Constructing the exact significance level for a person fit statistic

This paper uses an extension of the network algorithm originally introduced by Mehta and Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. By assuming that item difficulties are known, the algorithm is applicable to the statistical tests either given the maximum likelihood ability estimate or conditioned on the total score. A simulation study indicates that the network algorithm is an efficient tool for computing the significance level of a person fit statistic based on test lengths of 30 items or less.     

Beyond principal component analysis: A trilinear decomposition model and least squares estimation

The paper derives sufficient conditions for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis.     

A quasi-nonmetric method for multidimensional scaling VIA an extended euclidean model

An Extended Two-Way Euclidean Multidimensional Scaling (MDS) model which assumes both common and specific dimensions is described and contrasted with the standard (Two-Way) MDS model. In this Extended Two-Way Euclidean model then stimuli (or other objects) are assumed to be characterized by coordinates onR common dimensions. In addition each stimulus is assumed to have a dimension (or dimensions) specific to it alone. The overall distance between objecti and objectj then is defined as the square root of the ordinary squared Euclidean distance plus terms denoting the specificity of each object. The specificity,s _j , can be thought of as the sum of squares of coordinates on those dimensions specific to objecti, all of which have nonzero coordinatesonly for objecti. (In practice, we may think of there being just one such specific dimension for each object, as this situation is mathematically indistinguishable from the case in which there are more than one.)We further assume that _ij =F(d _ij ) +e _ij where _ij is the proximity value (e.g., similarity or dissimilarity) of objectsi andj,d _ij is the extended Euclidean distance defined above, whilee _ij is an error term assumed i.i.d.N(0, ²).F is assumed either a linear function (in the metric case) or a monotone spline of specified form (in the quasi-nonmetric case). A numerical procedure alternating a modified Newton-Raphson algorithm with an algorithm for fitting an optimal monotone spline (or linear function) is used to secure maximum likelihood estimates of the paramstatistics) can be used to test hypotheses about the number of common dimensions, and/or the existence of specific (in addition toR common) dimensions.This approach is illustrated with applications to both artificial data and real data on judged similarity of nations.     

The role of observing and attention in establishing stimulus control.

Early theorists (Skinner, Spence) interpreted discrimination learning in terms of the strengthening of the response to one stimulus and its weakening to the other. But this analysis does not account for the increasing independence of the two performances as training continues or for increases in control by dimensions of a stimulus other than the one used in training. Correlation of stimuli with different densities of reinforcement produces an increase in the behavior necessary to observe them, and greater observing of and attending to the relevant stimuli may account for the increase in control by these stimuli. The observing analysis also encompasses errorless training, and the selective nature of observing explains the feature-positive effect and the relatively shallow gradients of generalization generated by negative discriminative stimuli. The effectiveness of the observing analysis in handling these special cases adds to the converging lines of evidence supporting its integrative power and thus its validity.     

Three-way metric unfolding via alternating weighted least squares

Three-way unfolding was developed by DeSarbo (1978) and reported in DeSarbo and Carroll (1980, 1981) as a new model to accommodate the analysis of two-mode three-way data (e.g., nonsymmetric proximities for stimulus objects collected over time) and three-mode, three-way data (e.g., subjects rendering preference judgments for various stimuli in different usage occasions or situations). This paper presents a revised objective function and new algorithm which attempt to prevent the common type of degenerate solutions encountered in typical unfolding analysis. We begin with an introduction of the problem and a review of three-way unfolding. The three-way unfolding model, weighted objective function, and new algorithm are presented. Monte Carlo work via a fractional factorial experimental design is described investigating the effect of several data and model factors on overall algorithm performance. Finally, three applications of the methodology are reported illustrating the flexibility and robustness of the procedure.We wish to thank the editor and reviewers for their insightful comments.     

