Which term is the statistical procedure used to identify clusters of related abilities or traits that seem to measure a common underlying factor?

• A. Framing
• B. Sunk-Cost Fallacy
• C. Factor Analysis
• D. Priming

Answer: (C)

This is about uncovering underlying latent abilities that cause different tests to move together. When you administer many measures of ability or traits, some groups tend to correlate strongly with each other, suggesting they’re tapping into a shared, unseen factor. Factor analysis is the statistical procedure that formalizes this idea: it examines the pattern of correlations among observed variables, extracts one or more latent factors that account for the shared variance, and shows how strongly each observed measure relates to those factors through factor loadings. If a cluster of tests like vocabulary, reading, and verbal reasoning all load highly on the same factor, that indicates they’re all measuring a common verbal ability component. This approach also helps reduce many measures into a smaller number of underlying factors, which is why it’s central to theories of intelligence that posit general and specific abilities. The other terms aren’t about uncovering latent structures: framing concerns how information is presented, the sunk-cost fallacy is about ongoing investments, and priming is about how prior exposure influences later behavior, none of which identify clusters that reflect a shared underlying factor.