二参数逻辑斯蒂模型项目参数的估计精度

项目参数的估计精度对于测验的编制尤其是题库的建立十分重要。目前,国内外对项目参数估计精度的研究,大部分是基于在已知项目参数真值的情况下,运用各种参数估计方法产生新的估计值,再和真值进行偏度(BIAS)和均方根差(RMSE)的比较,从而说明该种估计方法的有效性。但是这种方法不能提供不同的参数真值之间的估计误差的变化规律。为了弥补这一缺陷,本文尝试从项目参数估计信息函数的角度出发研究项目参数的估计精度问题。本研究以二参数Logistic模型作为研究对象,首先定义了项目参数的估计信息函数,然后基于完全随机实验设计,通过模拟研究的方法探索影响项目参数的估计精度的因素,实验共设计了(2×3×2)种情形。研究结果表明：(1)项目参数(a,b)的估计精度均随着被试样本量的增大而提高;(2)被试的能力分布对难度参数的估计精度影响较大,对区分度参数的估计精度影响相对较小;(3)难度参数和区分度参数的估计精度都分别受到参数a和参数b的共同作用。     

Performance and Learning of Generalized Motor Programs: Relative (GMP) and Absolute (Parameter) Errors

The effects of practice (Experiment 1) and parameter variability (Experiment 2) on the learning of generalized motor programs (GMPs) and movement parameterization were investigated In each experiment, 2 tasks with different relative force-time structures were tested. Participants (N = 32, Experiment (N = 40, Experiment 2) attempted to exert a pattern of force that resembled in force and time a waveform that was displayed on a computer monitor. In both experiments, the analysis suggested that the GMP, although refined over practice, was relatively stable (i.e., resistant to decay and interference), even early in practice (after 20 trials). In addition, the results indicated that constant and variable parameter practice did not differentially affect GMP learning but did degrade the learning of the parameter that was not varied. The data provided additional evidence for the dissociation of the GMP and the parameterization processes proposed in GMP theory. Contrary to schema theory, the present data suggest an interdependence between the force and the time parameters: The manipulation of 1 of the parameters has a negative effect on the learning of the other parameter.     

Effect of Practice on Effector Independence

The authors' primary purpose in the present experiment was to determine if practice changes the extent to which simple motor sequences are effector independent. Contralateral and ipsilateral effector transfers were assessed in 24 participants after 1 (200 trials) and 4 (800 trials) days of practice. The response sequence became increasingly effector dependent; the response structure and the scaling of force on the effector transfer tests were no better after 4 days of practice than after only 1 day, even though retention performance improved substantially. Those results are consistent with the notion that participants refine their movements over extended practice by exploiting the unique characteristics of the effectors. The additional practice results in a more effective movement when the same effectors are used but is of little value when different effectors are required.     

基于GRM模型的BP神经网络参数估计

目前参数估计多采用统计方法,存在耗时长、要求被试样本容量大和项目数多等缺点。本文将BP神经网络和降维法相结合,对GRM的项目参数和考生能力参数进行估计。蒙特卡洛模拟结果显示：（1）不管是人多题少还是题多人少,该网络设计下的参数估计精度都较高;（2）可以应用到多个不同等级评分的参数估计中,甚至是超过15个等级的项目参数,估计精度也较高,这是其他参数估计方法所不可比拟的;（3）运行的时长和统计估计方法相比大大缩减。     

Investigating Separate and Concurrent Approaches for Item Parameter Drift in 3PL Item Response Theory Equating

This study examines separate and concurrent approaches to combine the detection of item parameter drift (IPD) and the estimation of scale transformation coefficients in the context of the common item nonequivalent groups design with the three-parameter item response theory equating. The study uses real and synthetic data sets to compare the two approaches based on IPD flagging rates, type I error and power rates, and recovery of scale transformation coefficients. Results indicate that the two approaches render similar outcomes with stable anchor sets. However, they can produce dissimilar results with unstable anchor sets because of differences in the performance of their IPD components. Further, the findings of this study caution about working backward from equated cut scores to motivate the selection of an anchor set.     

