KembaraXtra – Psychology: Communality

Definition

In factor analysis, communality is the proportion of a variable’s variance that is explained by the common factors included in the model. It indicates how much of the variable’s variation is shared with other measured variables rather than being unique, specific, or attributable to measurement error.

Statistical Mechanism

For a variable represented by several orthogonal factors, communality is commonly calculated as the sum of its squared factor loadings. A communality of .70 means that 70 per cent of the variable’s variance is accounted for by the retained common factors. The remaining 30 per cent is treated as unique variance and error within the model.

Role in Psychological Measurement

Communality is important in personality research, intelligence testing, scale development, and other areas using latent-variable models. A high communality suggests that the variable fits well within the factor structure. A low communality may indicate that the item measures something distinct, is unreliable, or is poorly represented by the selected factors.

Interpretation and Limitations

Communality estimates depend on the extraction method, number of factors, sample, and quality of the measurements. In exploratory factor analysis, initial and extracted communalities may differ. A low value does not automatically justify deleting an item, because theoretical importance and content coverage must also be considered. Communality should therefore be interpreted alongside factor loadings, model fit, and conceptual relevance.

Summary

Communality is the proportion of a variable’s variance explained by common factors in factor analysis. It reflects how well the variable is represented by the shared latent structure. Higher values generally indicate stronger integration with the factor model. Interpretation should combine statistical and theoretical considerations.