精神分裂症病人来源记忆研究的回顾与展望

精神分裂症病人的来源记忆研究是颇受关注的认识心理学课题之一。本文围绕该类病人的来源记忆研究状况从如下几方面做出简要回顾和总结：一是精神分裂症病人的来源记忆较项目记忆更困难;二是精神分裂症病人的来源记忆成绩与其阳性症状和阴性症状之间存在一定的关联;三是智商、注意和药物等因素对精神分裂症病人的来源记忆成绩存在一定的影响;四是精神分裂症病人来源记忆的脑机制研究受到高度重视。文章最后对未来研究和临床应用进行了思考和展望。     

运用均数与协方差结构模型侦查项目功能差异

阐释了运用多组均数与协方差结构(MACS)模型侦查多级反应项目的一致性与非一致性项目功能差异(DIF)的原理与程序, 以道德自我概念量表DIF的侦查进行示例, 并对该方法进行了评价。与项目反应理论比照, MACS采用系统的、迭代的方式利用修正指数来侦查DIF, 并提供多个拟合指数协同评价模型拟合;与标准验证性因素分析相较, MACS不仅能侦查非一致性DIF, 而且能侦查一致性DIF。运用MACS侦查DIF是一种值得推荐的方法。     

用信息函数法对标准参照测验作质量分析

根据项目反应理论的信息函数法,以自主开发的“现代教育和心理测量通用分析系统”为工具,对两个大规模实施的标准参照测验进行了测验质量分析。结果说明,信息函数分析法比经典真分数理论分析法,可以获得更丰富、深刻、有针对性的资料,能对项目筛选修订、测验编制开发给出有力的指导,值得大力引进、推广。     

Biodata Validity Decay and Score Inflation with Faking: Do Item Attributes Explain Variance Across Items?

Rating scales were developed to assess the biodata dimensions offered by Mael (1991). Biodata items assessing conscientiousness were administered under honest-responding and faking-good conditions. Item attributes were examined to determine their value in predicting item validity for honest respondents and item validity for faking respondents. Analyses were also conducted to determine whether the degree of item faking was related to item attributes. Item attributes associated with item validity for honest respondents are not the same as the item attributes indicative of item validity for the faking respondents. We suggest that this makes it very difficult to develop a biodata questionnaire which will be equally valid for both honest and faking respondents.     

计算机自适应性测验(CAT)选题策略的新进展

在计算机自适应性测验(CAT)中,传统的项目选题策略正面对越来越多的问题,比如:测验的安全,项目的曝光率,项目的平衡应用等等。分层选题策略的新发展-A-STR和BAS-选题策略部分地解决了传统的选题策略所面临的难题。能够有效地控制高区分度项目的曝光率,增加低区分度项目的曝光率,平衡项目的应用,提高测验效率,降低测验成本等等。为计算机自适应测验的选题策略提供了一种更加有效的方法,也为我国开展计算机自适应测验提供了一种思路。     

Measurement in Cross-Cultural Neuropsychology

The measurement of cognitive abilities across diverse cultural, racial, and ethnic groups has a contentious history, with broad political, legal, economic, and ethical repercussions. Advances in psychometric methods and converging scientific ideas about genetic variation afford new tools and theoretical contexts to move beyond the reflective analysis of between-group test score discrepancies. Neuropsychology is poised to benefit from these advances to cultivate a richer understanding of the factors that underlie cognitive test score disparities. To this end, the present article considers several topics relevant to the measurement of cognitive abilities across groups from diverse ancestral origins, including fairness and bias, equivalence, diagnostic validity, item response theory, and differential item functioning.     

个人拟合指数对人格测验的影响及意义

个人拟合指标是考察心理测验中偏差得分模式的新方法。研究中考察了G、C、MCI、U3、U、W、ECI6、L等8个拟合指标对艾森克人格问卷信效度的影响,以及各指标与正反向题回答不一致项目数的相关。结果表明,删除不同比例拟合程度不好的个体后,测验的信效度明显提高。同时PFS可鉴别人格测验中的默认反应偏差。各指标中l对测验信效度的改善效果最为理想。     

难度－价值权衡情境下的项目选择及其眼动特征

本研究采用行为和眼动技术,通过设置难度—价值权衡情境,考察在学习时间有限的条件下,学习者如何选择学习项目,以及该过程如何发生。结果发现：(1)被试更多选择得分期望最高的项目,而非分值最高或最简单的项目。(2)选择学习项目的过程不存在计算项目得分期望的过程。结果表明,学习者项目选择不仅基于难度或价值,而是会权衡难度和价值,选择得分期望最高的项目。该过程不符合补偿性决策理论的预期,与非补偿性理论预期一致。     

解释性项目反应理论模型：理论与应用

解释性项目反应理论模型(Explanatory Item Response Theory Models, EIRTM)是指基于广义线性混合模型和非线性混合模型构建的项目反应理论(Item Response Theory, IRT)模型。EIRTM能在IRT模型的基础上直接加入预测变量, 从而解决各类测量问题。首先介绍EIRTM的相关概念和参数估计方法, 然后展示如何使用EIRTM处理题目位置效应、测验模式效应、题目功能差异、局部被试依赖和局部题目依赖, 接着提供实例对EIRTM的使用进行说明, 最后对EIRTM的不足之处和应用前景进行讨论。     

Cognitive diagnosis models for multiple strategies

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students’ proficiency profiles. However, most CDMs assume that all students adopt the same strategy when approaching problems in an assessment, which may not be the case in practice. This study develops a generalized multiple-strategy CDM for dichotomous response data. The proposed model provides a unified framework to accommodate various condensation rules (e.g., conjunctive, disjunctive, and additive) and different strategy selection approaches (i.e., probability-matching, over-matching, and maximizing). Model parameters are estimated using the marginal maximum likelihood estimation via expectation-maximization algorithm. Simulation studies showed that the parameters of the proposed model can be adequately recovered and that the proposed model was relatively robust to some types of model misspecifications. A set of real data was analysed as well to illustrate the use of the proposed model in practice.