HI, Helena is right, 

Kaiser-Meyer Olkin (KMO) is measure of sampling adequacy index for comparing magnitudes of observed correlation coefficients to magnitudes of partial correlation coefficients

Dear Pamela, 

Kaiser-Mayer_Olkin Measure of sampling adequacy (=KMO) is a statistical procedure which allows the measurement the quality of the correlations between variables in order to be able to continue with the factorial analysis. KMO varies between 0 and 1 and compares the zero order correlation with the partial correlations observed between the variables.

The KMO close to 1 indicates small partial correlation coefficients while values close to 0 indicate that the factor analysis can not be a good idea because there is a weak correlation between the variables. Kaiser adjective values KMO as they are:

i) between 1 to 0.9 very good FA; 0.8-0.9 AF good; 0.7-0.8 FA medium; 0.6-0.7 AF reasonable; 0.5-0.6 AF bad;

HI, Helena is right, 

Kaiser-Meyer Olkin (KMO) is measure of sampling adequacy index for comparing magnitudes of observed correlation coefficients to magnitudes of partial correlation coefficients

My dear, the value you had shows a positive relationship between the variables and thus good to be used for say factor analysis, or principal component-factoring.

Dear Frank,

The KMO is just one measure relevant  for analysing the quality of FA 

KMO is a sample adequacy test.The value greater that 0.60 are considered good.The higher the value, the better it is.The KMO value of 0.848 is very good.Now you can proceed for Factor Analysis

..then we can proceed to explore the quality of FA to our data, becasuse we only know that there is correlation between VI variables.

PS: remember that almost everything is correlate in life, but the quality of it  must be analysed..it is the same with whatever models

KMO is a measurement of sample adequacy. A sample is deemed adequate if the value of KMO is greater than 0.5 . Your 0.846 show that your survey has collected a sample that is adequate.

This is about sampling adequacy for the purpose of factor analysis. The number of samples in the study has variation in its responses to a degree of 84.6 percent. The variation is necessary to identify the components of factors in a multidimensional scale. There is inadequacy of about 16.4% in the spread of data. However, anything above 60% is acceptable for further processing of factors.

SAM.Selvarasu A. Mutharasu

It means that your scale is factorable and that you can proceed with interpreting the factor analysis.

Hi All, 

my sample size is 1342

My KMO value is coming out to be 0.630 and 8 factors have been extracted at cumulative variance of 71.01 %

I have heard that is my KMO value 0.630 adequate ? and if yes, please could anyone provide me with references  of latest journals and books so that i can refer the same !

Dear Shorab, kindly refer to the article:

A STATISTICAL MODEL OF ORGANIZATIONAL PERFORMANCE USING FACTOR ANALYSIS - A CASE OF A BANK IN GHANA.

Once the KMO value is more than 50% it means it is adequate to carry out with FA.

You can visit my page for same article with regards to the explanation of the KMO value.

Regards!!

Dear RG colleague,

The KMO statistic, which can vary from 0 to 1, indicates the degree to which each variable in a set is predicted without error by the other variables. A value of 0 indicates that the sum of partial correlations is large relative to the sum correlations, indicating factor analysis is likely to be inappropriate . A KMO value close to 1 indicates that the sum of partial correlations is not large relative to the sum of correlations and so factor analysis should yield distinct and reliable factors. Hair et al. (2006) suggest accepting a value of 0.5 or more. values between 0.5 and 0.7 are mediocre, and values between 0.7 and 0.8 are good.

Bartlett’s test of sphericity is a statistical test for the presence of correlations among variables, providing the statistical probability that the correlation matrix has significant correlations among at least some of variables. For factor analysis to work some relationships between variables are needed. Thus, a significant Bartlett’s test of sphericity is required, say p

High values of KMO (close to 1.0) generally indicate that factor analysis may be useful with the data to be analysed. If the value of KMO is less than 0.50, the results of the factor analysis probably won't be very useful.