Effect of non-normality on test statistics for one-way independent groups designs

The data obtained from one‐way independent groups designs is typically non‐normal in form and rarely equally variable across treatment populations (i.e. population variances are heterogeneous). Consequently, the classical test statistic that is used to assess statistical significance (i.e. the analysis of variance F test) typically provides invalid results (e.g. too many Type I errors, reduced power). For this reason, there has been considerable interest in finding a test statistic that is appropriate under conditions of non‐normality and variance heterogeneity. Previously recommended procedures for analysing such data include the James test, the Welch test applied either to the usual least squares estimators of central tendency and variability, or the Welch test with robust estimators (i.e. trimmed means and Winsorized variances). A new statistic proposed by Krishnamoorthy, Lu, and Mathew, intended to deal with heterogeneous variances, though not non‐normality, uses a parametric bootstrap procedure. In their investigation of the parametric bootstrap test, the authors examined its operating characteristics under limited conditions and did not compare it to the Welch test based on robust estimators. Thus, we investigated how the parametric bootstrap procedure and a modified parametric bootstrap procedure based on trimmed means perform relative to previously recommended procedures when data are non‐normal and heterogeneous. The results indicated that the tests based on trimmed means offer the best Type I error control and power when variances are unequal and at least some of the distribution shapes are non‐normal.     

Exploring the dynamics of development and evolution: comment on Blair and Raver (2012)

Blair and Raver (2012) have provided an organism-in-environment conceptualization of the development of stress response physiology and its relation to the development of self-regulation. They argue that we must consider the context in which self-regulation and stress reactivity occur to understand their implications for developmental outcome. More generally, they present a cogent argument for why it is necessary to think developmentally when considering the effects of early experience on subsequent physical, emotional, cognitive, and social development. Blair and Raver's article also highlights a persistent challenge for developmental theory--how to make sense of the relationship among the various timescales over which phenotypes develop and change occurs. Their efforts to identify the factors involved in the variability and stability of self-regulation over different timescales demonstrate the dividends of integrating developmental and evolutionary perspectives to better understand the malleability of phenotypic development.     

A rational analysis of the acquisition of multisensory representations

How do people learn multisensory, or amodal, representations, and what consequences do these representations have for perceptual performance? We address this question by performing a rational analysis of the problem of learning multisensory representations. This analysis makes use of a Bayesian nonparametric model that acquires latent multisensory features that optimally explain the unisensory features arising in individual sensory modalities. The model qualitatively accounts for several important aspects of multisensory perception: (a) it integrates information from multiple sensory sources in such a way that it leads to superior performances in, for example, categorization tasks; (b) its performances suggest that multisensory training leads to better learning than unisensory training, even when testing is conducted in unisensory conditions; (c) its multisensory representations are modality invariant; and (d) it predicts ‘‘missing” sensory representations in modalities when the input to those modalities is absent. Our rational analysis indicates that all of these aspects emerge as part of the optimal solution to the problem of learning to represent complex multisensory environments.     

Effects of electromyostimulation on knee extensors and flexors strength and steadiness in older adults

It is known that electromyostimulation (EMS) alone or superimposed over voluntary contraction (EV) can effectively improve muscle strength. However, the effect of this type of training on the ability to control force production at submaximal levels is unknown. The authors examined the effects of EV training on steadiness in force production of knee extensors and flexors in older adults. Forty participants, including 20 men and 20 women, 60-77 years of age, were randomly allocated into a control group (CG) and an electromyostimulation superimposed over voluntary contraction (EVG) group. The EVG performed 30 bilateral isometric knee extension and flexion contractions per session, 3 training sessions per week, for 6 weeks. The variations in force production, expressed in absolute (standard deviation [SD]) and relative (coefficient of variation [CV]) terms, were assessed in isometric contractions at 5%, 15% and 25% maximal voluntary contraction (MVC) levels. Results indicated that MVC increased in knee extension and flexion in EVG (p < .05) after the training; steadiness CV also improved at 15% MVC in knee flexion (p < .05) but no significant changes were found in knee extension and steadiness SD. The training-induced changes in MVC were not correlated to steadiness CV that might indicate different mechanisms underlying these adaptations.     

A generalized least squares regression approach for computing effect sizes in single-case research: application examples

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice.     

Psychometric properties of the Minnesota Multiphasic Personality Inventory-Adolescent (MMPI-A) Clinical, Content, and Supplementary scales in a forensic sample

In this study, we evaluated the internal psychometric properties and external correlates of scores on the Clinical, Content, and Supplementary scales in a forensic sample of 496 adolescents (315 boys and 181 girls) who were court-ordered to receive psychological evaluations. We examined Cronbach's alpha coefficients, scale intercorrelation matrices, and frequencies of scale elevations. Further, we found varying degrees of support for the convergent and discriminant validity of scores on the MMPI-A (Butcher et al., 1992 ) Clinical, Content, and Supplementary scales. This study adds to the body of literature establishing the utility of the MMPI-A in forensic evaluations.     

TRACX: a recognition-based connectionist framework for sequence segmentation and chunk extraction

Individuals of all ages extract structure from the sequences of patterns they encounter in their environment, an ability that is at the very heart of cognition. Exactly what underlies this ability has been the subject of much debate over the years. A novel mechanism, implicit chunk recognition (ICR), is proposed for sequence segmentation and chunk extraction. The mechanism relies on the recognition of previously encountered subsequences (chunks) in the input rather than on the prediction of upcoming items in the input sequence. A connectionist autoassociator model of ICR, truncated recursive autoassociative chunk extractor (TRACX), is presented in which chunks are extracted by means of truncated recursion. The performance and robustness of the model is demonstrated in a series of 9 simulations of empirical data, covering a wide range of phenomena from the infant statistical learning and adult implicit learning literatures, as well as 2 simulations demonstrating the model's ability to generalize to new input and to develop internal representations whose structure reflects that of the items in the input sequence. TRACX outperforms PARSER (Perruchet & Vintner, 1998) and the simple recurrent network (SRN, Cleeremans & McClelland, 1991) in matching human sequence segmentation on existing data. A new study is presented exploring 8-month-olds' use of backward transitional probabilities to segment auditory sequences.