Refining the two‐parameter testlet response model by introducing testlet discrimination parameters

For testlet response data, traditional item response theory (IRT) models are often not appropriate due to local dependence presented among items within a common testlet. Several testlet‐based IRT models have been developed to model examinees' responses. In this paper, a new two‐parameter normal ogive testlet response theory (2PNOTRT) model for dichotomous items is proposed by introducing testlet discrimination parameters. A Bayesian model parameter estimation approach via a data augmentation scheme is developed. Simulations are conducted to evaluate the performance of the proposed 2PNOTRT model. The results indicated that the estimation of item parameters is satisfactory overall from the viewpoint of convergence. Finally, the proposed 2PNOTRT model is applied to a set of real testlet data.     

A Note on Motor Learning Without Post-Response Knowledge of Results

The authors studied the adjustment of the 2 distinct known expressions of gait velocity, the velocity of the center of gravity (CG) and the velocity of the center of foot pressure (CP) at the end of the 1st step in 2 experimental situations: natural gait initiation (the control situation, CS) and heel-off gait initiation (the test situation, TS). Gait was initiated by 7 healthy participants, from an erect spontaneous posture in the CS and from a posture with heels raised in the TS, on a force platform at 3 self-selected speed conditions. Biomechanical data from the force platform were collected in both experimental situations, and the authors used a particular gait analysis based on the differential method of Y. Brenière (2003) in order to approach velocity modulation by means of step length and frequency. Results showed that CG and CP velocities were adjusted differently during heel-off gait initiation than during natural gait initiation. CP velocity, as compared with CG velocity, was overestimated in TS. Results also established the relevance of the expression of step velocity by means of step length and frequency: The central nervous system, taking into account the specific postural constraints of each experimental situation, uses a reference value and a regulating parameter to modulate step velocity. Moreover, the contributions of 1st step length and frequency to the expression of step velocity in TS and CS were different. Thus, a specific locomotor behavior corresponds to a given experimental situation that is characterized by its own initial biomechanical constraints.     

Facilitation of Psychomotor Learning by Classically Differentiated Supplementary Feedback Cues

2 experiments on the control of psychomotor performance by classically differentiated supplementary feedback cues are reported. The first, conducted with 30 Ss as a replication of an earlier study, demonstrated again that otherwise ineffective supplementary cues can be transformed into highly effective regulators of psychomotor performance by differential conditioning procedures. The second, conducted with 78 Ss, showed that the effects of classically differentiated supplementary cues on tracking skill increased as a negatively accelerated function of the number of differentiation trials. Results were interpreted as exemplifying the operation of secondary drive and reinforcement principles, although the nonspecific transfer of arousal and/or warm-up effects were acknowledged as worthy alternatives.     

Fungible Weights in Multiple Regression

Every set of alternate weights (i.e., nonleast squares weights) in a multiple regression analysis with three or more predictors is associated with an infinite class of weights. All members of a given class can be deemed fungiblebecause they yield identical SSE (sum of squared errors) and R ² values. Equations for generating fungible weights are reviewed and an example is given that illustrates how fungible weights can be profitably used to evaluate parameter sensitivity in multiple regression. The author wishes to thank Drs. Robyn Dawes, William Grove, Markus Keel, Leslie Yonce, Joe Rausch, the editor, and three anonymous reviewers for helpful comments on earlier versions of this article.     

儿童绘图作业内隐学习智力独立性研究

实验采用中性参数程序和绘图作业,让正常智力、轻度智力落后、中度智力落后3组8～12岁右利手儿童接受符合、不符合或与“起始-旋转法则”无关的绘图练习,考察练习对原初行为方式的影响。结果发现,违反“起始-旋转法则”的练习使智力落后组被试的绘图行为发生了明显改变,证明内隐学习具有智力独立性;“意识污染”对内隐学习结果的影响可能是复杂的而非单向的;中性参数实验程序能够更好地隐蔽规则学习的实验目的,但不能有效消除意识污染的影响。     

被试作答的偶然性对IRT能力估计的影响研究

被试能力参数估计是项目反应理论应用研究最重要的技术之一。本文在理想的测验情境下,研究被试作答的偶然性对被试能力值估计的影响。研究设计了被试作答的两种偶然性情况：一是偶然做对了一道项目难度高于其能力值的试题,二是偶然做错了一道或几道项目难度低于其能力值的试题．然后分别探讨了这两种情况下对被试的能力估计所带来的影响,并且就如何消除这些偶然性所带来的影响提出了相应的方法。     

Mean Comparison: Manifest Variable Versus Latent Variable

Mean comparisons are of great importance in the application of statistics. Procedures for mean comparison with manifest variables have been well studied. However, few rigorous studies have been conducted on mean comparisons with latent variables, although the methodology has been widely used and documented. This paper studies the commonly used statistics in latent variable mean modeling and compares them with parallel manifest variable statistics. Our results indicate that, under certain conditions, the likelihood ratio and Wald statistics used for latent mean comparisons do not always have greater power than the Hotelling T² statistics used for manifest mean comparisons. The noncentrality parameter corresponding to the T² statistic can be much greater than those corresponding to the likelihood ratio and Wald statistics, which we find to be different from those provided in the literature. Under a fixed alternative hypothesis, our results also indicate that the likelihood ratio statistic can be stochastically much greater than the corresponding Wald statistic. The robustness property of each statistic is also explored when the model is misspecified or when data are nonnormally distributed. Recommendations and advice are provided for the use of each statistic. The research was supported by NSF grant DMS-0437167 and Grant DA01070 from the National Institute on Drug Abuse. We would like to thank three referees for suggestions that helped in improving the paper.     

认知诊断Q矩阵估计(修正)方法

Q矩阵代表着项目考察的属性, 反映了项目的重要特征, 其正确性是影响认知诊断分类准确性的关键因素。研究Q矩阵估计(修正)方法具有重要价值。首先, 研究从是否采用认知诊断模型将Q矩阵估计(修正)分为基于认知诊断模型视角下的参数化方法和基于统计视角下的非参数方法。然后, 分别从最优项目质量、最优模型数据拟合和参数估计视角对它们进行分类介绍, 评析不同方法的特征和表现、区别与联系、优势与不足。最后, 提出几个未来研究问题：在复杂测验条件下系统比较各种方法; 校准知识状态和参数估计误差、结合多种思路和方法等多角度提出Q矩阵估计(修正)方法; 研究多级评分项目、混合测验模型、属性多级、属性个数未知甚至Q矩阵元素为连续变量等条件下的Q矩阵估计(修正)方法。     

认知元反应理论--IRT直接应用于多值记分题

0-1记分测验的项目反应理论已经得到广泛的研究和应用.但是,许多测验都含有多值记分题,所以需要将IRT推广到此类情况.从认知理论的观点看,每个0-1记分题(项目)和多值记分题的每个测试点都可同样地看成一个由若干知识点构成的集合,称之为认知元;根据认知元之间存在的关系可以确定各受测者对各试题作出特定答案的概率,从而不需要引用任何其它假设就可将IRT的方法直接应用于含多值记分题的测验.本文应用这一理论分析了某些测验样本,结果表明是可行的.