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We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor analysis with subsequent Varimax, Equamax, Parsimax, Factor Parsimony, Tandem I, Tandem II, Infomax, and McCammon's minimum entropy rotation. Samples drawn from oblique populations were submitted to factor analysis with subsequent Geomin rotation and a Promax-based Tandem II rotation. As a benchmark, we investigated a target rotation of the sample loadings toward the corresponding faceted population loadings. The three conditions were sample size (n = 400, 1,000), number of factors (q = 4-12), and main loading size (l = 0.40, 0.50, 0.60). For less than six orthogonal factors Infomax and McCammon's minimum entropy rotation and for six and more factors Tandem II rotation yielded the highest congruence of sample loading matrices with faceted population loading matrices. For six and more oblique factors Geomin rotation and a Promax-based Tandem II rotation yielded the highest congruence with faceted population loadings. Analysis of data of 393 participants that performed a test for the Berlin Model of Intelligence Structure revealed that the faceted structure of this model could be identified by means of a Promax-based Tandem II rotation of task aggregates corresponding to the cross-products of the facets. Implications for the identification of faceted models by means of factor rotation are discussed.

Research on the structure of psychometric intelligence has used hierarchical models like the higher-order and the bi-factor model and has studied the hierarchical relationship between factors within these models. In contrast, research on the structure of personality has not only used hierarchical models but has also studied hierarchies of factor solutions. We clarify the theoretical and conceptual differences between hierarchical models and the solutions-hierarchy approach used in the field of personality research, and suggest that the solutions-hierarchy perspective can provide a novel perspective for intelligence research. We used the solutions-hierarchy approach to study four correlation matrices ( N = 230 to 710; 38 to 63 tests), and a large dataset ( N = 16,823; 44 tests). Results provided (a) insights into relationships between intelligence constructs across the hierarchy of factor solutions, and (b) evidence that intelligence has a 1–2–3–5 hierarchy of factor solutions with a g factor at the top, gc and gf factors at the second level, a speed–reasoning–knowledge taxonomy at the third level, and possibly a speed-reasoning–fluency–knowledge–memory/perception taxonomy at the fifth level. [ABSTRACT FROM AUTHOR]

Abstract: Positive conceptions of perfectionism () suggest that perfectionistic strivings may form part of a healthy pursuit of excellence and are associated with higher academic achievement and higher performance in laboratory tasks. To extend such research findings, the present study explores if perfectionistic strivings also predict aptitude test performance, while controlling for conscientious achievement striving. A sample of 111 participants, who completed measures of perfectionistic strivings and conscientious achievement striving, were given a set of aptitude tests comprising reasoning, speed, and work sample tests. Results showed that, while conscientious achievement striving was unrelated to performance in all tests, perfectionistic strivings predicted higher performance in both reasoning tests and work sample tests. Apart from providing further support for the view that perfectionistic strivings are a positive personality characteristic, the findings may also have relevance for applied psychology, as they suggest that testees who strive for perfection may achieve better results in aptitude tests which are routinely used in personnel selection and assessment. [Copyright &y& Elsevier]