从多元概化理论看高考综合能力测试的改进

通过多元概化理论的研究发现,高考综合能力测试（2001,广东）的总体信度达到了可以接受的水平（0.784）。但测验中各部分对总方差的贡献程度与预定的赋分比例有较大差距。其中,地理和政治的贡献度偏低,化学和历史的贡献度偏高。这表明有（历史和化学）偏科特点的考生得到了较高的综合分数。又经决策(D)研究发现,增加地理部分的题量会反常地降低测验的总体信度,这说明有不少高分考生答错或主动放弃了地理科题目。因此,如何有效控制各部分的实际贡献程度、避免负面导向是当前高考综合能力测试亟待解决的问题。     

失独父母创伤后应激障碍症状的潜在类别及其相关因素

摘 要 研究旨在探讨失独父母创伤后应激障碍（PTSD）症状的潜在类别及相关因素,分析不同类别失独父母心理韧性的差异。采用创伤后应激障碍量表（PCL-5）、心理韧性量表对385名失独父母进行测查。结果发现：（1）失独父母PTSD症状存在三种潜在类别：PTSD高症状组（30%）、PTSD中度症状组（38%）、PTSD低症状组（32%）。（2）相比PTSD低症状组而言,PTSD高或中度症状组有显著的年龄和失独年限效应,PTSD高症状组中年龄≤60岁、失独年限<10年的人群所占比例较高;PTSD中度症状组的年龄≤60岁的人群所占比例更高。（3）PTSD高症状组心理韧性得分显著低于其他两组。应及时关注和干预小于60岁、失独年限<10年和低心理韧性的失独群体。     

失独父母创伤后应激障碍症状的潜在类别及其相关因素

摘 要 研究旨在探讨失独父母创伤后应激障碍（PTSD）症状的潜在类别及相关因素,分析不同类别失独父母心理韧性的差异。采用创伤后应激障碍量表（PCL-5）、心理韧性量表对385名失独父母进行测查。结果发现：（1）失独父母PTSD症状存在三种潜在类别：PTSD高症状组（30%）、PTSD中度症状组（38%）、PTSD低症状组（32%）。（2）相比PTSD低症状组而言,PTSD高或中度症状组有显著的年龄和失独年限效应,PTSD高症状组中年龄≤60岁、失独年限<10年的人群所占比例较高;PTSD中度症状组的年龄≤60岁的人群所占比例更高。（3）PTSD高症状组心理韧性得分显著低于其他两组。应及时关注和干预小于60岁、失独年限<10年和低心理韧性的失独群体。     

持续正压通气治疗睡眠呼吸暂停低通气综合征的接受意向调查

探讨影响持续气道内正压通气(CPAP)治疗阻塞性睡眠呼吸暂停低通气综合征(OSAHS)的接受性的非技术因素.设计OSAHS认知度和CPAP意向调查表、信赖他人量表,60例OSAHS患者经健康教育后,100例健康志愿者随机填表,结果做统计分析.两组中43.3%和5%基本了解OSAHS(P＜0.01);10%和5%表示接受CPAP(P＞0.05);85%和90%认为CPAP机价格高(P＞0.05).量表得分:两组得分分布近似,高分比例大,添加题90%和80%选择负性答案.目前公众对OSAHS认知差,对医生的信任度低,而CPAP机价格高,患者对CPAP接受性差.     

持续正压通气治疗睡眠呼吸暂停低通气综合征的接受意向调查

探讨影响持续气道内正压通气(CPAP)治疗阻塞性睡眠呼吸暂停低通气综合征(OSAHS)的接受性的非技术因素。设计OSAHS认知度和CPAP意向调查表、信赖他人量表,60例OSAHS患者经健康教育后,100例健康志愿者随机填表,结果做统计分析。两组中43.3%和5%基本了解OSAHS(P<0.01);10%和5%表示接受CPAP(P>0.05);85%和90%认为CPAP机价格高(P>0.05)。量表得分:两组得分分布近似,高分比例大,添加题90%和80%选择负性答案。目前公众对OSAHS认知差,对医生的信任度低,而CPAP机价格高,患者对CPAP接受性差。     

