of factors Determining the number of factors to be extracted on the basis of Eigen Values. Assuming Eigen Value = 1, we will retain those factors above Eigen Value so we get (3) factors finally … OUTPUT FOR THE TOTAL VARIANCE AND DETERMINATION OF FACTORS : OUTPUT FOR THE TOTAL VARIANCE AND DETERMINATION OF FACTORS ROTATE THE FACTORS : ROTATE THE FACTORS The process of identifying which factors are associated with which original variables. We get outputs in factor matrix and then we get rotated factor matrix by performing quartimax method of rotation COMPONENT MATRIX : COMPONENT MATRIX INTERPRETATION : INTERPRETATION Factor 1 has the loading of variable 1,2,3 & 10 (.96863,.96268,.95977,.97469) can be classified as ECONOMY Factor 2 has the loading of variable 4 & 5 (.88911,.90771) can be classified as SPACIOUS Factor 3 has the loading of variable 6 & 7 (..82809,.76951) can be classified as SAFETY ALTERNATIVE SOLUTION : ALTERNATIVE SOLUTION ALTERNATIVE SOLUTION BY ADDING THE 11TH VARIABLE VAR0011= The colour of the car should be bright and attractive Communalities : Communalities Here the value of 11th variable falls below .5 so this variable is less likely to gel with other variables ALTERNATIVE SOLUTION : ALTERNATIVE SOLUTION Same output in terms of factor determination(3) COMPONENT MATRIX : COMPONENT MATRIX INTERPRETATION : INTERPRETATION THIS WILL BE THE SAME Factor 1 has the loading of variable 1,2,3 & 10 (.96863,.96268,.95977,.97469) can be classified as ECONOMY Factor 2 has the loading of variable 4 & 5 (.88911,.90771) can be classified as SPACIOUS Factor 3 has the loading of variable 6 & 7 (..82809,.76951) can be classified as SAFETY Since the output value of 11th variable is very low so it doesn’t make much of difference on the decision making. You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.