认知诊断框架下多级评分题目的DIF检测方法及其应用

本文对多级计分认知诊断测验的DIF概念进行了界定,并通过模拟实验以及实证研究对四种常见的多级计分DIF检验方法的适用性进行理论以及实践性的探索。研究结果表明：四种方法均能对多级计分认知诊断中的DIF进行有效的检验,且各方法的表现受模型的影响不大;相较于以总分为匹配变量,以KS为匹配变量时更利于DIF的检测;以KS为匹配变量的LDFA方法以及以KS为匹配变量的曼特尔检验方法在检测DIF题目时有着最高的检验力。     

Simulation and multi‐attribute utility modelling of life cycle profit

Investments on capital goods are assessed with respect to the life cycle profit as well as the economic lifetime of the investment. The outcome of an investment with respect to these economic criteria is generally non‐deterministic. An assessment of different investment options thus requires probabilistic modelling to explicitly account for the uncertainties. A process for the assessment of life cycle profit and the evaluation of the adequacy of the assessment is developed. The primary goal of the assessment process is to aid the decision‐maker in structuring and quantifying investment decision problems characterized by multiple criteria and uncertainty. The adequacy of the assessment process can be evaluated by probabilistic criteria indicating the degree of uncertainty in the assessment. Bayesian inference is used to re‐evaluate the initial assessment, as evidence of the system performance becomes available. Thus authentication of contracts of guarantee is supported. Numerical examples are given to demonstrate features of the described life cycle profit assessment process. Copyright © 2001 John Wiley & Sons, Ltd.     

Rough approximation of pairwise comparisons described by multi‐attribute stochastic dominance

Given a finite set A of actions evaluated by a set of attributes, preferential information is considered in the form of a pairwise comparison table including pairs of actions from subset B⊂A described by stochastic dominance relations on particular attributes and a total order on the decision attribute. Using a rough sets approach for the analysis of the subset of preference relations, a set of decision rules is obtained, and these are applied to a set A\B of potential actions. The rough sets approach of looking for the reduction of the set of attributes gives us the possibility of operating on a multi‐attribute stochastic dominance for a reduced number of attributes. Copyright © 1999 John Wiley & Sons, Ltd.     

Diagnostic Classification Models: Recent Developments,Practical Issues,and Prospects

More than three decades after their introduction, diagnostic classification models (DCM) do not seem to have been implemented in educational systems for the purposes they were devised. Most DCM research is either methodological for model development and refinement or retrofitting to existing nondiagnostic tests and, in the latter case, basically for model demonstration or constructs identification. DCMs have rarely been used to develop diagnostic assessment right from the start with the purpose of identifying individuals’ strengths and weaknesses (referred to as true applications in this study). In this article, we give an introduction to DCMs and their latest developments along with guidelines on how to proceed to employ DCMs to develop a diagnostic test or retrofit to a nondiagnostic assessment. Finally, we enumerate the reasons why we believe DCMs have not become fully operational in educational systems and suggest some advice to make their advent smooth and quick.