考试抄袭识别的心理测量学研究回顾

考试抄袭是最难识别的作弊方式。抄袭统计量（ACS）和人员拟合统计量（PFS）是识别抄袭的两类主要统计方法。ACS是根据被怀疑抄袭者与被抄袭者实际得分模式相似的概率来识别抄袭者。PFS 则把一个观察的项目得分模式与一定的测量模型相对比,来检验被试得分模式是否与测量模型预测的模式相吻合。其中,PFS由于在识别异常得分模式时存在一些干扰因素,所以对结果的解释存在多样性,应用较少。ACS是专门用于识别抄袭的统计方法,研究表明其识别率更高。目前ACS指标在美国的SAT和一些资格认证考试中已经得到广泛应用     

IRT_Δb法和修正LR法对矩阵取样DIF检验的有效性

矩阵取样测验包含多个题册,单个题册的总分不能直接作为匹配变量用于 DIF 检测。本研究首先基于模拟数据,同时采用 I RT_Δb法,以及用 I RT模型估计的考生能力作为匹配变量修订后的 L R法对矩阵取样测验进行DIF检测,分析二者进行DIF检测的有效性及其相关影响因素;并根据已有的LR法DIF判断标准划定出I RT_Δb法分类标准;最后使用实证数据加以验证。结果显示：矩阵取样测验中, I RT_Δb法和修正LR法均能较好地区分DIF量不同的题目;样本量、题册中DIF题目的比例和考生群体间真实能力的差异对两种方法的检验力、犯I类错误的概率和分类结果都有较大影响。     

LP方法及其与三种常用DIF检测方法的比较

本研究基于项目反应理论,提出了一种检验力高且犯Ⅰ类错误率小的检测DIF的新方法：LP法（Likelihood Procedure）,且以2PLM下对题目进行DIF检验为例介绍此法。本文通过与MH方法、Lord卡方检验法和Raju面积测量法三种常用的检验DIF的方法比较研究LP法的有效性,同时探讨样本容量、测验长度、目标组和参照组能力分布的差异、DIF值大小等相关因素对LP法有效性可能产生的影响。通过模拟研究,得到以下结论：(1)LP法比MH法及Lord卡方法更灵敏且更稳健;(2) LP法比Raju面积测量法更合理;(3)LP法的检验力随着被试样本容量或DIF值的增大而增大;（4）当参照组与目标组的能力无差异时,LP法在各种条件下的检验力比参照组与目标组的能力有差异时的检验力高;（5）LP法对一致性DIF和非一致性DIF都有良好的检验力,且LP法对一致性DIF的检验力比对非一致性DIF的检验力高。LP法可以简便的扩展并运用到多维度、多级评分项目上。     

基于不对称区间估计的有调节的中介模型检验

有调节的中介模型是中介过程受到调节变量影响的模型。评介了基于Bootstrap不对称置信区间和贝叶斯不对称可靠区间进行有调节的中介模型检验的3种方法, 包括亚组分析法、差异分析法和系数乘积法。模拟研究发现, 偏差校正的百分位Bootstrap置信区间和无先验信息的贝叶斯可靠区间在有调节的中介模型检验中表现相当, 都优于百分位Bootstrap置信区间的表现。建议使用系数乘积法进行第一阶段或第二阶段被调节的中介模型检验, 使用差异分析法进行两阶段被调节的中介模型检验, 并用一个实际例子演示如何用不对称区间估计检验有调节的中介模型。随后评述了3种有调节的中介模型检验方法在国内心理学的应用现状, 并展望了检验的拓展方向。     

