整合后验信息的多分属性认知诊断信效度指标

分类一致性和准确性是认知诊断评估中的重要指标,前者反映信度问题,后者反映效度问题。已有研究提出的指标均是基于二分属性,而多分属性的后验概率分布和属性边际概率分布均不同于二分属性,需要构建新指标来衡量多分属性情景下的信效度。本研究基于二分思想,构建出二元式信息指标用于计算多分属性测验中的信效度,并通过实验设计考察了新指标在多种影响因素中的表现,验证了新指标的有效性。最后,为多分属性诊断测验的编制提供了建议,并提出未来研究方向。     

多级评分聚类诊断法的影响因素

从测验和被试两个层面探讨了属性数目、属性层级关系、被试知识状态分布、属性层级误设和Q矩阵误设等因素对GRCDM的影响, 以进一步考察GRCDM的特性。研究发现：(1)GRCDM对属性数目无依赖, 随属性数目的增多判准率反而增高; (2)被试知识状态分布对GRCDM判准率高低无影响; (3)属性层级误设对GRCDM的影响与属性层级类型有关, 当属性层级为无结构型和发散型时, “属性层级关系错乱”的判准率降幅最大; (4)Q矩阵误设对GRCDM的影响因层级关系而异, 收敛型和发散型受影响较小, 无结构型和线型的判准率在属性既冗余又缺失时降幅最大。     

Family Demands and Job Attribute Preferences: A 4-Year Longitudinal Study of Women and Men

A longitudinal data set was used to examine the relationships between family demands and job attribute preferences. Study participants were 207 students who responded to surveys upon entering the MBA program of a large university and to follow-up surveys 1, 2, and 3 years later. Hierarchical regression analyses indicated that preferences for short, flexible work hours at earlier time periods positively predicted hours of household labor at later times, which supports a rational action model. Significant interaction effects indicated that the relationship between the importance of work hours and household labor was stronger for women than for men, which indicates that women were more likely than men to develop plans for combining work and family. Higher levels of household labor were associated with increased preferences for short, flexible work hours, and a comfortable work environment, which supports an accommodation model, but MBA students performing more household labor did not show a reduction in the desire for high salaries, good benefits, and intrinsically rewarding work.     

Testing unidimensionality in polytomous Rasch models

A fundamental assumption of most IRT models is that items measure the same unidimensional latent construct. For the polytomous Rasch model two ways of testing this assumption against specific multidimensional alternatives are discussed. One, a marginal approach assuming a multidimensional parametric latent variable distribution, and, two, a conditional approach with no distributional assumptions about the latent variable. The second approach generalizes the Martin-Löf test for the dichotomous Rasch model in two ways: to polytomous items and to a test against an alternative that may have more than two dimensions. A study on occupational health is used to motivate and illustrate the methods.The authors would like to thank Niels Keiding, Klaus Larsen and the anonymous reviewers for valuable comments to a previous version of this paper. This research was supported by a grant from the Danish Research Academy and by a general research grant from Quality Metric, Inc.     

Thinking about product attributes: Investigating the role of unconscious valence processing in attribute framing

In the present investigation we conducted three studies to examine how unconscious valence processing influences participants' quality judgments in an attribute-framing task. In Studies 1 and 2 we observed how individuals who had depleted cognitive resources, through distraction (Study 2) and time constraint (Study 3), differed in their responses to an attribute-framing task. In Study 3 we subliminally primed participants with attribute frames and then presented them with a frameless decision task. Our results revealed that attribute framing arises from unconscious valence processing and conscious processing may only play a role when the frame is especially salient.     

