概念转变理论及其发展述评

概念转变是心理与教育领域的热点问题。20多年来,研究者从不同的背景和视角研究概念转变的过程与机制,提出了基于认识论、本体论和朴素理论的多个概念转变理论,成为后续概念转变研究的理论框架。随着研究的深入,这些概念转变理论得以继承和发展,表现为融入了社会、情感等因素并具整合的趋势。本文对当前国际主流的概念转变理论及其发展加以评述,并从问题、方法和视角等方面对未来的概念转变研究加以展望,提出构建更具普适性的概念转变理论框架的设想     

An approach ton-mode components analysis

As an extension of Lastovicka's four-mode components analysis ann-mode components analysis is developed. Using a convenient notation, both a canonical and a least squares solution are derived. The relation between both solutions and their computational aspects are discussed.The first draft was written while Wansbeek was with the Netherlands Central Bureau of Statistics. We thank Jaap Verhees for performing the computations and for many discussions on the subject, John Lastovicka for kindly making available his data to us, and the Editor, the referees, Jeroen Weesie and Pieter Kroonenberg for their useful comments.     

Testing for DIF in a model with single peaked item characteristic curves: The parella model

The PARELLA model is a probabilistic parallelogram model that can be used for the measurement of latent attitudes or latent preferences. The data analyzed are the dichotomous responses of persons to items, with a one (zero) indicating agreement (disagreement) with the content of the item. The model provides a unidimensional representation of persons and items. The response probabilities are a function of the distance between person and item: the smaller the distance, the larger the probability that a person will agree with the content of the item. This paper discusses how the approach to differential item functioning presented by Thissen, Steinberg, and Wainer can be implemented for the PARELLA model. Requests for the PARELLA software should be sent to Iec Progamma PO Box 841, 9700 AV Groningen, The Netherlands.     

The integration of multidimensional scaling and multivariate analysis with optimal transformations

The recent history of multidimensional data analysis suggests two distinct traditions that have developed along quite different lines. In multidimensional scaling (MDS), the available data typically describe the relationships among a set of objects in terms of similarity/dissimilarity (or (pseudo-)distances). In multivariate analysis (MVA), data usually result from observation on a collection of variables over a common set of objects. This paper starts from a very general multidimensional scaling task, defined on distances between objects derived from one or more sets of multivariate data. Particular special cases of the general problem, following familiar notions from MVA, will be discussed that encompass a variety of analysis techniques, including the possible use of optimal variable transformation. Throughout, it will be noted how certain data analysis approaches are equivalent to familiar MVA solutions when particular problem specifications are combined with particular distance approximations.This research was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW). An earlier version of this paper was written during a stay at McGill University in Montréal; this visit was supported by a travel grant from the Netherlands Organization for Scientific Research (NWO). I am grateful to Jim Ramsay and Willem Heiser for their encouragement and helpful suggestions, and to the Editor and referees for their constructive comments.     

On the robustness of maximum likelihood scaling for violations of the error model

A Monte Carlo study was conducted to investigate the robustness of the assumed error distribution in maximum likelihood estimation models for multidimensional scaling. Data sets generated according to the lognormal, the normal, and the rectangular distribution were analysed with the log-normal error model in Ramsay's MULTISCALE program package. The results show that violations of the assumed error distribution have virtually no effect on the estimated distance parameters. In a comparison among several dimensionality tests, the corrected version of thex ² test, as proposed by Ramsay, yielded the best results, and turned out to be quite robust against violations of the error model.     

Probabilistic multidimensional scaling: Complete and incomplete data

Simple procedures are described for obtaining maximum likelihood estimates of the location and uncertainty parameters of the Hefner model. This model is a probabilistic, multidimensional scaling model, which assigns a multivariate normal distribution to each stimulus point. It is shown that for such a model, standard nonmetric and metric algorithms are not appropriate. A procedure is also described for constructing incomplete data sets, by taking into consideration the degree of familiarity the subject has for each stimulus. Maximum likelihood estimates are developed both for complete and incomplete data sets. This research was supported by National Science Grant No. SOC76-20517. The first author would especially like to express his gratitude to the Netherlands Institute for Advanced Study for its very substantial help with this research.     

A multidimensional scaling model for the size-weight illusion

The kinds of individual differences in perceptions permitted by the weighted euclidean model for multidimensional scaling (e.g., INDSCAL) are much more restricted than those allowed by Tucker's Three-mode Multidimensional Scaling (TMMDS) model or Carroll's Idiosyncratic Scaling (IDIOSCAL) model. Although, in some situations the more general models would seem desirable, investigators have been reluctant to use them because they are subject to transformational indeterminacies which complicate interpretation. In this article, we show how these indeterminacies can be removed by constructing specific models of the phenomenon under investigation. As an example of this approach, a model of the size-weight illusion is developed and applied to data from two experiments, with highly meaningful results. The same data are also analyzed using INDSCAL. Of the two solutions, only the one obtained by using the size-weight model allows examination of individual differences in the strength of the illusion; INDSCAL can not represent such differences. In this sample, however, individual differences in illusion strength turn out to be minor. Hence the INDSCAL solution, while less informative than the size-weight solution, is nonetheless easily interpretable.This paper is based on the first author's doctoral dissertation at the Department of Psychology, University of Illinois at Urbana-Champaign. The aid of Professor Ledyard R Tucker is gratefully acknowledged.     

Capturing the noonday demon: development and validation of the State Boredom Inventory

For centuries, scholars have positioned state boredom as an impediment to organizational productivity and performance given its unpleasant and distracting qualities. However, research on state boredom has been impeded by a lack of definitional consensus and measurement issues. In this article, we sought to advance organizational research on state boredom by developing the State Boredom Inventory (SBI), an 11-item measure grounded in a theoretically derived definition of state boredom. Across five studies using 10 independent samples, we develop the SBI and provide validity evidence for our measure, including content validity and convergent and discriminant validity. Our data support the conceptualization of state boredom as a higher-order multidimensional construct with three underlying dimensions: disengagement, unpleasant low arousal, and inattention.     

大学生亲社会倾向、亲社会推理以及它们的相关模式

采用《亲社会倾向测量》和《亲社会推理客观测量》研究了17至20岁大学生的六类亲社会倾向和亲社会推理的状况。发现大学生将利他的亲社会倾向列为六类倾向中的首位,接下来依次是紧急的、情绪性的、依从的、匿名的和公开的亲社会倾向;在亲社会推理五种类型中大学生选择比率最高的是内化价值定向的推理,接下来依次是刻板定向、需要定向和享乐主义定向的推理,所占比率最低的是赞扬定向的推理;六类亲社会倾向与亲社会推理之间具有特定的棚关模式．利他的和匿名的亲社会倾向与内化价值定向和刻板定向的推理正相关、与享乐主义定向及赞扬定向的推理负相关,公开的亲社会倾向与赞扬定向的推理正相关、与需要定向的推理负相关。跨文化比较证实,中美大学生的上述三个方面表现出一定的差异。