CAT选题策略分类概述

选题是计算机化自适应测验（CAT）测试过程的关键环节,选题策略的目标是要达到较高的测量精度,同时也实现试题曝光率控制及其他测验目标的实现.本文根据选题策略的基本原理和衍生发展,将众多CAT选题策略分为五大选题策略系列：Fisher函数系列、K-LI函数系列、α分层系列、贝叶斯系列、b匹配系列;并根据测验目标（测验精度、试题曝光率控制、内容平衡、多条件约束）对这些选题策略进行了细分,并对CAT选题策略的选择思路进行归纳.     

Prevalence and correlates of metabolic syndrome in Hong Kong Chinese adults—a random community sample study

The study investigates the prevalence and correlates of metabolic syndrome (MS) among Hong Kong Chinese adults. Random cluster sampling design and International Diabetes Federation (IDF) MS criteria were adopted. Totally 737 adults participated in questionnaire survey and received waist circumference (WC) measurement. Among them 335 showed central obesity (WC > 80 and 85 cm for women and men respectively). Subsequently 229 accepted blood test. Totally 71 participants met MS criteria, suggesting an overall prevalence of 14.1%. Both central obesity and MS increase sharply with age. Chi-square/ANOVA analyses revealed significant positive associations between central obesity with: being male, marital status being married/cohabit, lower education level, occupation as managers or housewives, and alcohol consumption. For MS, the significant positive correlates were: lower education level, occupation as service workers or housewives, lower income level, and alcohol consumption. After controlling for age, binary logistic regression analyses suggested the significant risk factors for central obesity were being male (OR = 1.4), married/cohabit (OR = 1.8), longer working hours (OR = 1.5), eating less vegetables (OR = 1.5), and alcohol consumption (OR = 1.8). For MS, only alcohol consumption appeared to be a significant risk factor (OR = 2.3). Multivariate binary logistic regressions also supported that age group and alcohol consumption were significant predictors of MS. To conclude, adopting randomized cluster sampling and IDF criteria, the study revealed a prevalence rate of MS at 14.1%. Alcohol consumption appears to be the strongest risk factor of MS, which however needs further investigation.     

多级评分项目的多维CAT选题策略开发

本研究开发了两种新的适用于多级评分项目的多维计算机化自适应测验（PMCAT）的选题策略——修正的连续熵（RCEM）和修正的后验期望KL信息（MKB）方法,并与以往PMCAT的选题策略进行了对比研究。Monte Carlo实验结果表明：两种新开发的选题策略比原方法估计精度更高,并且RCEM方法在所有选题策略中曝光率最低。新开发的选题策略具有较理想的估计精度和曝光控制效果,为PMCAT在实践中的应用提供了新的方法支持。     

What kind of kind is intelligence?

The model of human intelligence that is most widely adopted derives from psychometrics and behavioral genetics. This standard approach conceives intelligence as a general cognitive ability that is genetically highly heritable and describable using quantitative traits analysis. The paper analyzes intelligence within the debate on natural kinds and contends that the general intelligence conceptualization does not carve psychological nature at its joints. Moreover, I argue that this model assumes an essentialist perspective. As an alternative, I consider an HPC theory of intelligence and evaluate how it deals with essentialism and with intuitions coming from cognitive science. Finally, I highlight some concerns about the HPC model as well, and conclude by suggesting that it is unnecessary to treat intelligence as a kind in any sense.     

Detecting Clusters/Communities in Social Networks

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.     

Polytomous multilevel testlet models for testlet‐based assessments with complex sampling designs

A pplications of standard item response theory models assume local independence of items and persons. This paper presents polytomous multilevel testlet models for dual dependence due to item and person clustering in testlet‐based assessments with clustered samples. Simulation and survey data were analysed with a multilevel partial credit testlet model. This model was compared with three alternative models – a testlet partial credit model (PCM), multilevel PCM, and PCM – in terms of model parameter estimation. The results indicated that the deviance information criterion was the fit index that always correctly identified the true multilevel testlet model based on the quantified evidence in model selection, while the Akaike and Bayesian information criteria could not identify the true model. In general, the estimation model and the magnitude of item and person clustering impacted the estimation accuracy of ability parameters, while only the estimation model and the magnitude of item clustering affected the item parameter estimation accuracy. Furthermore, ignoring item clustering effects produced higher total errors in item parameter estimates but did not have much impact on the accuracy of ability parameter estimates, while ignoring person clustering effects yielded higher total errors in ability parameter estimates but did not have much effect on the accuracy of item parameter estimates. When both clustering effects were ignored in the PCM, item and ability parameter estimation accuracy was reduced.     

A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.     

On the Speed Sensitivity Parameter in the Lognormal Model for Response Times and Implications for High-Stakes Measurement Practice

In high-stakes testing, often multiple test forms are used and a common time limit is enforced. Test fairness requires that ability estimates must not depend on the administration of a specific test form. Such a requirement may be violated if speededness differs between test forms. The impact of not taking speed sensitivity into account on the comparability of test forms regarding speededness and ability estimation was investigated. The lognormal measurement model for response times by van der Linden was compared with its extension by Klein Entink, van der Linden, and Fox, which includes a speed sensitivity parameter. An empirical data example was used to show that the extended model can fit the data better than the model without speed sensitivity parameters. A simulation was conducted, which showed that test forms with different average speed sensitivity yielded substantial different ability estimates for slow test takers, especially for test takers with high ability. Therefore, the use of the extended lognormal model for response times is recommended for the calibration of item pools in high-stakes testing situations. Limitations to the proposed approach and further research questions are discussed.     

A Signal Detection Model for Multiple-Choice Exams

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.     

Does survival context enhance memory for source? A within-subjects comparison

ABSTRACT

Nairne, Thompson, and Pandeirada [2007. Adaptive memory: Survival processing enhances retention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 263–273] demonstrated that processing words according to their relevance to a survival scenario enhanced their subsequent retrieval in recall and recognition tasks compared to a variety of control scenarios. From an adaptive perspective, it is maintained that processing words in a survival context should also enhance memory for source; however, evidence in the literature is rather mixed regarding a survival context advantage for source memory. In the current study, we conducted four experiments to systematically investigate the survival advantage in source memory, when the context itself is the source, with both recall (Experiments 1A and 1B) and recognition tests (Experiments 2A and 2B). Results showed a survival advantage for item memory over the control contexts in all experiments. The survival context advantage was not extended to source memory performance in Experiment 1A. Results from all other experiments, however, indicated a survival context advantage for both item and source memory. Findings are discussed in relation to possible proximate mechanisms underlying the survival processing effect.