独体汉字的字形相似性研究

本文以速示呈现方式获得了一系列独体汉字和字符的混淆矩阵。用Luce（1963）的选择模型特混淆矩阵分解为对称的相似矩阵和反应倾向矢量。基于相似矩阵,以聚类分析和多维量表法探索了汉字和字符的视觉特征及其表征形式,为揭示汉字视觉特征的几何特性和潜在结构,在研究方法上作了有意义的偿试、此外,本研究的结果支持了作者（1995）关于在含“十”或“口”的独体汉字中,“十”与“口”是该类汉字的突出视觉特征的结论。     

WHAT IS THE ROLE OF SCIENCE IN THE DIALOGUE PROPOSED BY WILLIAM KLINK?

Abstract. Klink rejects the use of ecological models in environmental decision making because their predictions cannot be tested by rigorous scientific methods. I argue that models that cannot be tested according to the rigorous standards of the physical sciences can still be considered “scientific”; they are useful (and, in practice, used) for assessing the impacts of human actions on the environment and choosing between alternative courses of action. It is, however, important to be aware of the uncertainties and to make corrections as new data and insights become available. The interplay between (1) model-based decisions and action and (2) their consequences and subsequent corrections can be regarded as a dialogue between humans and nature (or God) in the sense proposed by Klink. Klink also claims that future actions should be informed by the larger vision of theology and should not be based on science. I suggest that science has an indispensible role. The larger vision is needed to respond to the fundamental religious question: How should I live—and why? But this question cannot be answered without first addressing the fundamental scientific question: How does the world work? I suggest that responses to the first question can be formulated as visions of a future state of existence that we feel compelled to strive to realize, and that science is necessary to provide “maps of reality” needed to realize visions. I also suggest that Christian traditions can probably provide adequate visions; the crucial need is for improving our “maps of reality.”     

A simple Gauss-Newton procedure for covariance structure analysis with high-level computer languages

An implementation of the Gauss-Newton algorithm for the analysis of covariance structures that is specifically adapted for high-level computer languages is reviewed. With this procedure one need only describe the structural form of the population covariance matrix, and provide a sample covariance matrix and initial values for the parameters. The gradient and approximate Hessian, which vary from model to model, are computed numerically. Using this approach, the entire method can be operationalized in a comparatively small program. A large class of models can be estimated, including many that utilize functional relationships among the parameters that are not possible in most available computer programs. Some examples are provided to illustrate how the algorithm can be used.We are grateful to M. W. Browne and S. H. C. du Toit for many invaluable discussions about these computing ideas. Thanks also to Scott Chaiken for providing the data in the first example. They were collected as part of the U.S. Air Force's Learning Ability Measurement Project (LAMP), sponsored by the Air Force Office of Scientific Research (AFOSR) and the Human Resource Directorate of the Armstrong Laboratory (AL/HRM).     

On quantifying different types of categorical data

In quantifying categorical data, constraints play an important role in characterizing the outcome. In the Guttman-type quantification of contingency tables and multiple-choice data (incidence data), the trivial solution due to the marginal constraints is typically removed before quantification; this removal, however, has the effect of distorting the shape of the total space. Awareness of this is important for the interpretation of the quantified outcome. The present study provides some relevant formulas for those cases that are affected by the trivial solution and those cases that are not. The characterization of the total space used by the Guttman-type quantification and pertinent discussion are presented.This study was supported by a grant from The Natural Sciences and Engineering Research Council of Canada to S. Nishisato.     

Analyzing preventive trials with generalized additive models

Described a new class of nonparametric regression procedures called generalized additive models (Hastie and Tibshirani, 1991) for assessing intervention effects in mental health preventive field trials. Such models are often better than analysis of covariance models for examining intervention effects adjusted for one or more baseline characteristics and for assessing potential interactions between the intervention and baseline characteristics. Because of these advantages, generalized additive models are important tools analysts should consider in evaluating preventive field trials. We apply generalized additive models as well as more standard linear models to data from a preventive trial aimed at improving mental health and school performance outcomes through a universal intervention in first and second grades. Practical guidance is given to researchers regarding when generalized additive models would be beneficial.     

Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data

A reparameterization of a latent class model is presented to simultaneously classify and scale nominal and ordered categorical choice data. Latent class-specific probabilities are constrained to be equal to the preference probabilities from a probabilistic ideal-point or vector model that yields a graphical, multidimensional representation of the classification results. In addition, background variables can be incorporated as an aid to interpreting the latent class-specific response probabilities. The analyses of synthetic and real data sets illustrate the proposed method.The authors thank Yosiho Takane, the editor and referees for their valuable suggestions. Authors are listed in reverse alphabetical order.     

A multidimensional latent trait model for measuring learning and change

A latent trait model is presented for the repeated measurement of ability based on a multidimensional conceptualization of the change process. A simplex structure is postulated to link item performance under a given measurement condition or occasion to initial ability and to one or more modifiabilities that represent individual differences in change. Since item discriminations are constrained to be equal within a measurement condition, the model belongs to the family of multidimensional Rasch models. Maximum likelihood estimators of the item parameters and abilities are derived, and an example provided that shows good recovery of both item and ability parameters. Properties of the model are explored, particularly for several classical issues in measuring change.     

A minimum-cost network-flow solution to the case V thurstone scaling problem

This paper presents an approach for determining unidimensional scale estimates that are relatively insensitive to limited inconsistencies in paired comparisons data. The solution procedure, shown to be a minimum-cost network-flow problem, is presented in conjunction with a sensitivity diagnostic that assesses the influence of a single pairwise comparison on traditional Thurstone (ordinary least squares) scale estimates. When the diagnostic indicates some source of distortion in the data, the network technique appears to be more successful than Thurstone scaling in preserving the interval scale properties of the estimates.My special thanks go to Alvin Silk, Thomas Magnanti, and Roy Welsch for their support and advice throughout the formative stages of this paper, and to V. Srinivasan for his helpful comments on a later draft of this paper. I also wish to thank the Editor, Associate Editor, and two reviewers for their constructive suggestions.James M. Lattin is Associate Professor of Marketing and Management Science and the James and Doris McNamara Faculty Fellow for 1988-1989.