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.     

Using Penalized EM Algorithm to Infer Learning Trajectories in Latent Transition CDM

Psychometrika - Cognitive diagnostic models (CDMs) have arisen as advanced psychometric models in the past few decades for assessments that intend to measure students’ mastery of a set of...     

On Interim Cognitive Diagnostic Computerized Adaptive Testing in Learning Context

Interim assessment occurs throughout instruction to provide feedback about what students know and have achieved. Different from the current available cognitive diagnostic computerized adaptive testing (CD-CAT) design that focuses on assessment at a single time point, the authors discuss several designs of interim CD-CAT that are suitable in the learning context. The interim CD-CAT differs from the current available CD-CAT designs primarily because students’ mastery profile (i.e., skills mastery) changes due to learning, and new attributes are added periodically. Moreover, hierarchies exist among attributes taught sequentially and such information could be used during item selection. Two specific designs are considered: The first one is when new attributes are taught in Stage II, but the student mastery status of the previously taught attributes stays the same. The second design is when both new attributes are taught, and previously taught attributes can be further learned or forgotten in Stage II. For both designs, the authors propose an individual prior, which considers a person’s learning history and population learning model, to start an interim CD-CAT. Simulation results show that the Stage II CD-CAT using individual prior outperforms the methods using population priors. The GDINA (generalized deterministic inputs, noisy, “and” gate) diagnostic index (GDI) is extended to accommodate item hierarchies, and analytic results are provided to further illustrate the types of items that are most popular during item selection. As the first study that focuses on the application of CD-CAT in a learning context, the methods and results present herein showed the great promise of using CD-CAT to monitor learning.     

More Eagerness,More Suffering from Search Bias: Accuracy Incentives and Need for Cognition Exacerbate the Detrimental Effects of Excessive Searching in Finding Romantic Partners Online

The systematic use of Web search tools to browse and evaluate recommendations is widespread. Such tools may be crucial for users to truly benefit from the enormous selection of options available online. However, a recent study demonstrated that a greater number of search options triggered excessive searching and led to inferior decisions (i.e., the more‐means‐worse effect; Wu & Chiou, 2009 ). Given that individuals who are motivated to make accurate choices (the accuracy incentive) tend to engage in broader and more exhaustive searches, Experiment 1 examined how the accuracy incentive influenced the search process. The findings indicated that the accuracy incentive led to excessive searching and poorer choices. Moreover, the search ratio, an indicator of excessive searching, mediated the negative effect of the accuracy incentive on choice quality. Experiment 2 investigated the role of the need for cognition (NFC) in the more‐means‐worse effect. High‐NFC participants demonstrated an increased tendency toward excessive searching and exhibited poorer selectivity than did low‐NFC participants. The current research indicated that the accuracy incentive of online searchers may serve as a determinant of excessive searching and that online searchers characterized by high NFC may be more vulnerable to the negative effects of excessive searching. Copyright © 2015 John Wiley & Sons, Ltd.     

Contribution of Leisure-Time Physical Activity on Psychological Benefits Among Elderly Immigrants

Research provides evidence that leisure-time physical activity engagement promotes health and well-being among older adults. In this study, using data released from the Health and Retirement Study (HRS) in 2008, we focused on the exploration of the relationship between leisure-time physical activity and psychological benefits such as positive affect, optimism, psychological well-being, and life satisfaction among elderly immigrants. The results of this study demonstrated that leisure-time physical activities play an important role in gaining psychological benefits. This finding provided further evidence that leisure-time physical activities provides rich opportunities for elderly immigrants to experience psychological benefits, which may contribute to quality of life and successful aging.     

着装行为的动机目标研究

本研究采用主因素分析法探讨动机系统中着装目标的结构 ,同时提出了测量着装动机目标的初步量表———着装目标重要性量表。研究采用动机诱导、服装作品分析与文献分析等方法收集、确认着装动机目标共 90项 ,并在此基础上进行主因素分析。结果表明着装行为的动机目标由 1 2个主要因素构成 :①寻求表现能力地位的外观 ;②寻求表达礼仪的外观 ;③追求表现美的外观 ;④寻求身心舒适的外观 ;⑤追求理想的自我形象 ;⑥寻求新奇气派的外观 ;⑦寻求团体认同的外观 ;⑧寻求便利的外观 ;⑨寻求年轻的外观 ;　l媢斞扒笳胧视玫耐夤?;　l媣曆扒蠓舷八椎耐夤?;　l媤栄扒笳娼巧耐夤邸     

面部表情加工的ERP成分

面部表情加工的ERP成分主要包括P1 (80~120 ms)、N170 (120~200 ms), 早期后部负电位(Early Posterior Negativity, EPN, 200~300 ms)和晚期正成分(Late Positive Potential, LPP, 300 ms以后)。这些成分代表了表情加工的不同阶段, 具有不同的心理含义。P1成分只对威胁类表情(恐惧, 厌恶和愤怒)敏感, 反映了对威胁面孔的快速探测, 具有自动加工的性质。N170成分与表情结构信息的编码有关, 同样具有自动加工的性质。EPN反映了对情绪信息的选择性注意, 具有情绪普遍性, 情绪场景图片和表情刺激都会对它产生影响, 在特定的条件下具有自动加工的性质。LPP则反映了对情绪信息的高级认知加工, 较易受注意控制的影响。在对上述成分的特性了解的基础上, 将来的研究应该探索以下问题：(1) P1是否受表情威胁程度的影响？(2) N170受到哪些自上而下因素的影响？(3)那些不能影响N170成分, 却能影响EPN成分的表情刺激是否被当成了普通的情绪刺激来看待？(4)表情加工引发的LPP是否能具有自动加工的性质？(5)不同的基本表情类型(如恐惧和厌恶)是否具有特异性的ERP成分？