Perfectionism,over commitment to work,and burnout in employees seeking workplace counselling

Perfectionism is linked to a range of psychological disorders. Burnout is an important construct in the working population and has been found to overlap with related constructs such as depression. There has been limited research conducted between perfectionism and burnout. This study aimed to examine the link between perfectionism and burnout and if procrastination and over commitment to work were mediators of the relationship. There were 69 participants recruited who were seeking counselling from a counselling firm, providing employee assistance services. The results indicated that procrastination was not a significant mediator of the relationship between concern over mistakes (CM) perfectionism and burnout. There was a significant mediating relationship found, where overcommitment to work mediated the relationship between CM perfectionism leading to burnout. The implications of the study in terms of interventions for employees suffering burnout, and directions for future research in regards to the relationship between perfectionism and burnout are considered.     

The standard model for perceived magnitude: a framework for (almost) everything known about it

The psychophysics of perceived magnitude entails three aspects of sensory systems: range of sensitivity (dynamic range [DR]), resolving power (the capacity to resolve small changes in stimulus intensity), and the form of the function relating perceived magnitude to signal strength throughout the DR. A simple model is proposed that integrates what is known about all three aspects into a single framework. According to the model, perceived magnitude is a power function of stimulus strength (S. S. Stevens, 1956), and both the exponent of that function and a measure of resolving power are inversely related to the log of DR (R. Teghtsoonian, 1971). The DR is thought to have a characteristic value for each sensory system and may be estimated directly by measurement of upper and lower limits, or indirectly by estimating the exponent of the power function under optimal conditions. A central feature of the model is that all DRs are assumed to be subjectively equal. It is also suggested that the impression of perceived magnitude may be mediated by a single mechanism, regardless of the sensory system that is activated. It remains to be seen whether brain science is able to identify a neural basis for such a mechanism.     

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.