On the optimality of the simultaneous redundancy transformations

The objective of this paper is to introduce and motivate additional properties and interpretations for the redundancy variables. It is shown that these variables can be derived by application of certain invariance arguments and without reference to the index of redundancy. In addition, an optimality property for the variables is presented which is important whenever one restricts attention in a study to a subset of the redundancy variables. This optimality property pertains to the subset rather than to the individual variables.This paper is based in part on the author's doctoral dissertation, Department of Statistics, Princeton, University. Research was conducted under the supervision of Lawrence S. Mayer.     

Covariance structure analysis in several populations

This paper is concerned with the study of covariance structural models in several populations. Estimation theory of the parameters that are subject to general functional restraints is developed based on the generalized least squares approach. Asymptotic properties of the constrained estimator are studied; and asymptotic chi-square tests are presented to evaluate appropriate model comparisons. The method of multipliers and the standard reparametrization technique are discussed in obtaining the estimates. The methodology is demonstrated by a set of real data.Computer facilities were provided by the Computer Services Center, The Chinese University of Hong Kong. The authors are indebted to several anonymous reviewers for suggestions for improvement of this paper.     

The polyserial correlation coefficient

The polyserial and point polyserial correlations are discussed as generalizations of the biserial and point biserial correlations. The relationship between the polyserial and point polyserial correlation is derived. The maximum likelihood estimator of the polyserial correlation is compared with a two-step estimator and with a computationally convenient ad hoc estimator. All three estimators perform reasonably well in a Monte Carlo simulation. Some practical applications of the polyserial correlation are described.By coincidence, the first author and the second and third authors learned that they were working independently on closely related problems and, consequently, decided to write a jointly authored paper.     

Canonical analysis of longitudinal and repeated measures data with stationary weights

When measuring the same variables on different occasions, two procedures for canonical analysis with stationary compositing weights are developed. The first, SUMCOV, maximizes the sum of the covariances of the canonical variates subject to norming constraints. The second, COLLIN, maximizes the largest root of the covariances of the canonical variates subject to norming constraints. A characterization theorem establishes a model building approach. Both methods are extended to allow for Cohort Sequential Designs. Finally a numerical illustration utilizing Nesselroade and Baltes data is presented.The authors wish to thank John Nesselroade for permitting us to use the data whose analysis we present.     

Precriterion stationarity in markovian learning models

Two forms of stationarity prior to criterion in absorbing Markov chains are examined. Both forms require that the probability of a particular response on a particular trial before absorption be independent of trial number. The stronger of these forms holds that this is true independent of starting state; the weaker, only for a specified set of starting probabilities. Simple, necessary and sufficient conditions for both forms are developed and applied to several examples.The author would like to thank Charles Lewis for his help in developing this article, and Peter Polson of the University of Colorado and an anonymous referee for several fruitful suggestions made in reviews of earlier versions.     

Naturalistic Partial Sleep Deprivation Leads to Greater Next-Day Anxiety: The Moderating Role of Baseline Anxiety and Depression

The detrimental effects of insufficient sleep on emotional functioning have been well established. Total sleep deprivation usually leads to increased anxiety and depressive symptoms the following day. However, no study has yet examined the relationships between unmanipulated partial sleep deprivation and next-day symptoms of anxiety and depression in everyday life, which this study sought to characterize. Participants (N = 94) completed daily diary surveys twice per day for 2 weeks without instructions to alter their sleep in any way. Nights of spontaneous, naturally occurring partial sleep deprivation were followed by increased levels of self-reported symptoms of anxious arousal the next day, but were unrelated to next-day symptoms of anhedonic depression or general distress. The relationship between partial sleep deprivation and next-day anxious arousal was found to be moderated by both baseline depressive symptoms and anxiety such that individuals reporting higher levels of depression or anxiety at baseline showed relatively greater increases in symptoms of anxiety following partial sleep deprivation. These results suggest that partial sleep deprivation occurring in everyday life can lead to higher next-day levels of anxious arousal, a relationship that is particularly deleterious for individuals with higher overall levels of anxiety or depressive symptoms.     

A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

Using Louis’ formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis’ formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis’ formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis’ formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis’ formula with previously existing methods. Results from these presentations suggest that Louis’ formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.     

教与学的大脑：人际神经科学助推教育研究

如何从神经生理层面刻画教育活动的人际互动模式和动态性是教育神经科学面临的一个重要挑战。人际神经科学视角为其提供了可能的解决途径; 这一新兴的视角通过记录和分析进行同一认知活动时两人或多人大脑活动之间的关联, 来揭示大脑活动的群体模式。目前, 人际神经科学方法已被应用于教育研究, 例如监控教学过程、预测教学效果和识别教学影响因素, 相应的研究成果对教育活动具有重要启示。未来的研究者可以更多地关注不同学习水平的学生大脑的互动机制及人际神经科学方法应用于技能教学及线上教学评估的巨大潜力。     

Shared lesion correlates of semantic and letter fluency in post-stroke aphasia

Lesion–symptom mapping studies have reported a temporal versus frontal dissociation between semantic and letter fluency, and mixed evidence regarding the role of white matter. Mass-univariate and multivariate lesion–symptom mapping was used to identify regions associated with semantic and letter fluency deficits in post-stroke aphasia. Multivariate LSM revealed broad networks including underlying white matter, and substantial overlap between both types of fluency, suggesting that semantic fluency and letter fluency largely rely on the same neural system. All data are available on OSF.