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Hierachical cluster analysis is shown to be an effective method for forming scales from sets of items. The number of scales to form from a particular item pool is found by testing the psychometric adequacy of each potential scale. Higher-order scales are formed when they are more adequate than their component sub-scales. It is suggested that a scale's adequacy should be assessed by a new measure of internal consistency reliability, coefficient beta, which is defined as the worst split-half reliability of the test. Comparisons with other procedures show that hierarchical clustering algorithms using this psychometrically based decisions rule can be more useful for scale construction using large item pools than are conventional factor analytic techniques.

A new procedure for determining the optimal number of interpretable factors to extract from a correlation matrix is introduced and compared to more conventional procedures. The new method evaluates the magnitude of the Very Simple Structure index of goodness of fit for factor solutions of increasing rank. The number of factors which maximizes the VSS criterion is taken as being the optimal number of factors to extract. Thirty-two artificial and two real data sets are used in order to compare this procedure with such methods as maximum likelihood, the eigenvalue greater than 1.0 rule, and comparison of the observed eigenvalues with those expected from random data.

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Personality psychology is concerned with affect (A), behavior (B), cognition (C) and desire (D), and personality traits have been defined conceptually as abstractions used to either explain or summarize coherent ABC (and sometimes D) patterns over time and space. However, this conceptual definition of traits has not been reflected in their operationalization, possibly resulting in theoretical and practical limitations to current trait inventories. Thus, the goal of this project was to determine the affective, behavioral, cognitive and desire (ABCD) components of Big-Five personality traits. The first study assessed the ABCD content of items measuring Big-Five traits in order to determine the ABCD composition of traits and identify items measuring relatively high amounts of only one ABCD content. The second study examined the correlational structure of scales constructed from items assessing ABCD content via a large, web-based study. An assessment of Big-Five traits that delineates ABCD components of each trait is presented, and the discussion focuses on how this assessment builds upon current approaches of assessing personality.