Health‐adjusted neuropsychological test norms based on 463 older Swedish car drivers

There is a need for improved normative information in particular for older persons. The present study provides neuropsychological test norms on seven cognitive tests used in a sample representing the general older driving population, when uncontrolled and controlled for physical health. A group of 463 healthy Swedish car drivers, aged 65 to 84 years, participated in a medical and neuropsychological examination. The latter included tests of visual scanning, mental shifting, visual spatial function, memory, reaction time, selective attention, and simultaneous capacity. Hierarchical regression analyses demonstrated that, when uncontrolled for health, old age was associated with significant impairment on all seven tests. Education was associated with a significant advantage for all tests except most reaction time subtests. Women outperformed men on selective attention. Controlling for health did not consistently change the associations with education, but generally weakened those with age, indicating rises in normative scores of up to 0.36 SD (residual). In terms of variance explained, impaired health predicted on average 2.5%, age 2.9%, education 2.1% and gender 0.1%. It was concluded (1) that individual regression‐based predictions of expected values have the advantage of allowing control for the impact of health on normative scores in addition to the adjustment for various demographic and performance‐related variables and (2) that health‐adjusted norms have the potential to classify functional status more accurately, to the extent that these norms diverge from norms uncontrolled for physical health.     

Happiness begets children? Evidence for a bi-directional link between well-being and number of children

The purpose of the current research was to examine the association between different facets of well-being and parenthood status. Specifically, using two longitudinal data sets, the present research explored whether individuals who possess high cognitive (Studies 1 & 2), emotional, and psychological well-being (Study 2) are more likely to subsequently become parents compared to their less happy counterparts. The results of both studies demonstrated that well-being at Time 1 positively predicted number of children at Time 2, controlling for a number of relevant variables (e.g. income, age). Additional analyses revealed that the relationship between cognitive well-being and subsequently having children was particularly strong for people who did not have any children previously. Potential mechanisms explaining how and why well-being may influence parenthood are discussed.     

Constrained maximum likelihood estimation of two-level covariance structure model via EM type algorithms

In this paper, the constrained maximum likelihood estimation of a two-level covariance structure model with unbalanced designs is considered. The two-level model is reformulated as a single-level model by treating the group level latent random vectors as hypothetical missing-data. Then, the popular EM algorithm is extended to obtain the constrained maximum likelihood estimates. For general nonlinear constraints, the multiplier method is used at theM-step to find the constrained minimum of the conditional expectation. An accelerated EM gradient procedure is derived to handle linear constraints. The empirical performance of the proposed EM type algorithms is illustrated by some artifical and real examples.This research was supported by a Hong Kong UCG Earmarked Grant, CUHK 4026/97H. We are greatly indebted to D.E. Morisky and J.A. Stein for the use of their AIDS data in our example. We also thank the Editor, two anonymous reviewers, W.Y. Poon and H.T. Zhu for constructive suggestions and comments in improving the paper. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Treatment of Missing Data: Beyond Ends and Means

The ethical decision making process behind the treatment of missing data has yet to be examined in the research literature in any discipline. The purpose of the current paper is to begin to discuss this decision-making process in view of a Foucauldian framework. The paper suggests how the ethical treatment of missing data should be considered from the adoption of this theoretical framework.     

Why and how we should care about the General Data Protection Regulation

Abstract

The General Data Protection Regulation (GDPR) is the new European Union-wide (EU) law on data protection, which is a great step towards more comprehensive and more far-reaching protection of individuals' personal data. In this editorial, we describe why and how we – as researchers within the field of health psychology – should care about the GDPR. In the first part, we explain when the GDPR is applicable, who is accountable for data protection, and what is covered by the notions of personal data and processing. In the second part, we explain aspects of the GDPR that are relevant for researchers within the field of health psychology (e.g., obtaining informed consent, data minimisation, and open science). We focus on questions that researchers may ask themselves in their daily practice. Compliance with the GDPR requires adopting research practices (e.g., data minimisation and anonymization procedures) that are not yet commonly used, but serve the fundamental right to protection of personal data of study participants.     

基于开放医疗大数据的人工智能研究

医疗人工智能（artificial intelligence, AI）的发展离不开大数据的汇聚与标注,同时也引发了医疗大数据及其标注标准如何统一的问题。从AI发展对医疗大数据需求出发,指出建立医疗大数据统一标准的重要性与必要性,分析当前医疗AI技术的开发过程缺乏数据统一标准和临床共识,提出“以开放换取共识”的医疗大数据平台方案——素问系统,实现“算法工程师开发技术—临床医生编辑知识—伦理专家监控伦理”的一体化AI生产流程。最后,以素问系统中第一个开源项目——医学知识图谱,作为大数据抽取标准知识的案例,解析了如何以开放换取共识,让AI技术专家与临床专家共同打造可落地的医疗AI技术。

     

A reformulation of the general Euclidean model for the external analysis of preference data

This paper discusses least squares methods for fitting a reformulation of the general Euclidean model for the external analysis of preference data. The reformulated subject weights refer to a common set of reference vectors for all subjects and hence are comparable across subjects. If the rotation of the stimulus space is fixed, the subject weight estimates in the model are uniquely determined. Weight estimates can be guaranteed nonnegative. While the reformulation is a metric model for single stimulus data, the paper briefly discusses extensions to nonmetric, pairwise, and logistic models. The reformulated model is less general than Carroll's earlier formulation.The author is grateful to Christopher J. Nachtsheim for his helpful suggestions.     

语言对知觉的影响—— 来自颜色范畴知觉研究的证据

介绍了近年来语言和颜色范畴知觉关系研究的新进展, 特别是颜色范畴知觉效应的偏侧化、婴幼儿颜色范畴知觉的特点等。同时指出了今后需要进一步研究的问题, 如颜色范畴知觉效应偏侧化与语言的关系、右脑语言优势者颜色范畴知觉的特点、颜色加工脑区和语言加工脑区之间的关系以及婴幼儿颜色范畴知觉特点的追踪研究等。     

Limited information estimation and testing of discretized multivariate normal structural models

Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification. This research was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grants BSO2000-0661 and BSO2003-08507 of the Spanish Ministry of Science and Technology.     

Component Models for Fuzzy Data

The fuzzy perspective in statistical analysis is first illustrated with reference to the “Informational Paradigm” allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to J LR fuzzy variables as observed on I observation units. Each observation is characterized by its center and its left and right spreads (LR₁ fuzzy number) or by its left and right “centers” and its left and right spreads (LR₂ fuzzy number). Two types of component models for LR₁ and LR₂ fuzzy data are proposed. The estimation of the parameters of these models is based on a Least Squares approach, exploiting an appropriately introduced distance measure for fuzzy data. A simulation study is carried out in order to assess the efficacy of the suggested models as compared with traditional Principal Component Analysis on the centers and with existing methods for fuzzy and interval valued data. An application to real fuzzy data is finally performed. We would like to express our gratitude to the Editor, the Associate Editor, and the Referees whose comments and suggestions improved significantly the quality of the paper.