A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models

In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes. In many applications, however, parametric SEMs are not adequate to capture subtle patterns in the functions over the entire range of the predictor variable. A different but equally important limitation of traditional parametric SEMs is that they are not designed to handle mixed data types—continuous, count, ordered, and unordered categorical. This paper develops a generalized semiparametric SEM that is able to handle mixed data types and to simultaneously model different functional relationships among latent variables. A structural equation of the proposed SEM is formulated using a series of unspecified smooth functions. The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the smooth functions and the unknown parameters. Moreover, we examine the relative benefits of semiparametric modeling over parametric modeling using a Bayesian model-comparison statistic, called the complete deviance information criterion (DIC). The performance of the developed methodology is evaluated using a simulation study. To illustrate the method, we used a data set derived from the National Longitudinal Survey of Youth.     

Flexible Approaches to Computing Mediated Effects in Generalized Linear Models: Generalized Estimating Equations and Bootstrapping

In behavioral research, interest is often in examining the degree to which the effect of an independent variable X on an outcome Y is mediated by an intermediary or mediator variable M. This article illustrates how generalized estimating equations (GEE) modeling can be used to estimate the indirect or mediated effect, defined as the amount by which the regression coefficient of X on Y changes after adjusting for M. Advantages of this method are: (a) it applies to the class of generalized linear models, including linear, logistic, and Poisson regression as special cases; (b) it allows multiple independent variables and mediators in the same model; and (c) asymptotically valid standard errors and confidence intervals are obtained using standard software. This methodology is compared with the bootstrap, another general methodology that can be applied to the same broad class of models, and is evaluated using simulation in both linear and logistic regression scenarios. The methods are utilized to examine the degree to which the effect of low birthweight status on internalizing symptoms at age 20 is mediated through IQ at age 8.     

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.     

Generalized Fiducial Inference for Binary Logistic Item Response Models

Generalized fiducial inference (GFI) has been proposed as an alternative to likelihood-based and Bayesian inference in mainstream statistics. Confidence intervals (CIs) can be constructed from a fiducial distribution on the parameter space in a fashion similar to those used with a Bayesian posterior distribution. However, no prior distribution needs to be specified, which renders GFI more suitable when no a priori information about model parameters is available. In the current paper, we apply GFI to a family of binary logistic item response theory models, which includes the two-parameter logistic (2PL), bifactor and exploratory item factor models as special cases. Asymptotic properties of the resulting fiducial distribution are discussed. Random draws from the fiducial distribution can be obtained by the proposed Markov chain Monte Carlo sampling algorithm. We investigate the finite-sample performance of our fiducial percentile CI and two commonly used Wald-type CIs associated with maximum likelihood (ML) estimation via Monte Carlo simulation. The use of GFI in high-dimensional exploratory item factor analysis was illustrated by the analysis of a set of the Eysenck Personality Questionnaire data.     

Optimal Scaling of Interaction Effects in Generalized Linear Models

This study involved two phases: first, when classification was based on the calibration sample; and second, in a cross-validation setting. Computer generated data were used. Results obtained from rules based on probabilities of group membership were compared for accuracy when classifying in the discriminant space and in the predictor variable spaces. In the first phase accuracy was greater in the predictor variable spaces, while the reverse was true in the second phase. In general, rules based on probabilities of group membership were approximately equally accurate and more accurate than a rule related to a multiple regression analysis. Other findings are also discussed.     

Resource Allocation by Greek and Dutch Students: A Test of Three Models

Three models are presented that explain cross-cultural differences in the allocation of resources to various social categories. (1) Individualism-collectivism predicts, for all kinds of resources, a higher allocation in collectivistic than in individualistic societies to members of one's in group. (2) Kagitçibasi's model of family change makes a distinction between material and nonmaterial resources. Collectivistic value patterns should lead to higher allocation of nonmaterial resources than found in individualistic societies, but no such difference is expected for material investments. Both these models assume cross-cultural differences in value orientations at a high level of generality. (3) An alternative is proposed, predicting universal patterns of exchange with only context-specific cross-cultural variations at a low level of generality. Hypotheses derived from the three models are tested with data from Greece and The Netherlands. It is concluded that the results are most compatible with the third model.     

Generalized Break Equivalence II: Contextual Control Over A Generalized Pattern of Stimulus Relations

The Psychological Record - In an earlier work (Gomez, Barnes-Holmes, & Luciano, 2001), it was found that although generalized break equivalence was achieved for 3 subjects (i.e., responding...     

Generalized equivalence: A pattern of mathematical expression

A simple propositional operator is introduced which generalizes pairwise equivalence and occurs widely in mathematics. Attention is focused on a replacement theorem for this notion of generalized equivalence and its use in producing further generalized equivalences.     

Hierarchical Diagnostic Classification Models: A Family of Models for Estimating and Testing Attribute Hierarchies

Although latent attributes that follow a hierarchical structure are anticipated in many areas of educational and psychological assessment, current psychometric models are limited in their capacity to objectively evaluate the presence of such attribute hierarchies. This paper introduces the Hierarchical Diagnostic Classification Model (HDCM), which adapts the Log-linear Cognitive Diagnosis Model to cases where attribute hierarchies are present. The utility of the HDCM is demonstrated through simulation and by an empirical example. Simulation study results show the HDCM is efficiently estimated and can accurately test for the presence of an attribute hierarchy statistically, a feature not possible when using more commonly used DCMs. Empirically, the HDCM is used to test for the presence of a suspected attribute hierarchy in a test of English grammar, confirming the data is more adequately represented by hierarchical attribute structure when compared to a crossed, or nonhierarchical structure.     

共享心智模型研究现状

共享心智模型是指团队成员共同拥有的知识结构,它使得团队成员能就团队作业形成正确的解释和预期,从而协调自己的行为以适应于团队作业和其他团队成员的需求。其研究为理解和提高团队绩效提供了新的视角,从而成为了团队研究的新热点。该文首先明确其定义以及相关概念,再介绍它的几种类型,进而介绍研究中采用的测量方法,最后总结其影响因素及对团队绩效的影响。文章最后指出研究中存在的问题以及今后的研究方向。     

创造性思维计算模型研究综述

本文综述了已有的创造性思维计算模型,根据其采用的计算方法进行分类,分析这些方法的优缺点,对创造性思维的计算模拟方法进行了展望。     

Generalized Common Fate: Grouping by Common Luminance Changes

A critical step toward object recognition is the segmentation of a scene into relevant regions. One of the most important cues for segmentation is that of common fate: Elements that move together are grouped together. Here we describe a new instantiation of common fate, in which elements move together not through physical space, but through luminance space. Experiment 1 shows that when elements of a scene become brighter together or darker together, observers group those elements together. Experiments 2 and 3 show that this effect is not due to the availability of fixed luminance differences between target and background regions, but requires common changes within each region in the direction of luminance over time. The effect is differentiated from the recently discovered grouping cue of temporal synchrony, and is considered instead to be an extension of Wertheimer's original grouping factor of common fate. Common fate for luminance, or generalized common fate, is an extremely strong cue for the segmentation of a scene, yielding a tremendous advantage over grouping by fixed luminance cues.