The Kaiser-Meyer-Olkin (KMO) Test measures the adequacy of sample size for factor analysis, indicating whether data is suitable for such analysis by evaluating the proportion of variance among variables that might be common variance. A KMO value closer to 1 suggests that factor analysis may be useful, while a value closer to 0 indicates that it may not be appropriate due to insufficient shared variance among variables.

Kaiser-Meyer-Olkin Test

adequacy of sample size

Factor Analysis is a statistical method used to identify underlying relationships between variables by reducing the number of observed variables into a smaller number of latent factors. It helps in understanding the structure of data, simplifying datasets, and is widely used in fields like psychology, finance, and social sciences to uncover hidden patterns and dimensions.

factor analysis

proportion of variance

common variance

KMO value

Shared variance refers to the portion of variability in a set of data that is common between two or more variables, indicating the extent to which these variables are related. It is a crucial concept in statistical analysis, particularly in understanding correlations and the degree of association between variables in multivariate analyses.

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