The impact of the position effect on the factorial structure of the Culture Fair Test (CFT)

The Culture Fair Test (CFT) is a psychometric test of fluid intelligence consisting of four subtests; Series, Classification, Matrices, and Topographies. The four subtests are only moderately intercorrelated, doubting the notion that they assess the same construct (i.e., fluid intelligence). As an explanation of these low correlations, we investigated the position effect. This effect is assumed to reflect implicit learning during testing. By applying fixed-links modeling to analyze the CFT data of 206 participants, we identified position effects as latent variables in the subtests; Classification, Matrices, and Topographies. These position effects were disentangled from a second set of latent variables representing fluid intelligence inherent in the four subtests. After this separation of position effect and basic fluid intelligence, the latent variables representing basic fluid intelligence in the subtests Series, Matrices, and Topographies could be combined to one common latent variable which was highly correlated with fluid intelligence derived from the subtest Classification (r=.72). Correlations between the three latent variables representing the position effects in the Classification, Matrices, and Topographies subtests ranged from r=.38 to r=.59. The results indicate that all four CFT subtests measure the same construct (i.e., fluid intelligence) but that the position effect confounds the factorial structure

The position effect in a Rasch-homogenous test: A fixed-links modeling approach.

The position effect describes the influence of just-completed items in a psychological scale on subsequent items. This effect has been repeatedly reported for psychometric reasoning scales and is assumed to reflect implicit learning during testing. One way to identify the position effect is fixed-links modeling. With this approach, two latent variables are derived from the test items. Factor loadings of one latent variable are fixed to 1 for all items to represent ability-related variance. Factor loadings on the second latent variable increase from the first to the last item describing the position effect. Previous studies using fixed-links modeling on the position effect investigated reasoning scales constructed in accordance with classical test theory (e.g., Raven’s Progressive Matrices) but, to the best of our knowledge, no Rasch-scaled tests. These tests, however, meet stronger requirements on item homogeneity. In the present study, therefore, we will analyze data from 239 participants who have completed the Rasch-scaled Viennese Matrices Test (VMT). Applying a fixed-links modeling approach, we will test whether a position effect can be depicted as a latent variable and separated from a latent variable representing basic reasoning ability. The results have implications for the assumption of homogeneity in Rasch-homogeneous tests.

Using multiple landscape genetic approaches to test the validity of genetic clusters in a species characterized by an isolation-by-distance pattern

Bayesian clustering methods are typically used to identify barriers to gene flow, but they are prone to deduce artificial subdivisions in a study population characterized by an isolation-by-distance pattern (IbD). Here we analysed the landscape genetic structure of a population of wild boars (Sus scrofa) from south-western Germany. Two clustering methods inferred the presence of the same genetic discontinuity. However, the population in question was characterized by a strong IbD pattern. While landscape-resistance modelling failed to identify landscape features that influenced wild boar movement, partial Mantel tests and multiple regression of distance matrices (MRDMs) suggested that the empirically inferred clusters were separated by a genuine barrier. When simulating random lines bisecting the study area, 60% of the unique barriers represented, according to partial Mantel tests and MRDMs, significant obstacles to gene flow. By contrast, the random-lines simulation showed that the boundaries of the inferred empirical clusters corresponded to the most important genetic discontinuity in the study area. Given the degree of habitat fragmentation separating the two empirical partitions, it is likely that the clustering programs correctly identified a barrier to gene flow. The differing results between the work published here and other studies suggest that it will be very difficult to draw general conclusions about habitat permeability in wild boar from individual studies.