句子理解过程中词类加工的功能性质

句法优先理论假设词类加工功能上优先于语义、动词论元结构和话语信息加工。来自德语和法语的证据显示, 词类违反阻断语义整合和动词论元结构加工, 因而词类优先于语义和动词论元结构。关于词类加工在功能上是否优先于话语信息加工, 尚无来自任何语言的证据。汉语证据尽管显示词类并不优先于语义, 但未充分讨论任务因素的影响。将来研究有必要使用ERP技术和违反范式, 从语义整合、动词论元结构加工和话语水平加工等多个层面, 同时操纵词类的正确性和非句法因素, 考察句法特性上与德语和法语不同的语言, 如汉语和韩语。这方面研究将有助于洞察一个语言的语言学特性如何制约或调整词类加工的功能性质。     

文章标记对高一学生阅读理解的影响

采用测验法,以64名高中一年级学生为被试,探讨文章标记对不同阅读水平学生的回忆成绩和问题解决的影响.结果表明:(1)不同阅读水平读者回忆成绩上的文章标记效应不显著;(2)文章标记对于不同阅读水平学生的问题解决都具有显著的影响作用;(3)不同阅读水平读者的回忆和问题解决成绩存在显著差异.     

关于我国高等师范院校公共课心理学教材整体改革的构想

本文在作者实践基础上,从建设新时期具有我国特色的高师公共课心理学教材的高度,提出四点改革构想:1.在教材体系上,围绕师范生今后“教书”和“育人”工作组织心理学基本内容,形成有机联系的“双主线’结构;2.在教材内容上,突出“三个性”——以强调心理学内容对中学教育实践指导意义的实用性,带动理论阐述上的针对性和实践运用上的可操作性;3.在教材论述上,兼顾师范生的认识规律,采用现象—规律—运用的“三段法”程式;4.在教材编写上,提倡专家、高师公共课心理学教师和中学优秀教师“三结合”原则。     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.     

A Regression Framework for Causal Mediation Analysis with Applications to Behavioral Science

We introduce and extend the classical regression framework for conducting mediation analysis from the fit of only one model. Using the essential mediation components (EMCs) allows us to estimate causal mediation effects and their analytical variance. This single-equation approach reduces computation time and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations. Additionally, we extend this framework to non-nested mediation systems, provide a joint measure of mediation for complex mediation hypotheses, propose new visualizations for mediation effects, and explain why estimates of the total effect may differ depending on the approach used. Using data from social science studies, we also provide extensive illustrations of the usefulness of this framework and its advantages over traditional approaches to mediation analysis. The example data are freely available for download online and we include the R code necessary to reproduce our results.     

Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions.     

The Impact of Item Misspecification and Dichotomization on Class and Parameter Recovery in LCA of Count Data

Mixture analysis of count data has become increasingly popular among researchers of substance use, behavioral analysis, and program evaluation. However, this increase in popularity seems to have occurred along with adoption of some conventions in model specification based on arbitrary heuristics that may impact the validity of results. Findings from a systematic review of recent drug and alcohol publications suggested count variables are often dichotomized or misspecified as continuous normal indicators in mixture analysis. Prior research suggests that misspecifying skewed distributions of continuous indicators in mixture analysis introduces bias, though the consequences of this practice when applied to count indicators has not been studied. The present work describes results from a simulation study examining bias in mixture recovery when count indicators are dichotomized (median split; presence vs. absence), ordinalized, or the distribution is misspecified (continuous normal; incorrect count distribution). All distributional misspecifications and methods of categorizing resulted in greater bias in parameter estimates and recovery of class membership relative to specifying the true distribution, though dichotomization appeared to improve class enumeration accuracy relative to all other specifications. Overall, results demonstrate the importance of accurately modeling count indicators in mixture analysis, as misspecification and categorizing data can distort study outcomes.     

Four Covariance Structure Models for Canonical Correlation Analysis: A COSAN Modeling Approach

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated. In terms of the data, one can analyze either the unstandardized or standardized variables. In terms of the unique parameters, one can estimate either the weights or loadings. Besides the unique parameters of each COSAN-CCA model, all four COSAN-CCA models also estimate the canonical correlations as their common parameters. Taken together, the four COSAN-CCA models provide the correct point estimates and standard error estimates for all commonly used CCA parameters. Two numeric examples are used to compare the standard error estimates obtained from the MIMIC approach and the COSAN modeling approach. Moreover, the standard error estimates from the COSAN modeling approach are validated by a simulation study and the asymptotic theory. Finally, software implementation and future extensions are discussed.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.     

French Validation of the Competitive Aggressiveness and Anger Scale (FVCAAS)

The aim of this study was to propose a French Validation of the Competitive Aggressiveness and Anger Scale (FVCAAS). The instrument was developed from the original version, which is composed of two subscales (six items by subscales) assessing aggressiveness and anger in competitive athletes (CAAS, Maxwell & Moores, 2007). Four studies have been conducted with 1428 competitors. In the first study, the exploratory factor analysis extracted the two-factor structure from the original version, both with good internal consistency. The second study confirmed that the two-factor structure of the instrument was consistent with the original version and showed its partial invariance across genders. The third study demonstrated the temporal stability of the FVCAAS. In the fourth study, both concurrent and discriminant validities were confirmed, supporting the validity and reliability of the FVCAAS. The contributions of this study and limitations are discussed, together with perspectives for future studies of aggressiveness in competitive sports.