Use factor analysis when several observed measurements may reflect fewer unobserved dimensions, such as related questionnaire items about planning, prioritization and record keeping. A common-factor model separates shared variation represented by latent factors from item-specific variation. Factor loadings describe how measurements relate to those latent factors; communalities summarize their shared-variance contributions.
Exploratory factor analysis investigates a structure not yet fixed; confirmatory factor analysis evaluates prespecified measurement restrictions, preferably in independent data. Extraction, factor count and factor rotation affect interpretation, so report them and justify the sample and measurement assumptions. A statistically convenient factor solution does not alone establish construct validity or demonstrate the existence of a literal psychological mechanism. The distinction from principal component analysis is the common-versus-total-variation objective, and from cluster analysis the modeling of measurement relationships rather than the grouping of cases.

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