基于项目作答反应时间的作弊甄别研究进展

随着基于计算机的测试逐渐普及,搜集、记录、分析考生的项目作答反应时间数据成为可能,越来越多的研究者开始基于这一数据开展考试的作弊甄别研究。然而,此类研究国外较多,国内则非常之少。提出“两种三类”的作弊行为分类标准,从参数建模法、非参数建模法两个维度,对基于项目作答反应时间的作弊研究进行梳理,评述其在甄别各类作弊行为中的应用实践和甄别效果,并对未来的研究方向做出展望。     

Using Deterministic, Gated Item Response Theory Model to Detect Test Cheating due to Item Compromise

The Deterministic, Gated Item Response Theory Model (DGM, Shu, Unpublished Dissertation. The University of North Carolina at Greensboro, 2010) is proposed to identify cheaters who obtain significant score gain on tests due to item exposure/compromise by conditioning on the item status (exposed or unexposed items). A “gated” function is introduced to decompose the observed examinees’ performance into two distributions (the true ability distribution determined by examinees’ true ability and the cheating distribution determined by examinees’ cheating ability). Test cheaters who have score gain due to item exposure are identified through the comparison of the two distributions. Hierarchical Markov Chain Monte Carlo is used as the model’s estimation framework. Finally, the model is applied in a real data set to illustrate how the model can be used to identify examinees having pre-knowledge on the exposed items.     

多级计分测验中基于残差统计量的被试拟合研究

本文提出一种多级计分项目下的个人拟合统计量R, 考察它在检测6种常见的异常作答模式(作弊、猜测、随机、粗心、创新作答、混合异常)下的表现, 并与标准化对数似然统计量l_zp进行比较。结果表明：(1) 在异常作答覆盖率较低并且异常作答类型为作弊和猜测时, R的检测率显著高于l_zp; (2) 随着测验长度和被试异常程度的增加, 两种统计量的检测率都会上升; (3) 在一些条件下, R与l_zp检测效果接近。实证数据分析进一步展示了R统计量的使用方法和过程, 结果也表明R统计量具有较好的应用前景。     

Predicting Academic Dishonesty on National Examinations: The Roles of Gender,Previous Performance,Examination Center Change,City Change,and Region Change

The purpose of the present studies was to evaluate and predict academic cheating with regard to a national examination in a Middle East country. In Study 1, 4,024 students took part and potential cheaters were classified as those having discrepant scores in multiple administrations that exceeded 1 SD in absolute terms. A latent class mixture analysis suggested two pathways for potential cheating: (a) The first path involved students—most male—who changed city or region of examination during test taking, and (b) the second path described students—most male—who did not change city, region, or center of administration. Study 2 profiled cheaters using a sample of examinees who were actually caught cheating. Participants were 545 students, 253 of whom were caught cheating between 2002 and 2012. Both samples were selected from a pool of 319,219 testees using random sampling procedures. Results indicated that a 4-class solution best fitted the data as in Study 1. Furthermore, a predictive model was tested with an independent cross-validation sample of 112 examinees (56 cheaters, 56 noncheaters). Results indicated that the model classified correctly 78.57 of the new cheating cases (sensitivity) and 94.64% of noncheaters (specificity).     

Response Time Based Nonparametric Kullback-Leibler Divergence Measure for Detecting Aberrant Test-Taking Behavior

Many important high-stakes decisions—college admission, academic performance evaluation, and even job promotion—depend on accurate and reliable scores from valid large-scale assessments. However, examinees sometimes cheat by copying answers from other test-takers or practicing with test items ahead of time, which can undermine the effectiveness of such assessments in yielding accurate, precise information of examinees' performances. This study focuses on the utility of a new nonparametric person-fit index using examinees' response times to detect two types of cheating behaviors. The feasibility of this method was investigated vis-à-vis a Monte Carlo simulation as well as through analyzing data from a large-scale assessment. Findings indicate that the proposed index was quite successful in detecting pre-knowledge cheating and extreme one-item cheating.     

