基于项目区分度的双目标CD-CAT选题策略

针对双目标CD-CAT,将六种项目区分度（鉴别力D、一般区分度GDI、优势比OR、2PL的区分度a、属性区分度ADI、认知诊断区分度CDI）分别与IPA方法结合,得到新的选题策略。模拟研究比较了它们的表现,还考察了区分度分层在控制项目曝光的表现。结果发现：新方法都能明显提高知识状态的判准率和能力估计精度;分层选题均能很好地提高题库利用率。总体上,OR加权能显著提高测量精度;OR分层选题在保证测量精度条件下显著提高项目曝光均匀性。     

项目竞争强度对网络成瘾大学生提取诱发遗忘的影响

执行功能缺陷是网络成瘾发生、维持和复发的原因之一,以往研究侧重于探讨网络成瘾者注意控制、反应抑制等执行功能成分的损伤状况,而其记忆抑制状况尚不清楚。本研究通过考察网络成瘾大学生的提取诱发遗忘（RIF）来探讨其记忆抑制的特点及RIF的产生机制。结果发现：低网络成瘾个体在高项目竞争强度下表现出显著的RIF,在低项目竞争强度下未产生;高网络成瘾个体在低项目竞争强度下表现出显著的RIF,在高项目竞争强度下未产生。结果表明网络成瘾大学生的记忆抑制能力受损严重,其在低项目竞争强度下易受相关代价与效益问题（CCB）影响而产生干扰性RIF。     

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.     

Exploring Differential Subgroup Functioning on SAT Writing Items: What Happens When English Is Not a Test Taker's Best Language?

The purpose of this study is to explore patterns in model-data fit related to subgroups of test takers from a large-scale writing assessment. Using data from the SAT, a calibration group was randomly selected to represent test takers who reported that English was their best language from the total population of test takers (N = 322,011). A reference scale for the items was constructed based on EBL responses. Response behaviors of test takers who reported that English was not their best language (ENBL) were examined in relationship to this reference scale. This study illustrates the use of differential subgroup analyses to identify patterns related to person misfit within subgroups, as well as subsets of items, that may affect the validity of writing scores for ENBL test takers. The methodology described here offers an approach that can be used to explore, understand, and improve the validity of scores obtained from ENBL test takers in large-scale writing assessments.     

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。     

Measuring work styles: Towards an understanding of the dynamic components of the theory of work adjustment

Work styles are an important yet largely unexplored component of the theory of work adjustment (TWA), describing a dynamic component of how individuals maintain and adjust fit with their work environment. The active work style (AWS) scale is the first attempt to develop a specific self-report measure of work styles suitable for longitudinal research. Results from three studies support Dawis and Lofquist's (1984) proposed four factor structure, but these factors are related through a second-order factor describing a person's generalised level of work activity and effort across time. The AWS scale demonstrated good evidence for reliability and validity, and strong measurement invariance across time signifying its suitability for longitudinal research. In line with expectations, overall work style was positively related to conscientiousness and work engagement yet unrelated to stress. When controlling for these variables, AWS was positively related to demands–abilities fit, but not needs–supplies fit. Limitations and possibilities for future research are also discussed.     

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.     

My way: How strategic preferences vary by negotiator role and regulatory focus

Negotiators may use vigilant, loss-minimizing strategies or eager, gain-maximizing strategies. The present study provides evidence that preferences for these different strategies depend on negotiator role and personal orientation. In a price negotiation, buyers and prevention-focused individuals prefer vigilant strategies, whereas sellers and promotion-focused individuals prefer eager strategies. When there is a match between the strategy and the role (role–strategy fit) or between the strategy and the individual's regulatory focus orientation (focus–strategy fit), the negotiator experiences more fit and plans to be more demanding in the negotiation. By manipulating strategy in a real, binding negotiation, we reveal its importance in determining negotiators' subjective experiences and planned demand. Our results show that shared strategic preferences between different motivational orientations—negotiator role and personal regulatory focus—can create self-regulatory compatibility.     

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.