The impact of culture and education on non-verbal neuropsychological measurements: a critical review

Clinical neuropsychology has frequently considered visuospatial and non-verbal tests to be culturally and educationally fair or at least fairer than verbal tests. This paper reviews the cross-cultural differences in performance on visuoperceptual and visuoconstructional ability tasks and analyzes the impact of education and culture on non-verbal neuropsychological measurements. This paper compares: (1) non-verbal test performance among groups with different educational levels, and the same cultural background (inter-education intra-culture comparison); (2) the test performance among groups with the same educational level and different cultural backgrounds (intra-education inter-culture comparisons). Several studies have demonstrated a strong association between educational level and performance on common non-verbal neuropsychological tests. When neuropsychological test performance in different cultural groups is compared, significant differences are evident. Performance on non-verbal tests such as copying figures, drawing maps or listening to tones can be significantly influenced by the individual's culture. Arguments against the use of some current neuropsychological non-verbal instruments, procedures, and norms in the assessment of diverse educational and cultural groups are discussed and possible solutions to this problem are presented.     

Robust nonorthogonal analyses revisited: An update based on trimmed means

Three approaches to the analysis of main and interaction effect hypotheses in nonorthogonal designs were compared in a 2×2 design for data that was neither normal in form nor equal in variance. The approaches involved either least squares or robust estimators of central tendency and variability and/or a test statistic that either pools or does not pool sources of variance. Specifically, we compared the ANOVA F test which used trimmed means and Winsorized variances, the Welch-James test with the usual least squares estimators for central tendency and variability and the Welch-James test using trimmed means and Winsorized variances. As hypothesized, we found that the latter approach provided excellent Type I error control, whereas the former two did not.Financial support for this research was provided by grants to the first author from the National Sciences and Engineering Research Council of Canada (#OGP0015855) and the Social Sciences and Humanities Research Council (#410-95-0006). The authors would like to express their appreciation to the Associate Editor as well as the reviewers who provided valuable comments on an earlier version of this paper.     

Distinguishing genetic from nongenetic medical tests: Some implications for antidiscrimination legislation

Genetic discrimination is becoming an increasingly important problem in the United States. Information acquired from genetic tests has been used by insurance companies to reject applications for insurance policies and to refuse payment for the treatment of illnesses. Numerous states and the United States Congress have passed or are considering passage of laws that would forbid such use of genetic information by health insurance companies. Here we argue that much of this legislation is severely flawed because of the difficulty in distinguishing genetic from nongenetic tests. In addition, barring the use by insurance companies of a genetic test but not a nongenetic test (conceivably for the same multifactorial disease) raises issues of fairness in health insurance. These arguments suggest that ultimately the problems arising from genetic discrimination cannot be solved by narrowly focused legislation but only by a modification of the entire health care system.     

A monte carlo study of kruskal's variance based measure on stress

Researchers in the past ten years have studied various parameters involved in nonmetric multidimensional scaling by utilizing Monte Carlo procedures. This paper develops stress distributions using Kruskal's second stress formula based upon a null hypothesis of equal likelihood in the ranking of a set of proximities. These distributions can serve to determine whether a set of data has other than random structure.This research was supported in part by a grant from the Baruch College Scholar Assistance program.     

Statistical inference based on ranks

This paper develops a unified approach, based on ranks, to the statistical analysis of data arising from complex experimental designs. In this way we answer a major objection to the use of rank procedures as a major methodology in data analysis. We show that the rank procedures, including testing, estimation and multiple comparisons, are generated in a natural way from a robust measure of scale. The rank methods closely parallel the familiar methods of least squares, so that estimates and tests have natural interpretations.This research was supported in part by grant MCS76-07292 from the National Science Foundation.     

Generalized concordance

Based on a simple nonparametric procedure for comparing two proximity matrices, a measure of concordance is introduced that is appropriate whenK independent proximity matrices are available. In addition to the development of a general concept of concordance and specific techniques for its evaluation within and between the subsets of a partition of theK matrices, several methods are also suggested for comparing and/or for fitting a particular structure to the given data. Finally, brief indications are provided as to how the well-known notion of concordance forK rank orders can be included within the more general framework.Partial support for this research was supplied by the National Science Foundation through SOC-77-28227.     

Exact person fit indexes for the rasch model for arbitrary alternatives

A new algorithm for obtaining exact person fit indexes for the Rasch model is introduced which realizes most powerful tests for a very general family of alternative hypotheses, including tests concerning DIF as well as model-deviating item correlations. The method is also used as a goodness-of-fit test for whole data sets where the item parameters are assumed to be known. For tests with 30 items at most, exact values are obtained, for longer tests a Monte Carlo-algorithm is proposed. Simulated examples and an empirical investigation demonstrate test power and applicability to item elimination.The author wishes to thank Elisabeth Ponocny-Seliger and the reviewers for many helpful comments. All exact goodness-of-fit tests proposed in this article are implemented in the menu-driven program T-Rasch 1.0 by Ponocny and Ponocny-Seliger (1999) which can be obtained from ProGAMMA (WWW: http://www.gamma.rug.nl) and also performs nonparametric tests.     

Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions. Special thanks go to Cajo Ter Braak, Philip Hans Franses, Patrick Houweling, Pierre Legendre, three anonymous reviewers, the associate editor, and the editor for comments.     

Rule following and rule use in the balance-scale task

Quinlan et al. [Quinlan, p., van der Mass, H., Jansen, B., Booij, O., & Rendell, M. (this issue). Re-thinking stages of cognitive development: An appraisal of connectionist models of the balance scale task. Cognition, doi:10.1016/j.cognition.2006.02.004] use Latent Class Analysis (LCA) to criticize a connectionist model of development on the balance-scale task, arguing that LCA shows that this model fails to capture a torque rule and exhibits rules that children do not. In this rejoinder we focus on the latter problem, noting the tendency of LCA to find small, unreliable, and difficult-to-interpret classes. This tendency is documented in network and synthetic simulations and in psychological research, and statistical reasons for finding such unreliable classes are discussed. We recommend that LCA should be used with care, and argue that its small and unreliable classes should be discounted. Further, we note that a preoccupation with diagnosing rules ignores important phenomena that rules do not account for. Finally, we conjecture that simple extensions of the network model should be able to achieve torque-rule performance.     

Identifying similarities in cognitive subtest functional requirements: An empirical approach

In the cognitive test interpretation literature, a Rational/Intuitive, Indirect Empirical, or Combined approach is typically used to construct conceptual taxonomies of the functional (behavioral) similarities between subtests. To address shortcomings of these approaches, the functional requirements for 49 subtests from six individually administered scales of cognitive ability (WISC-IV, WPPSI-III, SB-V, WJ-III, CAS, and UNIT) were typed on index cards. Forty-eight (48) in-service and/or pre-service school psychologists were asked to sort the cards into two, three, five, or eight functionally similar piles. A multidimensional scaling analysis was performed on a 49 × 49 matrix of similarity scores collapsed across all 48 subjects. Three dimensions were extracted from the data: Reproduction of Stimuli Sequences versus Picture Viewing (Dimension 1), Timed Speed versus Untimed Power (Dimension 2), and Verbal versus Performance (Dimension 3). A hierarchical clustering procedure was then applied to the matrix data, and results were compared to previous categorization schemes identified in the test interpretation literature.