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.     

On a test of dimensionality in redundancy analysis

Lazraq and Cléroux (Psychometrika, 2002, 411–419) proposed a test for identifying the number of significant components in redundancy analysis. This test, however, is ill-conceived. A major problem is that it regards each redundancy component as if it were a single observed predictor variable, which cannot be justified except for the rare situations in which there is only one predictor variable. Consequently, the proposed test leads to drastically biased results, particularly when the number of predictor variables is large, and it cannot be recommended for use. This is shown both theoretically and by Monte Carlo studies.The work reported in this paper was supported by Grant A6394 to the first author from the Natural Sciences and Engineering Research Council of Canada.     

Ordinal state-trait regression for intensive longitudinal data

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).     

父母亲职压力与儿童对立违抗行为的关系:一项交叉滞后分析

采用亲职压力量表和对立违抗行为表现评估表调查了来自北京、山东和云南三地14所小学有ODD(oppositional defiant disorder)行为表现的301名儿童及其父母。通过为期两年的追踪研究,采用交叉滞后分析探讨了父母亲职压力与儿童的ODD行为之间的关系。结果发现:(1)在第一年和第二年的数据中,父母亲职压力和儿童的ODD行为表现均存在显著正相关;(2)第一年和第二年的父母亲职压力得分显著正相关,同样,两年的儿童ODD行为表现得分也显著正相关;(3)交叉滞后分析发现,对于有ODD行为表现的男孩来说,其父母第一年的亲职压力能预测他们第二年的ODD行为表现,但对于有ODD行为表现的女孩来说,其父母第一年的亲职压力并不能预测她们第二年的ODD行为表现;而不管是男孩,还是女孩,第一年的ODD行为表现都不能预测第二年的父母亲职压力。研究结果揭示出男孩父母的亲职压力可预测男孩的ODD行为表现,而不论是男孩或女孩的ODD行为表现都不能预测其父母亲职压力,说明父母亲职压力可能是男孩ODD行为表现的影响因素。