Tatsuoka 矩阵理论的修正

K .K.Tatsuoka和她同事开发的规则空间模型（RSM）是一种在国内外有较大影响的认知诊断模型,但是Tatsuoka的RSM中 矩阵理论存在缺陷和错误,这些失误使得RSM中用布尔描述函数（BDF）计算被试理想项目反应模式（IRP）的方法缺乏理论依据。这里揭示了Tatsuoka的 矩阵理论的缺陷和错误并引进既不使用BDF又便于应用的计算IRP的方法;接着还介绍一种由可达阵计算简化 阵的方法,该方法显示了可达阵在构造认知诊断测验的重要性。这些结果对丰富 矩阵理论及正确使用RSM进行认知诊断有一定的意义     

MCDM methods in strategic planning of forestry on state‐owned lands in Finland: applications and experiences

The forests in Finland have been under intensive planning for decades. Currently, mathematical programming is widely used in planning of wood production. Today's multi‐functional forestry, however, calls for more flexible decision support methods. MCDM tools have been used in responding to fresh planning challenges. For example, the Finnish Forest and Park Service, entrusted with the care of the vast majority of state‐owned natural resources in Finland, endeavours to produce large‐scale natural resource plans satisfying the needs of both economic, social, and ecological sustainability. Participatory approach is applied in the process. Several forestry applications of MCDM methods, particularly those making use of the AHP or the HIPRE program, have been presented. Also, the outranking methods ELECTRE and PROMETHEE have been tested. Due to the nature of forestry applications, statistical techniques for analysing uncertainties in pairwise comparisons and for utilizing interval judgement data have been developed to improve the usability of the AHP. Recently, a hybrid method called A'WOT, making use of the AHP and SWOT, was also introduced into strategic forest planning. This paper summarizes the experiences gained in applying a MAVT and two outranking methods in connection with a participatory natural resource planning process in Finland. In addition, some results of the method development work related to application needs are briefly presented. The details of the planning cases reviewed here have previously been presented in forestry journals. The purpose of this paper is not only to show how MCDM methods have been applied in forestry, but also to discuss the usability and usefulness of MCDM methods from the viewpoint of supporting forestry decision making—and how they might further be improved. Also, some perspectives for the future development work of MCDM applications in the field of natural resource management are focused on. As a conclusion, the use of more than just one MCDM method in a single planning process is seen usually recommendable. In addition, developing hybrid MCDM methods is regarded as a potential direction for future research. Also, closer co‐operation between method developers and appliers is called for to produce more useful applications. Copyright © 2002 John Wiley & Sons, Ltd.     

The unique correspondence of the item response function and item category response functions in polytomously scored item response models

The item response function (IRF) for a polytomously scored item is defined as a weighted sum of the item category response functions (ICRF, the probability of getting a particular score for a randomly sampled examinee of ability ). This paper establishes the correspondence between an IRF and a unique set of ICRFs for two of the most commonly used polytomous IRT models (the partial credit models and the graded response model). Specifically, a proof of the following assertion is provided for these models: If two items have the same IRF, then they must have the same number of categories; moreover, they must consist of the same ICRFs. As a corollary, for the Rasch dichotomous model, if two tests have the same test characteristic function (TCF), then they must have the same number of items. Moreover, for each item in one of the tests, an item in the other test with an identical IRF must exist. Theoretical as well as practical implications of these results are discussed.This research was supported by Educational Testing Service Allocation Projects No. 79409 and No. 79413. The authors wish to thank John Donoghue, Ming-Mei Wang, Rebecca Zwick, and Zhiliang Ying for their useful comments and discussions. The authors also wish to thank three anonymous reviewers for their comments.     

Additive conjoint isotonic probabilistic models (ADISOP)

The ISOP-model or model of twodimensional or bi-isotonicity (Scheiblechner, 1995) postulates that the probabilities of ordered response categories increase isotonically in the order of subject ability and item easiness. Adding a conventional cancellation axiom for the factors of subjects and items gives the ADISOP model where the c.d.f.s of response categories are functions of an additive item and subject parameter and an ordinal category parameter. Extending cancellation to the interactions of subjects and categories as well as of items and categories (independence axiom of the category factor from the subject and item factor) gives the CADISOP model (completely additive model) in which the parallel c.d.f.s are functions of the sum of subject, item and category parameters. The CADISOP model is very close to the unidimensional version of the polytomous Rasch model with the logistic item/category characteristic(s) replaced by nonparametric axioms and statistics. The axioms, representation theorems and algorithms for model fitting of the additive models are presented.