支持死后医疗数据捐赠：呼吁对个人健康数据的伦理利用

死后医疗数据捐赠对于科学研究的意义重大。但目前不存在有针对性的准则用于指导捐赠过程。如果死后医疗数据的使用缺乏监管可能会引发多种风险,一是给他人利益造成损害;二是造成数据的不规范使用。因此,应当类比现有捐献制度,允许并鼓励捐献死后医疗数据用于科学研究,并起草专门的捐赠准则。该准则的制定应当建立在遵循五大基本原则的前提下,主张以自愿参与的方式使用个人医疗数据,反对未经书面同意将数据用于以病例档案为基础的医学研究。

     

Perfectionistic concerns predict increases in adolescents’ anxiety symptoms: a three-wave longitudinal study

Background and Objectives: Although perfectionism has been proposed to be a risk factor for the development of anxiety, research on perfectionism and anxiety symptoms in adolescents is scarce and inconclusive. The aim of the present study was to investigate whether the two higher-order dimensions of perfectionism – perfectionistic strivings and perfectionistic concerns – predict the development and maintenance of anxiety symptoms. An additional aim of the present study was to examine potential reciprocal effects of anxiety symptoms predicting increases in perfectionism.

Design: The study used a longitudinal design with three waves spaced 4–5 months apart.

Methods: A non-clinical sample of 489 adolescents aged 12–19 years completed a paper-and-pencil questionnaire.

Results: As expected, results showed a positive effect from perfectionistic concerns to anxiety symptoms, but the effect was restricted to middle-to-late adolescents (16–19 years old): Perfectionistic concerns predicted longitudinal increases in adolescents’ anxiety symptoms, whereas perfectionistic strivings did not. Furthermore, anxiety symptoms did not predict increases in perfectionism.

Conclusions: Implications for the understanding of the relationship between perfectionism and anxiety symptoms are discussed.     

“Minimax length links” of a dissimilarity matrix and minimum spanning trees

A theorem is proved stating that the set of all minimax links, defined as links minimizing, over paths, the maximum length of links in any path connecting a pair of objects comprising nodes in an undirected weighted graph, comprise the union of all minimum spanning trees of that graph. This theorem is related to methods of fitting network models to dissimilarity data, particularly a method called Pathfinder due to Schvaneveldt and his colleagues, as well as to single linkage clustering, and results concerning the relationship between minimum spanning trees and single linkage hierarchical trees.Acknowledgments: The author thanks Phipps Arabie, Lawrence J. Hubert, and K. Christoph Klauer for a number of helpful suggestions and comments on various aspects of this paper.     

Testing Process Factor Analysis Models Using the Parametric Bootstrap

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.     

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation,Model Evaluation,and Predicting Change

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.     

Accounting for Missing Correlation Coefficients in Fixed-Effects MASEM

Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In Stage 1, a pooled correlation matrix is estimated based on the reported correlation coefficients in the individual studies. In Stage 2, a structural model (such as a path model) is fitted to explain the pooled correlations. Frequently, the individual studies do not provide all the correlation coefficients between the research variables. In this study, we modify the currently optimal MASEM-method to deal with missing correlation coefficients, and compare its performance with existing methods. This study is the first to evaluate the performance of fixed-effects MASEM methods under different levels of missing correlation coefficients. We found that the often used univariate methods performed very poorly, while the multivariate methods performed well overall.     

Images of the disciplining of psychology, 1890–1940

Over the course of psychology’s first several decades, the language used to convey the subject matter gradually shifted from being free and literary to being strictly constrained and disciplined by increasingly focused theoretical demands. The project described here, “Disciplining Psychology,” aimed to depict this transformation by generating images of the faces of three highly influential psychologists—William James, Sigmund Freud, and B. F. Skinner. Each image is composed of the words used in one of each individual’s most important books. The tightening of the disciplinary vocabulary is revealed in the differences among the three arrays of words themselves, but I have also striven to reflect it in the aesthetic aspects of each image. The method used here could easily be extended to a wider array of authors, texts, and psychological topics.     

Developmental models for children's temperament: alternatives to chronometric polynomial curves

Partridge and Lerner (2007), in a secondary analysis of the New York Longitudinal Study, employed a chronometric polynomial growth curve model to argue that the developmental course of difficult temperament follows a non‐linear trajectory over the first 5 years of life. The free curve slope intercept (FCSI) growth curve model of Meredith and Tisak (1990) is presented as a preferable conceptual alternative because it contains a number of currently popular statistical models, including repeated measures multivariate analysis of variance, factor mean, linear growth, linear factor analysis, and hierarchical linear models as special cases. As such, researchers can compare the fit of each of these models relative to the FCSI model, and, at times, to each other. The present paper conducts a re‐analysis of the data, and establishes that fit of the FCSI model is arguably better than other statistical alternatives. The FCSI model is also used as the basis for identifying subgroups of individuals with their qualitatively distinct growth patterns within a growth mixture modeling framework. Copyright © 2010 John Wiley & Sons, Ltd.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity–dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.