= Solution
Use \b[<cluster analysis>] to discover groups of observations with similar profiles when no correct group labels have been supplied. For example, it could group lecturers by patterns of reported organizational behavior. Define the analysis unit and features, handle missing data, and choose scaling and a meaningful similarity measure; otherwise a feature with large numerical units may dominate the groups.
<K-means clustering> partitions observations around centers, whereas <agglomerative hierarchical clustering> produces nested groupings represented by a <dendrogram>. Their results include group membership and interpretable profiles, conditional on these choices. Check stability across resampling and reasonable analysis choices and, when possible, in new data. \b[A clustering does not prove that natural psychological types exist]: an algorithm can partition a continuous distribution. Its units are cases; <factor analysis> instead primarily models shared variation among measured variables.
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