阅读成语时最佳注视位置的实验研究

本研究以大学生为被试,探讨阅读成语时最佳注视位置的问题。按成语的结构,将其分为四种类型,实验一用DMDX系统完成,通过操纵被试首次注视成语的位置．要求他们完成语义判断任务。实验二用EyelinkⅡ眼动仪完成,要求被试阅读句子,分析他们再注视成语的次数和时间。两个实验的结果表明,在阅读成语时存在最佳注视位置,这个位置因成语结构的不同而变化。     

In- and outgroup representation in a dynamic society: Hong Kong after 1997

Reunification with the People's Republic of China in 1997 draws attention to the identity of Hong Kong people along with their representation and perception of in- and outgroups. We gathered current data on in- and outgroup representations, self-categorizations, and the willingness to assimilate in this dynamic sociocultural environment. A new experimental method was developed to measure the structure and feature dimensions of in- and outgroup representation in 25 university students and 25 employees. Results showed that the structure of mental representations had changed after reunification. Interestingly, significant feature dimension of intergroup perception, such as modernity and Confucian values, proved to be consistent with earlier research. In the present study, it became obvious that such feature dimensions played an important role in discriminating between in- and outgroups.     

A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables

In the distance approach to nonlinear multivariate data analysis the focus is on the optimal representation of the relationships between the objects in the analysis. In this paper two methods are presented for including weights in distance-based nonlinear multivariate data analysis. In the first method, weights are assigned to the objects while the second method is concerned with differential weighting of groups of variables. When each analysis variable defines a group the latter method becomes a variable weighting method. For objects the weights are assumed to be given; for groups of variables they may be given, or estimated. These weighting schemes can also be combined and have several important applications. For example, they make it possible to perform efficient analyses of large data sets, to use the distance-based variety of nonlinear multivariate data analysis as an addition to loglinear analysis of multiway contingency tables, and to do stability studies of the solutions by applying the bootstrap on the objects or the variables in the analysis. These and other applications are discussed, and an efficient algorithm is proposed to minimize the corresponding loss function.This study is funded by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the PIONEER project Subject Oriented Multivariate Analysis to the third author.     

Homogeneity analysis withk sets of variables: An alternating least squares method with optimal scaling features

Homogeneity analysis, or multiple correspondence analysis, is usually applied tok separate variables. In this paper we apply it to sets of variables by using sums within sets. The resulting technique is called OVERALS. It uses the notion of optimal scaling, with transformations that can be multiple or single. The single transformations consist of three types: nominal, ordinal, and numerical. The corresponding OVERALS computer program minimizes a least squares loss function by using an alternating least squares algorithm. Many existing linear and nonlinear multivariate analysis techniques are shown to be special cases of OVERALS. An application to data from an epidemiological survey is presented.This research was partly supported by SWOV (Institute for Road Safety Research) in Leidschendam, The Netherlands.