学业自我效能感与在线学习投入的关系：学习动机和心流体验的链式中介作用

为探讨学业自我效能感对在线学习投入的影响及其作用机制,采用学业自我效能感量表、学习动机量表、心流体验量表和在线学习投入量表,对310名在线学习者进行问卷调查。结果发现：（1）学业自我效能感对在线学习投入具有正向预测作用;（2）学习动机和心流体验分别在学业自我效能感和在线学习投入之间起部分中介作用;（3）学习动机、心流体验在学业自我效能感和在线学习投入之间起链式中介作用。     

社会排斥对网络偏差行为的影响:自我控制的中介作用和道德同一性的调节作用

本研究拟探讨社会排斥对大学生网络偏差行为的影响及其作用机制——自我控制的中介作用和道德同一性的调节作用。对769名大学生问卷调查的结果表明：（1）社会排斥与网络偏差行为呈显著正相关,与自我控制呈显著负相关;自我控制和道德同一性均与网络偏差行为呈显著负相关;（2）社会排斥对网络偏差行为具有显著的正向预测作用,且自我控制在这一关系中具有显著的中介作用;（3）社会排斥对网络偏差行为的直接预测作用以及自我控制的中介作用均受到道德同一性的调节,且这两种效应在道德同一性水平较低的群体中都更为显著。本研究不仅有助于进一步明晰网络偏差行为的影响因素及其发生机制,而且对大学生网络偏差行为的预防和干预还具有一定的实践指导价值。     

Hierarchical classes models for three-way three-mode binary data: interrelations and model selection

Several hierarchical classes models can be considered for the modeling of three-way three-mode binary data, including the INDCLAS model (Leenen, Van Mechelen, De Boeck, and Rosenberg, 1999), the Tucker3-HICLAS model (Ceulemans, Van Mechelen, and Leenen, 2003), the Tucker2-HICLAS model (Ceulemans and Van Mechelen, 2004), and the Tucker1-HICLAS model that is introduced in this paper. Two questions then may be raised: (1) how are these models interrelated, and (2) given a specific data set, which of these models should be selected, and in which rank? In the present paper, we deal with these questions by (1) showing that the distinct hierarchical classes models for three-way three-mode binary data can be organized into a partially ordered hierarchy, and (2) by presenting model selection strategies based on extensions of the well-known scree test and on the Akaike information criterion. The latter strategies are evaluated by means of an extensive simulation study and are illustrated with an application to interpersonal emotion data. Finally, the presented hierarchy and model selection strategies are related to corresponding work by Kiers (1991) for principal component models for three-way three-mode real-valued data.     

Language as a cultural vessel in online servicescapes: Its impact on consumers' perceived risk,attitudes, and behavioural intentions

Globalisation and the broader use of the Internet have led both academia and professionals to dedicate a great deal of attention to the analysis of the cultural differences that exist within a market and between different markets. Language emerges as a relevant issue in current marketing and communication management environments, especially in online servicescape. Following this premise, the current paper examines the role of language as a vessel of cultural values, namely, individualism and uncertainty avoidance, and its impact on consumer's perceived risk, attitudes, and behavioural intentions in online servicescape. To address this objective, an experimental design was performed. The sample comprised 491 Internet users from 2 different national cultures (British and Spanish). Users were asked to browse an experimental website in their native or first language, whereas the other half of the sample browsed in their second language (British subjects browsing in Spanish and vice versa). The unique contribution of this study lies in demonstrating that online information processing is moderated by the language by which users elaborate information and their cultural values in terms of the dimensions of individualism and uncertainty avoidance. The findings provide valuable insights into the role of language in international marketing strategies, as well as in multicultural and cross‐cultural integrated marketing communications. In this regard, marketers must acknowledge the symbolic cues of language and the competitive advantage that might derive from a strategic language choice to evoke emotions and forge attitudes that are consistent with brand and corporate values.     

高中生化学问题解决思维过程口语报告编码分析

将高中生化学问题解决思维过程口语报告分为相应的10个行为指标进行人工编码和定量分析。被试是56名高中生,材料为6个中等难度的化学问题,通过“出声思考”实验,记录口语报告,并对口语报告进行编码和数据统计分析,结果发现:(1)高中优生的重复、解释、推理、归纳、总结等行为指标上明显优于中等生;(2)在解决不同类型的化学问题时,重复、设问、推理、归纳和反思等指标存在明显差异;(3)学生能力层次与问题类别在重复、推理、归纳和试误等行为指标上存在明显交互作用。     

