= Solution
Use \b[<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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