潜在类别分析技术在心理学研究中的应用

潜在类别分析是通过对类别型的外显变量和潜在变量之间的关系建立统计模型,根据模型参数得到各种潜在类别的具体外在表现的潜在特征分类技术。该分析方法主要应用于心理行为特征的分类、控制认知心理实验中被试个体差异引起的系统误差、评价临床心理诊断的精确性,以及心理测验中的项目分析、信度分析、结构分析等。对此方法的优劣进行分析比较,表明:该方法可以与其他测量理论相结合进一步拓展其在心理测量中的应用,也可在纵向数据和多水平数据中应用。在应用中亦有提升方法技术的空间。     

二级项目题组混合IRT模型及其稳健权重估计

在心理测量和教育测量中,二级项目和题组项目是两类常见的项目类型。由这两种项目混合构成的测试在实践中有着重要的应用。被试在答题时,由于个人的潜在能力和项目难度不匹配,常常会产生异常反应,这些异常反应会影响IRT中潜在特质估计的准确性。仿真实验证明,二级项目题组混合IRT模型的稳健估计方法在出现异常值的情况下,能够比极大似然估计对被试的潜在特质做出更加准确的估计,能够满足实际测试的需求。     

瑞文高级推理测验作答反应的潜在类别分析

以瑞文高级推理测验为测试工具,对501名师范院校大学生被试的作答反应进行探索性潜在类别分析,模型统计指标支持将被试分成3个潜在类别的模型。各个题目在不同潜在类别上的条件概率存在明显的差异,基于潜在类别的项目分析可以提供更为丰富的信息,进一步分析发现,归于不同潜在类别的被试在视觉空间过程和言语分析过程上存在明显的个体差异。     

项目间多维测验作答时间数据分析：潜在特质速度间效应建模

随着计算机测验使用的普及化,被试在心理与教育测验上的作答反应时的获取也越发便利。为了充分利用项目反应时信息,单维与多维的反应时模型相继被提出。然后,在项目间多维反应时数据中,潜在特质速度之间可能存在共同关系（比如,层阶关系）,此时现有的反应时模型并不能适用。基于此,本研究提出了高阶对数正态反应时模型与双因子对数正态反应时模型。在模拟研究中,高阶对数正态反应时模型与双因子对数正态反应时模型的各参数都能被准确估计。在瑞文标准推理测验的三组测验项目的反应时数据中,双因子对数正态反应时模型表现出更为优秀的拟合效果,同时基于多个统计量说明了局部与全局潜在特质速度同时存在的必要性。因此,在项目间多维测验反应时数据分析中,非常有必要考虑多维潜在特质速度之间的共同效应。     

基于认知诊断的被试类比推理测验行为分析

本文在构建被试解题路径模型的基础上,应用多策略多成分潜在特质模型研究被试类比推理测验的行为,精细地分析了被试在类比推理过程中所使用的策略、分析了影响被试类比推理测验策略选择的因素、估计了被试在类比推理测验中所表现的各种能力.     

概率性输入,噪音“与”门(PINA)模型

当前认知诊断测验的主要目的是对被试进行合理分类,进而采用类别变量去描述被试对某技能或知识(即认知属性)的掌握情况,但该粗糙的分类方法不能精细地区分不同被试之间的差异。对此,采用掌握概率这一连续变量去描述被试对某认知属性的掌握情况是一种值得尝试的做法。本文首先基于高阶潜在特质(简称"潜质")模型给出了认知属性掌握概率的量化定义,之后与多成分潜质模型相结合提出了概率性输入,噪音"与"门(PINA)模型;其次,采用MCMC算法实现了对PINA的参数估计,结果表明参数估计程序对各参数的估计返真性均较好;最后,以ECPE数据为例来说明PINA在实际测验分析中具有可行性。     

IRT展开模型及对非累积反应机制的检测

被试回答人格测验题目时并不是特质水平越高其得分率越高,这称为非累积反应机制。广义等级展开模型GGUM就是针对这一机制提出来的。使用EPQ和五因素人格问卷发现GGUM比累积IRT模型有更好的模型拟合度和测量精度。研究结果表明GGUM有其合理性,且有助于反应心理过程机制的深入探讨。     

人格测量：从累积式模型到展开式模型

当前大多数人格测量都采用的是累积式反应模型方法,该模型假设被试在测验上的得分随其能力或特质提高而增加,但是随着人格测量技术的不断发展,这一模型的实施效果遭到了质疑,研究者们开始关注展开式模型,该模型认为被试的反应取决于被试能力和项目阈值的匹配程度,当被试能力与项目阈值完全匹配时,被试做出肯定回答的概率达到最高点,称之为“理想点”,展开式模型的目的就是找到被试的理想点,从而寻找其真正的态度强度或人格特质水平。GGUM作为一种比较成熟的展开式模型,已经开始应用于人格测量的各个领域,但仍需要进行大规模的试测,在评估和预测效度方面积累经验,建立业界认可的心理测量学标准,不断探讨和开发相应的心理测量理论和简便易行的统计程序。     

