计算机化多维测验中作答时间和作答精度数据的联合分析

随着心理与教育测量研究的发展和科技的进步,计算机化(大规模)测验逐渐受到人们的关注。为探究在计算机化多维测验中如何利用作答时间数据来辅助评估多维潜在能力,以及为我国义务教育阶段教育质量监测提供数据分析方法上的理论支持。本研究以2012年和2015年国际学生能力评估(PISA)计算机化数学测验数据为例,提出了一种可同时利用作答时间和作答精度数据的联合作答与时间的多维Rasch模型。根据新模型对PISA数据的分析结果,表明引入作答时间数据,不仅有助于提高模型参数的估计精度,还有助于数据分析者利用被试的作答时间信息来做进一步的决策和干预(e.g., 对异常作答行为或预备知识的诊断)。     

协单参数单维度Rasch模型的优势与意义

相比多参数多维度IRT模型通过增加参数的方式来提升模型拟合度和解释度，Rasch模型流派强调“理论驱动研究”和“数据符合模型”，推崇单参数单维度的测量模型能最大限度地减少额外因素对真实测量目的的影响和干扰，从而保证测量的客观性和准确性。Rasch模型关注测量目标与测量工具的对应关系，它的“简单”特性有助于研究者更准确地评估和解释被测目标与测量工具间的适配性，且在将非线性数据转化为等距数据时具有天然的优势。     

心理科学领域内的客观测量——Rasch模型之特点及发展趋势

Rasch模型是在国外学术界受到广泛关注和深入研究的一个潜在特质模型。该模型为解决心理科学领域内测量的客观性问题提供了一个可行性很高的解决方案。而国内关于Rasch模型的理论探讨和应用研究却并不多见。不同于一般项目反应理论, Rasch模型要求所收集的数据必须符合模型的先验要求, 而不是使用不同的参数去适应数据的特点。Rasch模型的主要特点(包括个体与题目共用标尺、线性数据、参数分离)确保了客观测量的实现。未来关于Rasch模型的研究方向包括多维度Rasch模型、测验的等值与链接、计算机自适应性考试, 大型应用测量系统(比如Lexile系统)等等。     

文字与数字量尺的差异及心理机制:兼论量尺制作的方法学问题

量表采用何种评分标尺可能影响心理测量的结果。已有研究发现,对于同一测量对象,采用文字量尺和数字量尺所产生的评定结果有显著差异,并提出了标准改变效应、认知策略等可能的机制。然而,目前这两种量尺的差异未得到足够的重视,呈现出研究不充足、心理机制不明确等特点,未来研究可以文字量尺和数字量尺间的差异为焦点,从心理机制、文化差异、实际应用等方面展开深入探讨。基于量尺的不同特征,文章最后探讨了量尺制作的方法学问题。     

应用Rasch模型测试和分析儿童入学准备状态

本研究的目的在于应用Rasch模型编制和分析数学入学准备测验,从而分析Rasch模型的有效性和优势。自编数学入学准备测试,对150名平均年龄为6.6岁的儿童进行测查,应用Rasch模型对题目和评分等级做出修正并分析结果。结果表明修正后的测试具有较好的信效度,较好地拟合了Rasch模型,评分等级设置合理,测试的整体难度相对较低。儿童的Rasch分数和性别无关,但受到年龄、家庭社会经济地位的影响。相对于经典测量理论而言,应用Rasch模型进行入学准备测试的编制和分析具有优势。     

托幼机构教育质量评价中评委偏差的多侧面Rasch分析

采用多侧面Rasch模型对28位评委在托幼机构教育质量评价中的评委偏差进行了分析。分析结果显示：28名评委评分宽严度差异显著;3名评委内部一致性较差,其余25名评委内部一致性较稳定;评委与评价班级的交互作用不显著,与评价项目的交互作用显著。研究结果表明MFRM可以对托幼机构教育质量评价的评委偏差进行个体层面的具体分析,从项目反应理论的视角为托幼机构教育质量评价的评委针对性培训、评估评委的合格性从而建立合格评委库等提供现代教育、心理测量学依据。     

网络成瘾的潜在结构:连续的还是分类的?

