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.     

Neurocognitive Effects of Methamphetamine: A Critical Review and Meta-analysis

This review provides a critical analysis of the central nervous system effects of acute and chronic methamphetamine (MA) use, which is linked to numerous adverse psychosocial, neuropsychiatric, and medical problems. A meta-analysis of the neuropsychological effects of MA abuse/dependence revealed broadly medium effect sizes, showing deficits in episodic memory, executive functions, information processing speed, motor skills, language, and visuoconstructional abilities. The neuropsychological deficits associated with MA abuse/dependence are interpreted with regard to their possible neural mechanisms, most notably MA-associated frontostriatal neurotoxicity. In addition, potential explanatory factors are considered, including demographics (e.g., gender), MA use characteristics (e.g., duration of abstinence), and the influence of common psychiatric (e.g., other substance-related disorders) and neuromedical (e.g., HIV infection) comorbidities. Finally, these findings are discussed with respect to their potential contribution to the clinical management of persons with MA abuse/dependence.     

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.     

Disorders in planning and strategy application in frontal lobe lesion patients

The aim of this study was to investigate deficits in planning ability using an adapted version of the Modified Six Elements Test, from the Behavioral Assessment of the Dysexecutive Syndrome—BADS [Wilson, B. A., Alderman, N., Burgess, P. W., Emslie, H., & Evans, J. J. (1996). Behavioural Assessment of the Dysexecutive Syndrome (BADS). Bury St Edmunds, U.K.: Thames Valley Test Company. Trans. Ricardo O Souza, Sergio L Schmidt. Rio de Janeiro: Cognição]. Subjects were left- and right-frontal lobe lesion patients. Other measures of executive dysfunctions used were verbal fluency, the Wisconsin Card Sorting Test, and the Trail Making Test. These other instruments were sensitive to detect executive deficits in the left frontal lobe lesion group, except the Wisconsin Card Sorting Test, which showed impairment only for the frontal lobe lesion group as a whole. The Modified Six Elements Test detected planning disorders in left frontal lobe lesion patients. The deficit of these patients was due to a greater likelihood to break the rules of the task, that is, in plan-following processes, rather than in planning the strategic approach to solve it.     

Perfectionism and exam performance: The mediating effect of task-approach goals

Perfectionistic strivings are positively correlated with students’ achievement goals and exam performance. However, so far no study has employed a prospective design investigating whether achievement goals mediate the positive relationship between perfectionistic strivings and exam performance. In the present study, 100 university students completed a measure of self-oriented perfectionism and socially prescribed perfectionism (Hewitt & Flett, 1991) and received a chapter from a textbook to study for 2–4 days. Then they returned to the lab to complete a measure of achievement goals following the 3 × 2 model (Elliot, Murayama, & Pekrun, 2011) and sit a mock exam testing their knowledge of the chapter. Multiple regressions showed that socially prescribed perfectionism negatively predicted exam performance when the overlap with self-oriented perfectionism was controlled for. In contrast, self-oriented perfectionism—a defining indicator of perfectionistic strivings—positively predicted exam performance. Moreover, task-approach goals mediated the positive relationship between self-oriented perfectionism and exam performance. The findings suggest that perfectionistic strivings make students adopt task-approach goals that help them achieve better results on exams.     

Exploring Factor Model Parameters across Continuous Variables with Local Structural Equation Models

Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.