应征公民计算机自适应化拼图测验的编制

在文献回顾和参考外军有关资料的基础上,根据项目反应理论和空间能力测验的有关理论编制试题库。首先采用纸笔测验的形式进行预实验,探讨采用IRT理论编制CAT拼图测验的可行性。然后,在预实验的基础上对试题进行修订并扩充试题数量,编制计算机辅助测验。选择三参数Logistic模型,采用铆题等值设计,分7份不同的试卷在全国征兵心理检测的过程中对55777名应征公民进行施测。根据测试结果,对题目进行分析,选择高质量的题目构成CAT试题库,采用a系数分层抽样的方法控制曝光率,并采用不同的测验终止策略编制CAT拼图测验。最后用WAIS智力测验积木分测验和三门功课的考试成绩为效标,通过72名被试对CAT拼图测验进行效度验证。结果显示该测验符合项目反应理论三参数Logistic模型的假设,各题目参数比较理想,所编制的测验具有较好的信度和效度,可用于应征公民心理选拔的实践     

四参数模型对被试作答异常现象的拟合与纠正

心理与教育测验中存在着被试作答异常现象(能力测验中的猜测现象和睡眠现象, 人格测验中的非0下渐近线现象和非1上渐近线现象), 会导致被试能力或人格特征的测量偏差。在能力测验中, 研究者已提出了多种方法来纠正猜测现象和睡眠现象, 这些方法往往需要调整或删除被试作答信息, 而四参数模型不需要改变被试作答信息而能有效纠正被试能力高估或低估现象。在人格测验中存在着非0下渐近线和非1上渐近线现象, 四参数模型能增强测验项目拟合性能, 提高人格测验的准确性。     

高阶项目反应模型的发展与应用

在测量具有层阶结构的潜质时, 标准项目反应模型对项目参数估计和能力参数估计都具有较低的效率, 多维项目反应模型虽然在估计第一阶潜质时具有高效性, 但没有考虑到潜质层阶的情况, 所以它不适合用来处理具有层阶结构的潜质; 而高阶项目反应模型在处理这种具有层阶结构的潜质时, 不仅能够高效准确地对项目参数和能力参数进行估计, 而且还能同时获得高阶潜质与低阶潜质。目前存在的高阶项目反应模型有高阶DINA模型、高阶双参数正态肩型层阶模型、高阶逻辑斯蒂模型、多级评分的高阶项目反应模型和高阶题组模型。未来对高阶项目反应模型的研究方向应注意多水平高阶项目反应模型、项目内多维情况下的高阶项目反应模型以及高阶认知诊断模型。     

Specifying Ability Growth Models Using a Multidimensional Item Response Model for Repeated Measures Categorical Ordinal Item Response Data

When categorical ordinal item response data are collected over multiple timepoints from a repeated measures design, an item response theory (IRT) modeling approach whose unit of analysis is an item response is suitable. This study proposes a few longitudinal IRT models and illustrates how a popular compensatory multidimensional IRT model can be utilized to formulate such longitudinal IRT models, which permits an investigation of ability growth at both individual and population levels. The equivalence of an existing multidimensional IRT model and those longitudinal IRT models is also elaborated so that one can make use of an existing multidimensional IRT model to implement the longitudinal IRT models.     

多水平IRT的发展与应用述评

阶层线性模型是处理阶层结构数据的高级统计方法, 项目反应理论是精确测量被试能力的现代测量理论。多水平项目反应理论将阶层线性模型和项目反应理论相结合, 将项目反应模型嵌套在阶层线性模型内, 实现了项目参数和不同水平能力参数的估计, 对回归系数和误差项变异的估计也更加精确。作者概述了多水平项目反应理论的发展历程, 并从项目功能差异、测验等值、学校效能研究等方面评述了多水平项目反应理论在心理与教育测量中的应用, 总结了多水平项目反应理论的价值, 同时展望了今后的研究趋势。     

Nonparametric polytomous IRT models for invariant item ordering, with results for parametric models

It is often considered desirable to have the same ordering of the items by difficulty across different levels of the trait or ability. Such an ordering is an invariant item ordering (IIO). An IIO facilitates the interpretation of test results. For dichotomously scored items, earlier research surveyed the theory and methods of an invariant ordering in a nonparametric IRT context. Here the focus is on polytomously scored items, and both nonparametric and parametric IRT models are considered.The absence of the IIO property in twononparametric polytomous IRT models is discussed, and two nonparametric models are discussed that imply an IIO. A method is proposed that can be used to investigate whether empirical data imply an IIO. Furthermore, only twoparametric polytomous IRT models are found to imply an IIO. These are the rating scale model (Andrich, 1978) and a restricted rating scale version of the graded response model (Muraki, 1990). Well-known models, such as the partial credit model (Masters, 1982) and the graded response model (Samejima, 1969), do no imply an IIO.     