变量的潜在结构是连续的还是分类的不应被随意指定,错误的设定可能导致不正确的结论。本研究的目的在于从实证的角度探索网络成瘾的潜在结构。研究采用了来自中国杭州的2511名初中生对Young网络成瘾量表的有效作答数据,比较了Rasch模型、潜在类别模型和混合Rasch模型与数据的拟合情况。结果表明:2个潜在类别的混合Rasch模型可以最好地反映网络成瘾的潜在结构,说明网络成瘾包含两个存在着质的差异的群体,并且每个群体内的个体间存在量的差异。研究进一步比较了基于混合Rasch模型的分类与传统划界分数分类的区别,结果表明:Young的网络成瘾标准可能具有很小的误判率和较高的漏判率;基于Young网络成瘾测验的修订需要考虑有针对性地增加部分题目。     

Rasch的测验理论

当Rasch(1960)提出他的测验理论的时候,有许多种心理测量学方法可以供人们选择,并且正在被人们应用。所以,若要在理论与实践上作出进一步拓展,就得说明这种拓展的必要性。Rasch关于提出一种新理论的主要理由是:传统方法导出的结果只具有     

手机依赖的计算机化自适应测量及其效果评估

本文深入探讨了手机依赖的新的测量方式即计算机化自适应测量,并同时与原手机依赖的纸笔测验量表进行了比较,以探讨在相同测量长度下,新测量方式能在多大程度上提升对手机依赖的测量精度及测量信度。为此本文共进行了两项研究:研究1开发构建了手机依赖的计算机化自适应测量(CAT-MPD),并探讨了其测量的特征与性能。研究2通过对真实数据的模拟CAT测试,以CAT-MPD来源的纸笔测验量表为比较对象,考察CAT-MPD对原纸笔量表测量精度及测量信度的提升幅度。研究结果表明:CAT-MPD具有较理想的测量精度和信度,而且能有效减少被试所测项目量;同时,在同等条件下,CAT-MPD不论是在测量的精度还是测量的信度,均大幅优于手机依赖的纸笔测验量表。总之,本文为手机依赖的实际测量提供了一种新的技术支持。     

基于神经生理学的疼痛测量

疼痛是一种受多重因素影响的复杂主观感受。临床上,疼痛测量主要依赖于患者的主观评价。然而,这种传统的疼痛测量方法具有多方面的局限。近年来,研究者借助生理记录、脑电和功能磁共振等技术,揭示疼痛的神经生理、心理机制,挖掘与疼痛相关的神经生理指标,进而构建有效、客观和精确的疼痛评价体系。在基础研究和临床实践中,这些技术有望弥补传统疼痛测量方法的不足,从而极大推动疼痛测量及其治疗等相关领域研究的发展。     

多维Rasch模型在维度分数报告中的应用—对带宽-保真度困境的解决

分别采用四维度和十五维度Rasch模型分析包含项目内多维度结构的科学测验数据,估计两种维度结构下维度分数的信度.结果表明,对比相应的单维模型而言,四维度与十五维度Rasch模型均能够极大提高各内容维度上分数估计的信度.四维度与十五维度Rasch模型拟合结果的比较表明,对于总长度固定的测验,维度数目的增加能够补偿子维度长度减少引起的信度损失.但是这一作用必须以维度间较高的相关性为前提.     

Reducing Measurement Error in Student Achievement Estimation

The achievement level is a variable measured with error, that can be estimated by means of the Rasch model. Teacher grades also measure the achievement level but they are expressed on a different scale. This paper proposes a method for combining these two scores to obtain a synthetic measure of the achievement level based on the theory developed for regression with covariate measurement error. In particular, the focus is on ordinal scaled grades, using the SIMEX method for measurement error correction. The result is a measure comparable across subjects with smaller measurement error variance. An empirical application illustrates the method.     