Consumer brand choice: individual and group analyses of demand elasticity

Following the behavior-analytic tradition of analyzing individual behavior, the present research investigated demand elasticity of individual consumers purchasing supermarket products, and compared individual and group analyses of elasticity. Panel data from 80 UK consumers purchasing 9 product categories (i.e., baked beans, biscuits, breakfast cereals, butter, cheese, fruit juice, instant coffee, margarine and tea) during a 16-week period were used. Elasticity coefficients were calculated for individual consumers with data from all or only 1 product category (intra-consumer elasticities), and for each product category using all data points from all consumers (overall product elasticity) or 1 average data point per consumer (interconsumer elasticity). In addition to this, split-sample elasticity coefficients were obtained for each individual with data from all product categories purchased during weeks 1 to 8 and 9 to 16. The results suggest that: 1) demand elasticity coefficients calculated for individual consumers purchasing supermarket food products are compatible with predictions from economic theory and behavioral economics; 2) overall product elasticities, typically employed in marketing and econometric research, include effects of interconsumer and intraconsumer elasticities; 3) when comparing demand elasticities of different product categories, group and individual analyses yield similar trends; and 4) individual differences in demand elasticity are relatively consistent across time, but do not seem to be consistent across products. These results demonstrate the theoretical, methodological, and managerial relevance of investigating the behavior of individual consumers.     

Latent variable modeling in heterogeneous populations

Common applications of latent variable analysis fail to recognize that data may be obtained from several populations with different sets of parameter values. This article describes the problem and gives an overview of methodology that can address heterogeneity. Artificial examples of mixtures are given, where if the mixture is not recognized, strongly distorted results occur. MIMIC structural modeling is shown to be a useful method for detecting and describing heterogeneity that cannot be handled in regular multiple-group analysis. Other useful methods instead take a random effects approach, describing heterogeneity in terms of random parameter variation across groups. These random effects models connect with emerging methodology for multilevel structural equation modeling of hierarchical data. Examples are drawn from educational achievement testing, psychopathology, and sociology of education. Estimation is carried out by the LISCOMP program.Presidential address delivered at the Psychometric Society meetings in Los Angeles, USA and Leuven, Belgium, July 1989. The research was supported by Grant No. SES-8821668 from the National Science Foundation and by Grant No. OERI-G-86-003 from the Office for Educational Research and Improvement, Department of Education. I thank Leigh Burstein, Mike Hollis, Linda Muthén, and Albert Satorra for helpful discussions and Tammy Tam, Jin-Wen Yang, Suk-Woo Kim, and Lynn Short for computational assistance. Designs were created by Arlette Collier, Rita Ling and Jennifer Edic-Bryant.     

Conditions for rasch-dichotomizability of the unidimensional polytomous rasch model

Jansen and Roskam (1986) discussed the compatibility of the unidimensional polytomous Rasch model with dichotomization of the response continuum. They derived a rather strict condition in which dichotomization of multicategory data that fit the unidimensional polytomous Rasch model, results in dichotomous data which fit the dichotomous Research model with effectively the same subject parameter. In this paper a more general dichotomization condition is derived for the polytomous Rasch model, which appears less restrictive, but upholds that the intrinsic logic of the unidimensional polytomous Rasch model defies dichotomization in general. The robustness of dichotomous analysis investigated in a simulation study. It shows a close relation with the two-parameters (Birnbaum) model. Theoretical and methodological implications are discussed.The authors are indebted to H. Müller (personal communication, August 1986), for giving an example which pointed toward the core equation in this paper. The authors also acknowledge the critical comments of Th. Bezambinder and P. Wakker, and of Psychometrika's reviewers to an earlier version of this paper.     

产品类别与网上购物决策过程的关系

产品的分类方法有很多,在网络购物环境中,应用较广的是将产品分为搜索产品、体验产品和信任产品;在传统购物环境中,应用较广的是将产品分为日用品、选购品和特殊产品。文献的研究结果表明,消费者在网上购买不同类别的产品时,网上购物决策过程中的信息搜索、网上购物意愿和偏好等行为存在着许多不同之处,文章对此进行了详细的介绍,并对今后的研究方向提出了建议     

Pooling data versus averaging model fits for some prototypical multinomial processing tree models

For a study with multinomial data where there are n_g individuals and with each person having n_r test trials, the question arises as to how to fit the parameters of a multinomial processing tree (MPT) model. Should each parameter be estimated for each individual and then averaged to obtain a group estimate, or should the frequencies in the multinomial categories be pooled so that the model is fit once for the entire group? This basic question is explored with a series of Monte Carlo simulations for some prototypical MPT models. There is a general finding of a pooling advantage for the case where there is a single experimental condition. Also when there are different experimental conditions, there is reduced bias for detecting condition differences for a method based on the pooled data. Although the focus of the paper is on multinomial models, a general theorem is advanced that establishes a basic condition that determines whether there is or is not a difference between the averaging of individual estimates and the estimate based on the pooled data.