Minimax d-optimal designs for item response theory models

Various different item response theory (IRT) models can be used in educational and psychological measurement to analyze test data. One of the major drawbacks of these models is that efficient parameter estimation can only be achieved with very large data sets. Therefore, it is often worthwhile to search for designs of the test data that in some way will optimize the parameter estimates. The results from the statistical theory on optimal design can be applied for efficient estimation of the parameters.A major problem in finding an optimal design for IRT models is that the designs are only optimal for a given set of parameters, that is, they are locally optimal. Locally optimal designs can be constructed with a sequential design procedure. In this paper minimax designs are proposed for IRT models to overcome the problem of local optimality. Minimax designs are compared to sequentially constructed designs for the two parameter logistic model and the results show that minimax design can be nearly as efficient as sequentially constructed designs.     

Polytomous IRT models and monotone likelihood ratio of the total score

In a broad class of item response theory (IRT) models for dichotomous items the unweighted total score has monotone likelihood ratio (MLR) in the latent trait. In this study, it is shown that for polytomous items MLR holds for the partial credit model and a trivial generalization of this model. MLR does not necessarily hold if the slopes of the item step response functions vary over items, item steps, or both. MLR holds neither for Samejima's graded response model, nor for nonparametric versions of these three polytomous models. These results are surprising in the context of Grayson's and Huynh's results on MLR for nonparametric dichotomous IRT models, and suggest that establishing stochastic ordering properties for nonparametric polytomous IRT models will be much harder.Hemker's research was supported by the Netherlands Research Council, Grant 575-67-034. Junker's research was supported in part by the National Institutes of Health, Grant CA54852, and by the National Science Foundation, Grant DMS-94.04438.     

Robustness of Parameter Estimation to Assumptions of Normality in the Multidimensional Graded Response Model

A central assumption that is implicit in estimating item parameters in item response theory (IRT) models is the normality of the latent trait distribution, whereas a similar assumption made in categorical confirmatory factor analysis (CCFA) models is the multivariate normality of the latent response variables. Violation of the normality assumption can lead to biased parameter estimates. Although previous studies have focused primarily on unidimensional IRT models, this study extended the literature by considering a multidimensional IRT model for polytomous responses, namely the multidimensional graded response model. Moreover, this study is one of few studies that specifically compared the performance of full-information maximum likelihood (FIML) estimation versus robust weighted least squares (WLS) estimation when the normality assumption is violated. The research also manipulated the number of nonnormal latent trait dimensions. Results showed that FIML consistently outperformed WLS when there were one or multiple skewed latent trait distributions. More interestingly, the bias of the discrimination parameters was non-ignorable only when the corresponding factor was skewed. Having other skewed factors did not further exacerbate the bias, whereas biases of boundary parameters increased as more nonnormal factors were added. The item parameter standard errors recovered well with both estimation algorithms regardless of the number of nonnormal dimensions.     

题组反应理论及其在中学英语考试中的应用研究

题组作为众多测验中的一种常见题型,由于项目间存在一定程度的依赖性而违背了局部独立性假设,若用项目反应模型进行参数估计将会出现较大的偏差.题组反应理论将被试与题组的交互作用纳入到模型中,解决了项目间相依性的问题.笔者对题组反应理论的发展、基本原理及其相关研究进行了综述,并将其应用在中学英语考试中.与项目反应理论相对比,结果发现：（1）题组反应模型与项目反应模型在各参数估计值的相关系数较强,尤其是能力参数和难度参数;（2）在置信区间宽度的比较上,题组反应模型在各个参数上均窄于项目反应模型,即题组反应模型的估计精度优于项目反应模型.     

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 psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences

This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual differences in the response data. Following this line of reasoning, we discuss the appropriateness of IRT for measuring abilities and bipolar traits, such as pro versus contra attitudes. Surprisingly, if a diffusion model underlies the response processes, IRT models are appropriate for bipolar traits but not for ability tests. A reconsideration of the concept of ability that is appropriate for such situations leads to a new item response model for accuracy and speed based on the idea that ability has a natural zero point. The model implies fundamentally new ways to think about guessing, response speed, and person fit in IRT. We discuss the relation between this model and existing models as well as implications for psychology and psychometrics.