What does the Kaiser-Meyer-Olkin (KMO) measure assess in factor analysis?

What does the Kaiser-Meyer-Olkin (KMO) measure assess in factor analysis?

• A. The normality of data
• B. The total variance explained
• C. The adequacy of sampling for factor analysis
• D. The homogeneity of variances

Answer: (C)

The test is about whether the data are suitable for factor analysis. The Kaiser-Meyer-Olkin measure looks at sampling adequacy by comparing the overall correlations among variables with their partial correlations (the relationships after removing the influence of other variables). If partial correlations are small relative to the regular correlations, it suggests that a few underlying factors could account for much of the shared variance, which yields a high KMO and indicates good suitability for factor analysis. If partial correlations are large, the data may not produce reliable factors, and the KMO is low. This measure therefore focuses on how appropriate the sample is for revealing a factor structure, not on normality, the amount of variance explained by factors, or whether variances are equal across groups.

The test is about whether the data are suitable for factor analysis. The Kaiser-Meyer-Olkin measure looks at sampling adequacy by comparing the overall correlations among variables with their partial correlations (the relationships after removing the influence of other variables). If partial correlations are small relative to the regular correlations, it suggests that a few underlying factors could account for much of the shared variance, which yields a high KMO and indicates good suitability for factor analysis. If partial correlations are large, the data may not produce reliable factors, and the KMO is low. This measure therefore focuses on how appropriate the sample is for revealing a factor structure, not on normality, the amount of variance explained by factors, or whether variances are equal across groups.