兼顾两种匹配的考试抄袭统计量研究评述

考试抄袭统计量是专门用于识别抄袭的统计指标。它们是以被怀疑抄袭者和抄袭来源匹配反应的数目为基础建立的,按其考虑的匹配反应信息可分为两类:一类只考虑被怀疑抄袭者和抄袭来源间的错误答案匹配;另一类同时考虑两者错误答案匹配和正确答案匹配。由于第一类抄袭统计量对那些只从抄袭来源处抄袭正确答案的抄袭者检测不敏感,所以存在缺陷;第二类抄袭统计量兼顾两种匹配,考虑的信息更全面。s2、g2、ω是具有代表性的第二类抄袭统计量,它们在测量理论基础、被怀疑抄袭者和抄袭来源的异常相似反应模式确定方法及考试抄袭检测效能等方面存在差异,在抄袭识别应用中应合理选择。     

Action and Ethics Education

This paper provides a model for testing the relation between a particular action (cheating) and ethics education. The test is for a difference in the incidence of cheating (answer copying) between two groups: students who have and students who have not taken a course in ethics. The model facilitates testing by obtaining a relation between the unobservable variable (cheating) and an observable variable (a wrong answer on the target question which is the same as the answer of a nearby student). The required sample size is large but roughly comparable to the size that has been used with an answer copying index. Unlike an answer copying index, the model does not rely on extensive copying by individual students. The model is best suited for faculty who use in-class, multiple-choice exams.     

A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing

Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are (1) modeling aberrant behavior via mixture modeling methods, and (2) flagging aberrant behavior via residual based outlier detection methods. In this paper, we propose a two-stage method that is conceived of as a combination of both approaches. In the first stage, a mixture hierarchical model is fitted to the response and response time data to distinguish normal and aberrant behaviors using Markov chain Monte Carlo (MCMC) algorithm. In the second stage, a further distinction between rapid guessing and cheating behavior is made at a person level using a Bayesian residual index. Simulation results show that the two-stage method yields accurate item and person parameter estimates, as well as high true detection rate and low false detection rate, under different manipulated conditions mimicking NAEP parameters. A real data example is given in the end to illustrate the potential application of the proposed method.     

College Student Cheating: The Role of Motivation,Perceived Norms,Attitudes, and Knowledge of Institutional Policy

This study provides a comparative analysis of students' self-reported beliefs and behaviors related to six analogous pairs of conventional and digital forms of academic cheating. Results from an online survey of undergraduates at two universities (N = 1,305) suggest that students use conventional means more often than digital means to copy homework, collaborate when it is not permitted, and copy from others during an exam. However, engagement in digital plagiarism (cutting and pasting from the Internet) has surpassed conventional plagiarism. Students also reported using digital “cheat sheets” (i.e., notes stored in a digital device) to cheat on tests more often than conventional “cheat sheets.” Overall, 32% of students reported no cheating of any kind, 18.2% reported using only conventional methods, 4.2% reported using only digital methods, and 45.6% reported using both conventional and digital methods to cheat. “Digital only” cheaters were less likely than “conventional only” cheaters to report assignment cheating, but the former was more likely than the latter to report engagement in plagiarism. Students who cheated both conventionally and digitally were significantly different from the other three groups in terms of their self-reported engagement in all three types of cheating behavior. Students in this “both” group also had the lowest sense of moral responsibility to refrain from cheating and the greatest tendency to neutralize that responsibility. The scientific and educational implications of these findings are discussed in this study.     

A theory of cheating

The theory of motivated cheating postulates that test takers may cheat when they do not know an answer. With probabilityk, an “observer” is unsure of an answer and will copy from a nearby “target” with probabilityc. The corresponding parameters for the target may be entirely unrelated to those of the observer. Thus, the undesirable feature of bidirectionality of parameters found in correlational techniques is not an inherent feature of this theory of cheating. Predictions are derived, and estimates ofk andc are proposed. Statistically large values of c suggest that an observer was copying from a target. High values ofc for both the observer and the target suggest collusion. The theory is applied to a 40-item five-choice test taken by students in an introductory psychology section. From the full paired comparison matrix of target × observer parameter estimates, the method identifies 2 students who were probably in collusion.