Marginal maximum likelihood estimation of item response theory (IRT) equating coefficients for the common-examinee design

A method of estimating item response theory (IRT) equating coefficients by the common-examinee design with the assumption of the two-parameter logistic model is provided. The method uses the marginal maximum likelihood estimation, in which individual ability parameters in a common-examinee group are numerically integrated out. The abilities of the common examinees are assumed to follow a normal distribution but with an unknown mean and standard deviation on one of the two tests to be equated. The distribution parameters are jointly estimated with the equating coefficients. Further, the asymptotic standard errors of the estimates of the equating coefficients and the parameters for the ability distribution are given. Numerical examples are provided to show the accuracy of the method.     

和而不同,相辅相成——制度与道德相关性的哲学辨析

在社会发展实践中,制度与道德之间有着相互交叉、相互促进、相互转化的错综复杂的关系。制度的生成、演化、运行背后有着强烈的道德意蕴,道德本身所具有的规范性也不可避免地体现出制度的色彩。制度的道德依托使得制度的价值指向更加积极和明确,道德的制度支持则会促进道德的有效实现和践履。但制度和道德毕竟是各自独立的范畴,有着各自内涵的确定性,不能不适当地淡化两者之间的差别,两者之间是互补而不是替代的关系。     

编制中国人个性测量表(CPAI)的意义与程序

从1990年开始中国科学院心理研究所与香港中文大学心理系合作,编制“中国人个性测量表”(CPAI)。编制方法严格按照个性测验编制程序进行。共分四个步骤,一、建立正常与病态个性量表的框架。二、构成量表的项目。三、测量表的最后确定。四、量表的标准化工作。编制出的测量表共包括22个正常个性量表,12个病态个性量表及2个效度量表,共510个项目。经实践检验与统计分析,证明该测量表具有一定的信度与效度。     

大学英语四、六级考试分数等值研究

对现有的大学英语四、六级考试分数等值模式中存在的若干问题进行了深入的分析,并提出了新的解决方案——一个基于铆题设计和两参数IRT模型的解决方案。主要包括：（1）用两参数逻辑斯蒂模型替代原来的Rasch模型,以改进题目模型的适合性;（2）用共同题目的等值设计取代原来的共同被试等值设计,解决共同被试等值设计中,等值考生的动机水平难以控制的难题;（3）建立专用的等值用题库,并且一次性完成其中铆题的预测和参数标定工作,以解决原来等值模式中存在的误差累积问题。同时,由于铆题的保密工作难度较小,因此,等值专用题库对保证等值结果的可靠性也具有重大意义;（4）本文还对新的分数等值方案进行了真实的考试数据等值计算实验,并得到了一个令人满意的分数等值结果。     

Causal graphical views of fixed effects and random effects models

Despite the long-standing discussion on fixed effects (FE) and random effects (RE) models, how and under what conditions both methods can eliminate unmeasured confounding bias has not yet been widely understood in practice. Using a simple pretest–posttest design in a linear setting, this paper translates the conventional algebraic formalization of FE and RE models into causal graphs and provides intuitively accessible graphical explanations about their data-generating and bias-removing processes. The proposed causal graphs highlight that FE and RE models consider different data-generating models. RE models presume a data-generating model that is identical to a randomized controlled trial, while FE models allow for unobserved time-invariant treatment–outcome confounding. Augmenting regular causal graphs that describe data-generating processes by adding the computational structures of FE and RE estimators, the paper visualizes how FE estimators (gain score and deviation score estimators) and RE estimators (quasi-deviation score estimators) offset unmeasured confounding bias. In contrast to standard regression or matching estimators that reduce confounding bias by blocking non-causal paths via conditioning, FE and RE estimators offset confounding bias by deliberately creating new non-causal paths and associations of opposite sign. Though FE and RE estimators are similar in their bias-offsetting mechanisms, the augmented graphs reveal their subtle differences that can result in different biases in observational studies.     

Psychological components of responses to interest inventory items

This study tests the relevance of 10 psychological traits that are assumed to exemplify responses to the items of INFORIZON, an occupational interest inventory. To accomplish this goal, the author analyzed the responses to the interest items based on a sample of 824 participants (17–25 years old), with an assessment of the items based on psychological traits using another French sample (147 participants) in the same age category. The results of multiple linear regression analyses supported most of the hypotheses presented in this study.     

