How is the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy used in factor analysis?
- It is used to assess the appropriateness of factor analysis
- It is used to determine the number of factors to retain
- It is used to test the assumption of homoscedasticity
- It is used to test the assumption of normality
The Kaiser-Meyer-Olkin (KMO) measure is a measure of how suitable the data is for factor analysis. It determines the adequacy for each observed variable and for the complete model. KMO estimates vary between 0 and 1. A value of 0 indicates that the sum of partial correlations is large relative to the sum correlations, implying diffusion in the pattern of correlations (hence, factor analysis will be likely inappropriate).
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