基于GT和多面Rasch模型的结构化面试分析

该研究应用GT和多面Rasch模型对结构化面试数据进行分析,并提出一些建议针对某辅导员招聘面试数据,运用GT从宏观上分析应聘者、考官和项目所带来的总体误差大小,在此基础上,运用多面Rasch模型从微观上进一步探查考官严厉度、应聘者能力差异、项目难易度及侧面偏差．结果表明：1）GT分析表明应聘者产生的变异较大（90．65％）,说明面试可靠性较高,且当考官数为2时可靠性已较好.2）多面Rasch模型分析出了各侧面效应中的非拟合因素及交互效应中的偏差因素,表明面试误差主要来自考官间严厉度的差异及其自身一致性的不稳定。将GT与多面Rasch模型相结合分析面试数据不仅能测查出评价过程各方面的问题因素,并能更好地作整体把握。     

Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates

This paper proposes a structural analysis for generalized linear models when some explanatory variables are measured with error and the measurement error variance is a function of the true variables. The focus is on latent variables investigated on the basis of questionnaires and estimated using item response theory models. Latent variable estimates are then treated as observed measures of the true variables. This leads to a two-stage estimation procedure which constitutes an alternative to a joint model for the outcome variable and the responses given to the questionnaire. Simulation studies explore the effect of ignoring the true error structure and the performance of the proposed method. Two illustrative examples concern achievement data of university students. Particular attention is given to the Rasch model.     

Testing the assumptions and interpreting the results of the Rasch model using log-linear procedures in SPSS

This paper shows how to use the log-linear subroutine of SPSS to fit the Rasch model. It also shows how to fit less restrictive models obtained by relaxing specific assumptions of the Rasch model. Conditional maximum likelihood estimation was achieved by including dummy variables for the total scores as covariates in the models. This approach greatly simplifies the specification of the Rasch models. We illustrate these procedures in an analysis of four items selected from the Reiss Premarital Sexual Permissiveness Scale. We found that a modified version of the Rasch model with item dependencies fits the data significantly better than the simple Rasch model. We also found that the item difficulties are the same for men and women, but that the item dependencies are significantly greater for men. Apart from any substantive issues these results raise, the value of this exercise lies in its demonstration of how researchers can use the procedures of popular, accessible software packages to study an increasingly important set of measurement models.     

The rasch model,the law of comparative judgment and additive conjoint measurement

Relationships between the Rasch model and both the law of comparative judgment and additive conjoint measurement are discussed. The distance between the ability of Persona and the difficult of Itemi is, in the Rasch model, the baseline value corresponding to the probability thata will respond correctly toi, where this probability is interpreted as the area under a logistic curve (which is substantially equivalent to the normal curve) and is thus an application of the law of comparative judgment. Under certain assumptions, the Rasch model is also a special case of additive conjoint measurement and, properly reinterpreted, may be usefully applied in contexts other than individual differences.     

Models for measurement,precision, and the nondichotomization of graded responses

It is common in educational, psychological, and social measurement in general, to collect data in the form of graded responses and then to combine adjacent categories. It has been argued that because the division of the continuum into categories is arbitrary, any model used for analyzing graded responses should accommodate such action. Specifically, Jansen and Roskam (1986) enunciate ajoining assumption which specifies that if two categoriesj andk are combined to form categoryh, then the probability of a response inh should equal the sum of the probabilities of responses inj andk. As a result, they question the use of the Rasch model for graded responses which explicitly prohibits the combining of categories after the data are collected except in more or less degenerate cases. However, the Rasch model is derived from requirements of invariance of comparisons of entities with respect to different instruments, which might include different partitions of the continuum, and is consistent with fundamental measurement. Therefore, there is a strong case that the mathematical implication of the Rasch model should be studied further in order to understand how and why it conflicts with the joining assumption. This paper pursues the mathematics of the Rasch model and establishes, through a special case when the sizes of the categories are equal and when the model is expressed in the multiplicative metric, that its probability distribution reflects the precision with which the data are collected, and that if a pair of categories is collapsed after the data are collected, it no longer reflects the original precision. As a consequence, and not because of a qualitative change in the variable, the joining assumption is destroyed when categories are combined. Implications of the choice between a model which satisfies the joining assumption or one which reflects on the precision of the data collection considered are discussed.