In factor analysis, what term describes a latent variable that underlies observed variables?

In factor analysis, what term describes a latent variable that underlies observed variables?

• A. Factor
• B. Extracted score
• C. Latent trait
• D. Component

Answer: (A)

In factor analysis, the hidden dimension that explains why observed variables covary is called a factor. It represents a latent construct that underlies the observed measures, inferred from their patterns of correlations and not directly observed itself. An extracted score, by contrast, is simply the estimated value of a factor for each case after the extraction step, not the latent variable itself. A component comes from principal components analysis and serves a different modeling approach, while a latent trait is a broader term used in other psychometric frameworks. So the latent variable that underlies observed variables in this context is best described as a factor.

In factor analysis, the hidden dimension that explains why observed variables covary is called a factor. It represents a latent construct that underlies the observed measures, inferred from their patterns of correlations and not directly observed itself. An extracted score, by contrast, is simply the estimated value of a factor for each case after the extraction step, not the latent variable itself. A component comes from principal components analysis and serves a different modeling approach, while a latent trait is a broader term used in other psychometric frameworks. So the latent variable that underlies observed variables in this context is best described as a factor.