Communality is described as the proportion of variance explained by common factors.

Communality is described as the proportion of variance explained by common factors.

• A. The proportion of variance explained by common factors
• B. The total variance
• C. The unique variance
• D. The covariance with others

Answer: (A)

Communality refers to the portion of a variable’s variance that is explained by the underlying common factors in a factor model. In factor analysis, each observed variable has variance split into two parts: the shared variance explained by the common factors (communality) and the unique variance specific to that variable (including measurement error). The communality is effectively the sum of the squared factor loadings of that variable across all extracted factors, representing how much of its variance the latent factors account for. Since it is exactly the share of variance explained by the common factors, this is the best description. The total variance is the full variance of the variable (communality plus unique variance); the unique variance is the part not explained by the factors; and covariance with others describes how two variables vary together, not the proportion of a single variable’s variance explained by factors.

Communality refers to the portion of a variable’s variance that is explained by the underlying common factors in a factor model. In factor analysis, each observed variable has variance split into two parts: the shared variance explained by the common factors (communality) and the unique variance specific to that variable (including measurement error). The communality is effectively the sum of the squared factor loadings of that variable across all extracted factors, representing how much of its variance the latent factors account for. Since it is exactly the share of variance explained by the common factors, this is the best description. The total variance is the full variance of the variable (communality plus unique variance); the unique variance is the part not explained by the factors; and covariance with others describes how two variables vary together, not the proportion of a single variable’s variance explained by factors.