Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

Order‐constrained linear optimization

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order‐constrained linear least‐squares (OCLO) optimization algorithm that maximizes the linear least‐squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out‐of‐sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat‐tailed distributions), OCLO shows less bias and variance, and substantially better out‐of‐sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data.     

AFFECT,SYMPTOM, FANTASY,DREAM: CLINICAL AND THEORETICAL CONSIDERATIONS

A symptom being studied in the process of analysis can be seen as not unlike the unconscious affect it sprang from. The author presents a case in which a symptom, premature ejaculation, was analogous to the unconscious affect of guilt, which itself seemed to be a premature defensive transformation of a deeper current of anger. Guilt was interpreted as if it were a psychic premature ejaculation, a defensive derailment of anger. Fantasy and dream seemed to be engaged in similar transformations, with a fantasy of “premature incarceration” not unlike the symptom itself in its analogous functioning. Analysis of affect, symptom, fantasy, and dream in complex, integrative analytic process led not only to resolution of the symptom itself, but also to a deeper understanding of the mind's complex functioning in general.     

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.     

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.     

Individual ambidexterity: the duality of exploration and exploitation and its relationship with innovative performance

Organizational ambidexterity has been established as an important antecedent of organizational innovation and performance. Recently, researchers have started to argue that ambidexterity is not only essential at the organizational, but also at the individual level. Thus, to be innovative, individuals need to engage in both explorative and exploitative behaviours. However, questions remain regarding the optimal balance of explorative and exploitative behaviours and how ambidexterity can be operationalized. At the organizational level, most empirical research utilized either the difference between, or the product of, exploration and exploitation. In this article, we criticize these approaches on conceptual and methodological grounds and argue for an alternative operationalization of ambidexterity: polynomial regression and response surface methodology. In two diary studies with daily and weekly data, we demonstrate the advantages of this approach. We discuss implications for ambidexterity research and innovation practice.     

The regression trunk approach to discover treatment covariate interaction

The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable withmultiple covariates. The performance of RTA is compared to the classical method of forward stepwise regression. The results of two simulation studies, in which the true interactions are modeled as threshold interactions, show that RTA detects the interactions in a higher number of cases (82.3% in the first simulation study, and 52.3% in the second) than stepwise regression (56.5% and 20.5%). In a real data example the final RTA model has a higher cross-validated variance-accounted-for (29.8%) than the stepwise regression model (12.5%). All of these results indicate that RTA is a promising alternative method for demonstrating differential effectiveness of treatments. Supported by The Netherlands Organization for Scientific Research (NWO), grant 030-56403 to Jacqueline Meulman for the Pioneer project “Subject-Oriented Multivariate Analysis,” and grant 451-02-058 to Elise Dusseldorp for the Veni project “Modeling interaction effects as small trees in regression and classification.” We thank Bram Bakker for making the data of his doctoral dissertation available, and David Hand, Jerome Friedman, Bart Jan van Os, Philip Spinhoven, and the anonymous reviewers for their helpful suggestions. Special thanks are due to Lawrence Hubert.     

An experimentally confirmed statistical model on arm movement

The purpose of this research work was to develop a methodology to model arm movement in normal subjects and neurologically impaired individuals through the application of a statistical modelling method. Thirteen subjects with Parkinson's disease and 29 normal controls were recruited to participate in an arm motor task. An infrared optoelectronic kinematic movement analysis system was employed to record arm movement at 50 times per second. This study identified the modified extended Freundlich model as one that could be used to describe this task. Results showed that this model fit the data well and that it has a good correspondence between the observed and the predicted data. However, verification of the model showed that the residuals contained a sizeable autocorrelation factor. The Cochrane and Orcutt method was applied to remove this factor, which improved the fit of the model. Results showed that Parkinson's disease subjects had a higher autocorrelation coefficient than the normal subjects for this task. A positive correlation (r(s) = 0.72, p < 0.001) was found between the Langton-Hewer stage and the autocorrelation coefficient of PD subjects. This finding suggests that if autocorrelation is positively correlated with disease progression, clinicians in their clinical practice might use the autocorrelation value as a useful indicator to quantify the progression of a subjects' disease. Significant differences in model parameters were seen between normal and Parkinson's disease subjects. The use of such a model to represent and quantify movement patterns provides an important base for future study.     

中文阅读及发展性阅读障碍眼动研究新进展

中文阅读眼动特征的评估参数主要包括注视点停留位置、眼跳、回视和注视时间等。基于眼动技术,并结合生理技术,研究者已揭示出正常读者中文阅读的多方面特点和规律。但对发展性阅读障碍的眼动研究,国内还没有开展起来。从上述两个角度出发,这里总结了正常读者中文阅读的眼动研究,并探讨了发展性阅读障碍者的眼动特征及机制。     

Models for the statistics and mechanisms of response speed and accuracy

Van Breukelen offers a promising method for modeling both response speed and response accuracy. However, the underlying conception of both dependent measures is somewhat flawed, leading the author to conclude that the approach possesses limitations that, under revised assumptions, may not hold. The central misconception, and a set of related misconceptions, is addressed, and it is suggested that this approach holds a good deal of promise for application in the perceptual and cognitive sciences.