Statistics as psychometrics

In this paper, modern statistics is considered as a branch of psychometrics and the question of how the central problems of statistics can be resolved using psychometric methods is investigated. Theories and methods developed in the fields of test theory, scaling, and factor analysis are related to the principle problems of modern statistical theory and method. Topics surveyed include assessment of probabilities, assessment of utilities, assessment of exchangeability, preposterior analysis, adversary analysis, multiple comparisons, the selection of predictor variables, and full-rank ANOVA. Reference is made to some literature from the field of cognitive psychology to indicate some of the difficulties encountered in probability and utility assessment. Some methods for resolving these difficulties using the Computer-Assisted Data Analysis (CADA) Monitor are described, as is some recent experimental work on utility assessment.1980 Psychometric Society presidential address.I am indebted to Paul Slovic and David Libby for valuable consultation on the issues discussed in this paper and to Nancy Turner and Laura Novick for assistance in preparation.Research reported herein was supported under contract number N00014-77-C-0428 from the Office of Naval Research to The University of Iowa, Melvin R. Novick, principal investigator. Opinions expressed herein reflect those of the author and not those of sponsoring agencies.     

On more powerful tests of judge agreement with a known standard

To establish the existence of his abilities, a judge is given the task of classifying each ofN=rs subjects into one ofr known categories, each containings of the subjects. An incomplete design is proposed whereby the judge is presented withb groups, each one containingn=rs/b<r subjects. Then different categories corresponding to members of the group are known. Using the total number of correct classifications, this method of grouping is compared to that in which the group size is equal to the number of categories. The incomplete grouping is shown to yield a more powerful test for discriminating between the null hypothesis that the judge is guessing the classifications and the alternative hypothesis that he has some definite abilities. The incomplete design is found to be most effective (powerful) when the number of subjects in a group is limited to two or three.The author is grateful for the suggestions of the referees and the editor, which greatly improved the paper.     

Stimulus affectivity of the Danish Word Association Test as measured by response heterogeneity and Rasch scaled number of prolonged reaction times

With the purpose of increasing the knowledge of the psychometric properties of the 70-item Danish Word Association Test, data from three samples of non-patients and psychiatric patients (N = 326) were used to provide two measures of affectivity of the stimulus words, response heterogeneity and reaction time prolongation. It was possible to fit an item response theory one-parameter measurement (Rasch) model to the number of reaction time prolongations (> or =3 seconds) for 54 of the stimulus words. Correlation between Rasch-model item parameters and response heterogeneity was high (r = 0.86), while no correlation was found between either of these measures and frequency of the stimulus words in the Danish language. Both measures of stimulus affectivity supported a theoretically based classification of stimulus words as emotional or neutral. Response heterogeneity measures and Rasch measurement item and person parameters for reaction time prolongations are provided.     

Visual cognitive algorithms for high-dimensional data and super-intelligence challenges

In the long run the cognitive algorithms intend to make super-intelligent machines and super-intelligent humans. This paper presents a technical process to reach specific aspects of super-intelligence that are out of the current human cognitive abilities. These aspects are inabilities to discover patterns in large numeric multidimensional data with a naked eye. This is a long-standing problem in Data Science and Modeling in general. The major obstacle is in human inability to see n-D data by a naked eye and our needs in visualization means to represent n-D data in 2-D losslessly. While these means exist their number and abilities are limited. This paper expands the class of such lossless visual methods, by further developing a new concept of Generalized Shifted Paired Coordinates. It shows the advantages of proposed reversible lossless technique by representing real data and by proving mathematical properties.     

Factor Retention Using Machine Learning With Ordinal Data

Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2–6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.     

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.     

概化理论方差分量估计的跨分布分析

方差分量估计是进行概化理论分析的关键。采用MonteCarlo模拟技术,探讨心理与教育测量数据分布对概化理论各种方法估计方差分量的影响。数据分布包括正态、二项和多项分布,估计方法包括Traditional、Jackknife、Bootstrap和MCMC方法。结果表明:(1)Traditional方法估计正态分布和多项分布数据的方差分量相对较好,估计二项分布数据需要校正,Jackknife方法准确地估计了三种分布数据的方差分量,校正的Bootstrap方法和有先验信息的MCMC方法(MCMCinf)估计三种分布数据的方差分量结果较好;(2)心理与教育测量数据分布对四种方法估计概化理论方差分量有影响,数据分布制约着各种方差分量估计方法性能的发挥,需要加以区分地使用。