Number of pages
SourcePersonality and Individual Differences, 29, 1, (2000), pp. 45-64
Article / Letter to editor
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FSW_PSY_MA Mathematische psychologie
Personality and Individual Differences
Unidimensionality was investigated for Raven's Standard Progressive Matrices, one of the most widely used intelligence tests in the world. The test was administered as part of a research project devoted to the identification of highly gifted children. Unidimensionality was tested by means of the Rasch model, which was applied to subsets A-E separately. The Rasch model was not rejected for sets A, C and D. It was rejected for sets B and E, meaning that the items of these sets measure at least two different dimensions. It was hypothesized that these dimensions are Gestalt continuation and analogical reasoning for set B, and analogical reasoning and coping for set E. In the case of set C Rasch homogeneity could be considerably improved by assuming a second factor, apart from analogical reasoning, which was identified as lack of resistance to perceptual distractors. Splitting of set B into appropriate subsets yielded two unidimensional subsets, B1 and B2. Splitting of set E yielded one unidimensional subset E1 and a heterogeneous, multidimensional subset E2. Set C was redefined by disregarding some of its items. At the level of the newly defined subset scores the factor analogical reasoning is common to all subsets. The factor Gestalt continuation is common to the subsets A and B1. However, the reliability of these subsets were very low, implying that this factor might be too weak to be distinguishable in a factor analysis. The factors coping and lack of resistance to perceptual distractors are both unique. Therefore, one might expect the emergence of only one factor when a factor analysis would be performed on all newly defined subsets. However, factor analysis of the newly defined subsets yielded two factors. Further inspection of the factor plot showed that the emergence of a second factor could be considered as an artefact due to the skewness of the subset scores.
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