Combinatorial data analysis: Association and partial association

A combinatorial data analysis strategy is reviewed that is designed to compare two arbitrary measures of proximity defined between the objects from some set. Based on a particular cross-product definition of correspondence between these two numerically specified notions of proximity (typically represented in the form of matrices), extensions are then pursued to indices of partial association that relate the observed pattern of correspondence between the first two proximity measures to a third. The attendant issues of index normalization and significance testing are discussed; the latter is approached through a simple randomization model implemented either through a Monte Carlo procedure or distributional approximations based on the first three moments. Applications of the original comparison strategy and its extensions to partial association may be developed for a variety of methodological and substantive tasks. Besides rank correlation, we emphasize the topics of spatial autocorrelation for one variable and spatial association between two and mention the connection to the usual randomization approach for one-way analysis-of-variance.     

Statistical consistency and hypothesis testing for nonmetric multidimensional scaling

The properties of nonmetric multidimensional scaling (NMDS) are explored by specifying statistical models, proving statistical consistency, and developing hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. For these models, statistical consistency often depends crucially upon how error enters the model and how data are collected and summarized (e.g., by means, medians, or rank statistics). A maximum likelihood estimator for NMDS is developed, and its relationship to the standard Shepard-Kruskal estimation method is described. This maximum likelihood framework is used to develop a method for testing the overall fit of the model.     

In-session gaming as a tool in treating adolescent problematic gaming

For some adolescent gamers, playing online games may become problematic, impairing functioning in personal, family, and other life domains. Parental and family factors are known to influence the odds that adolescents may develop problematic gaming (PG), negative parenting and conflictual family dynamics increasing the risk, whereas positive parenting and developmentally supportive family dynamics protecting against PG. This suggests that a treatment for adolescent PG should not only address the gaming behaviors and personal characteristics of the youth, but also the parental and family domains. An established research-supported treatment meeting these requirements is multidimensional family therapy (MDFT), which we adapted for use as adolescent PG treatment. We report here on one adaptation, applying in-session gaming. In-session demonstration of the “problem behavior” is feasible and informative in PG. In the opening stage of therapy, we use in-session gaming to establish an alliance between the therapist and the youth. By inviting them to play games, the therapist demonstrates that they are taken seriously, thus boosting treatment motivation. Later in treatment, gaming is introduced in family sessions, offering useful opportunities to intervene in family members' perspectives and interactional patterns revealed in vivo as the youth plays the game. These sessions can trigger strong emotions and reactions from the parents and youth and give rise to maladaptive transactions between the family members, thus offering ways to facilitate new discussions and experiences of each other. The insights gained from the game demonstration sessions aid the therapeutic process, more so than mere discussion about gaming.     

Neuropsychological identification of motor problems: Can we learn something from the feet and legs that hands and arms will not tell us?

The degree of structural and functional specialization that differentiates between upper and lower limb use in humans is quite unparalleled among primates. It is argued that less neural resources are devoted to leg and foot control than to arm and hand control, and that this aspect of lower limb innervation, together with the uniquely restricted use of the lower limb, renders lower limb function more sensitive to general neural insult. In addition, the status of leg and foot control differs from that of arm and hand control both early in life and during the later years of decline.     

汉字内隐记忆的实验研究（Ⅱ）：任务分离和反应倾向

在实验Ｉ的基础上,操纵学习时的呈现时间（２５０ｍｓ,５００ｍｓ,７５０ｍｓ）和注意变量（目标和非目标字）,运用任务分离方法及间接证明逻辑,比较了再认测验和整字偏好测验在不同条件下的辨别力（Ａ′）和反应倾向（Ｂ″）。结果：（１）双向功能分离的出现表明再认测验和偏好测验存在本质差异。根据“过程纯净”假设,可以推证内隐记忆存在于偏好测验中。此时,匹配比较法的标准未满足。（２）偏好测验中,５００ｍｓ呈现目标字时,有显著正向启动效应：７５０ｍｓ呈现非目标字时,有显著负向启动效应。（３）内隐记忆和外显记忆发生时,都具有稳定的心理状态。但是前者的报告标准较松宽,而后者的较严格。