改进的MCMC算法及其在估计IRT模型参数中的应用

本文首先简要的阐述了MCMC算法的思想及在IRT参数估计中的操作过程;其次,针对该算法存在的一些问题,提出相应的改进建议;然后,分别运用传统的和改进型的MCMC算法进行模拟数据分析和比较,结果显示新的方法表现更好;最后总结新方法的优点所在,并指出下一步的研究方向。     

Modeling storage and retrieval processes with clinical populations with applications examining alcohol-induced amnesia and Korsakoff amnesia

Multinomial processing tree models can provide for measures of underlying cognitive processes. In this paper, the Chechile [Chechile, R. A. (2004). New multinomial models for the Chechile-Meyer task. Journal of Mathematical Psychology, 48, 364-384] 6P model is described and applied to several applications involving clinical populations. The model provides for separate measures of storage and retrieval. Monte Carlo studies were conducted to examine the relative accuracy of two methods for obtaining an overall condition estimate for the 6P model, i.e., averaging estimates found for individuals versus pooling the multinomial frequency data before estimating the model parameters. The sampling studies showed that the pooling of frequencies resulted in more accurate parameter estimates. However, psychological assessment in clinical psychology requires precise measurement on an individual basis. In order to recover information about individuals from pooled frequency information, a modified jackknife method was advanced. The jackknife method is based on a contrast between the overall pooled frequency information and the pool frequency without the observations from a single individual. Another series of Monte Carlo simulations demonstrate that the new jackknife method resulted in better recovery of the correct individual parameter values relative to estimates based on only the data from the individual. Finally, the 6P model was used to examine the data from two previously reported studies with clinical populations. One application addressed the effect of alcohol-induced amnesia, and the other application dealt with Korsakoff amnesia. In both cases the pattern of storage and retrieval measurements resulted in a clarification of the underlying storage and retrieval differences between the clinical group and the control group.     

人体实验失范原因分析及对策

人体实验是一种特殊的交易活动.信息不完备与不对称、交易制度监督漏洞都使卫生研究机构在交易博弈中处于优势地位.集体机会主义行为是导致违规人体实验的重要因素.对违规人体实验的宽容在一定程度上怂恿了这种行为.从健全制度、完善法治、加强教育等方面提出了对策.     

Goodness-of-fit and confidence intervals of approximate models

If the model for the data are strictly speaking incorrect, then how can one test whether the model fits? Standard goodness-of-fit (GOF) tests rely on strictly correct or incorrect models. But in practice the correct model is not assumed to be available. It would still be of interest to determine how good or how bad the approximation is. But how can this be achieved? If it is determined that a model is a good approximation and hence a good explanation of the data, how can reliable confidence intervals be constructed? In this paper, an attempt is made to answer the above questions. Several GOF tests and methods of constructing confidence intervals are evaluated both in a simulation and with real data from the internet-based daily news memory test.     

Evaluating Factorial Invariance: An Interval Estimation Approach Using Bayesian Structural Equation Modeling

In this study, we introduce an interval estimation approach based on Bayesian structural equation modeling to evaluate factorial invariance. For each tested parameter, the size of noninvariance with an uncertainty interval (i.e. highest density interval [HDI]) is assessed via Bayesian parameter estimation. By comparing the most credible values (i.e. 95% HDI) with a region of practical equivalence (ROPE), the Bayesian approach allows researchers to (1) support the null hypothesis of practical invariance, and (2) examine the practical importance of the noninvariant parameter. Compared to the traditional likelihood ratio test, simulation results suggested that the proposed Bayesian approach could offer additional insight into evaluating factorial invariance, thus, leading to more informative conclusions. We provide an empirical example to demonstrate the procedures necessary to implement the proposed method in applied research. The importance of and influences on the choice of an appropriate ROPE are discussed.