Maximum likelihood additivity analysis

A maximum likelihood estimation procedure was developed to fit unweighted and weighted additive models to conjoint data obtained by the categorical rating, the pair comparison or the directional ranking method. The scoring algorithm used to fit the models was found to be both reliable and efficient, and the program MAXADD is capable of handling up to 300 parameters to be estimated. Practical uses of the procedure are reported to demonstrate various advantages of the procedure as a statistical method.The research reported here was supported by Grant A6394 to the author from the Natural Sciences and Engineering Research Council of Canada. Portions of this research were presented at the Psychometric Society meeting in Iowa City, Iowa, in May, 1980.Thanks are due to Jim Ramsay, Justine Sergent and anonymous reviewers for their helpful comments.Two MAXADD programs which perform the computations discussed in this paper may be obtained from the author.     

Testing Process Factor Analysis Models Using the Parametric Bootstrap

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.     

Response Selection Contributes to the Preparation Cost for Bimanual Asymmetric Movements

Movement preparation of bimanual asymmetric movements takes more time than bimanual symmetric movements in choice reaction-time conditions. This bimanual asymmetric cost may be caused by increased processing demands on any stage of movement preparation. The authors tested the contributions of each stage of movement preparation to the asymmetric cost by using the additive factors method. This involved altering the stimulus contrast, response compatibility, and response complexity. These manipulations changed the processing demands on stimulus identification, response selection, and response programming, respectively. Any manipulation with a larger reaction time cost than control suggests that stage contributes to the bimanual asymmetric cost. The bimanual asymmetric cost was larger for incompatible stimuli, which supports that response selection contributes to the bimanual asymmetric cost.     

自适应分组认知诊断测验设计及其选题策略

应用OMST在线装配模式,提出自适应分组认知诊断测验（CD-AMGT）。由于知识状态的先决关系是偏序关系,而且构成格（lattice),利用知识状态当前估计值在格中的上下确界对被试真实知识状态的可能范围进行界定,由此装配下一分组,分组中结合PWKL策略或SHE策略进行选题以兼顾诊断精度、效率和安全性。模拟实验表明,CD-AMGT与PWKL、SHE对比,当题目类型丰富时,以分类准确率略微降低为代价,其题库使用均匀性和计算用时均表现出较大优势。     

三类多层中介效应分析方法比较

比较了贝叶斯法、Monte Carlo法和参数Bootstrap法在2-1-1多层中介分析中的表现。结果发现：1)有先验信息的贝叶斯法的中介效应点估计和区间估计都最准确;2)无先验信息的贝叶斯法、Monte Carlo法、偏差校正和未校正的参数Bootstrap法的中介效应点估计和区间估计表现相当,但Monte Carlo法在第Ⅰ类错误率和区间宽度指标上表现略优于其他三种方法,偏差校正的Bootstrap法在统计检验力上表现略优于其他三种方法,但在第Ⅰ类错误率上表现最差;结果表明,当有先验信息时,推荐使用贝叶斯法;当先验信息不可得时,推荐使用Monte Carlo法。     

加强住院医师临床思维能力培养的探讨

良好的临床思维能力,是住院医师能否成为合格和高水平医生的关键。对住院医师临床思维能力的培养必须明确临床思维的哲学本质和组成的关键要素,并结合住院医师的特点去培养。要注重基本知识、基本理论和基本技能训练,把握正确的恩维程序,运用哲学思想,并以学科综合为中心。     

《周易》境遇学SWOT分析法论纲

"境遇学"是如何应对境遇的"学问",其着眼点在于提升个体生命的"境界".<周易>是集中体现中国古代"境遇学"的一部代表性著作.<周易·大象传>所预设的境遇是方方面面的,其应对之方也是随宜变化的,其中体现着非凡的道德智慧.但它属于"不占之易",可操作性不强.当代西方的SWOT分析法(又称"态势分析法")从优势、劣势、机会威胁四个方面作企业战略研究与个人生涯规划分析,与<周易>境遇学有很强的互补性.本文尝试将中国古老的"<周易>境遇学"与当代西方"SWOT分析法"相结合,提出<周易>境遇学SWOT分析法".     

基于认知设计系统的几何类比推理测验的编制及认知模型分析

认知诊断是一种新的测量范式。本研究首先根据Mulholland,Pellegrino＆Glaser和Whitely＆Schneider关于类比推理的研究结果,采用认知设计系统编制了几何类比推理测试题,并对所编制的测试题的质量进行验证,然后采用口语报告法分析解决几何类比推理所涉及的认知模型,结果表明：1）编制的几何类比推理测试题的质量比较好;2）学生解决类比推理所涉及的认知属性主要为置换、翻转、旋转、数量、大小、颜色与条纹和形状。     

拓广分部评分模型下计算机自适应测验变加权选题策略

摘要: 在计算机自适应测验中, 对0-1评分模型按a-分层选题是高效安全的策略,但多级评分模型的项目难度/步骤参数有多个而无法直接应用这种选题策略。信息函数能够很好地综合项目所有参数及能力参数,但最大信息量选题策略会影响考试安全。本文提出一种变加权选题策略,它通过调用一个与信息量相关联的函数,该函数与信息量成正比,与区分度的某个幂函数成反比,从而达到既能综合项目所有参数又按a分层的效果。在GPCM模型下用蒙特卡罗实验进行比较研究,结果显示新的选题策略总体效果比已有相关结果好。     

Accounting for fluctuations in body dissatisfaction

The present study evaluated whether the strength of relationship between contextual cues (presence of company and mood) and state body dissatisfaction varied as a function of individual differences in key trait measures (body shame, body surveillance tendencies, internalization of appearance standards, and trait affect) which have been linked to trait body dissatisfaction. Fifty-five undergraduate women completed a questionnaire containing the trait-based measures and then carried a Personal Digital Assistant (PDA) for a 7-day period. The PDA prompted participants six times daily to self-report their current mood and state body dissatisfaction. Multi-level modeling revealed that individual differences in body shame predicted inter-individual variability in the strength of the relationships between presence of company and state body dissatisfaction, and positive mood and state body dissatisfaction. Trait positive affect also explained variance in the positive mood state-body dissatisfaction relationship. The implications of the findings for prevention of body image disturbances are discussed.