Extending the Debate Between Spearman and Wilson 1929: When do Single Variables Optimally Reproduce the Common Part of the Observed Covariances?

The covariances of observed variables reproduced from conventional factor score predictors are generally not the same as the covariances reproduced from the common factors. We sought to find a factor score predictor that optimally reproduces the common part of the observed covariances. It was found algebraically that—under some conditions—the single observed variable with highest loading on a factor reproduces the non-diagonal elements of the observed covariance matrix more exactly than the conventional factor score predictors. This finding is linked to Spearman's and Wilson's 1929 debate on the use of single variables as factor score predictors. A population-based and a sample-based simulation study confirmed the algebraic result that taking a single variable can outperform conventional factor score predictors in reproducing the non-diagonal covariances when the nonzero loading size and the number of nonzero loadings per factor are small. The results indicated that a weighted aggregation of variables does not necessarily lead to an improvement of the score over the variable with the highest loading.     

On the Mathematical Relationship Between Latent Change Score and Autoregressive Cross-Lagged Factor Approaches: Cautions for Inferring Causal Relationship Between Variables

The present paper focuses on the relationship between latent change score (LCS) and autoregressive cross-lagged (ARCL) factor models in longitudinal designs. These models originated from different theoretical traditions for different analytic purposes, yet they share similar mathematical forms. In this paper, we elucidate the mathematical relationship between these models and show that the LCS model is reduced to the ARCL model when fixed effects are assumed in the slope factor scores. Additionally, we provide an applied example using height and weight data from a gerontological study. Throughout the example, we emphasize caution in choosing which model (ARCL or LCS) to apply due to the risk of obtaining misleading results concerning the presence and direction of causal precedence between two variables. We suggest approaching model specification not only by comparing estimates and fit indices between the LCS and ARCL models (as well as other models) but also by giving appropriate weight to substantive and theoretical considerations, such as assessing the justifiability of the assumption of random effects in the slope factor scores.     

地佐辛混合液对预防腭咽成形术苏醒期不良反应观察

比较应用芬太尼和地佐辛在预防腭咽成形术苏醒期,因疼痛以及口咽部呼吸道梗阻引起的患者躁动,恶心呕吐等不良反应情况,寻找最佳药物以及方法组合。200例美国麻醉师协会（ASA）分级I级～Ⅱ级患者,随机分为A、B、C、D四组,给药时间均为手术结束前15分钟,A组：单次静注芬太尼0．1μg·kg^-1;B组单次静注地佐辛0．1mg·kg^-1;C组单次静注地佐辛0．1mg·kg^-1＋氟哌利多2．5mg;D组单次静注地佐辛0．1mg·kg^-1＋氟哌利多2．5mg＋伤口涂抹复方利多卡因乳膏。记录患者苏醒时间,拔管后呼吸抑制,恶心呕吐等情况。结果D组出现恶心呕吐、头痛头晕例数明显少于A、B、C三组（P〈o．05）。手术结束前15分钟单次静注地佐辛O．1mg·kg^-1＋氟哌利多2．5mg＋伤口涂抹复方利多卡因乳膏,对于有效预防腭咽成形术苏醒期疼痛、恶心呕吐、头痛以及躁动是有效的。     

急性冠脉综合征患者血液Rho激酶活性分析

探讨 Rho 激酶（ROCK）活性与急性冠脉综合征（ACS）患者冠状动脉狭窄程度及预后的关系。选取2012年6月至2013年3月住院的129例患者,其中急性心肌梗死（AMI）组68例、不稳定型心绞痛（UA）组30例、对照（CON ）组31例。Western Blot 检测血液白细胞中 ROCK 、ROCK1、ROCK2活性,同时根据冠状动脉的狭窄程度及是否发生心血管意外分组再次比较。 AMI 组和 UA 组 ROCK 、ROCK1、ROCK2活性均增高;且 AMI 组高于 UA 组;三支、双支、单支病变组活性依次减低,Gensini 评分≥20分组及发生心血管意外组均高于 Gensini 评分＜20分组及未发生心血管意外组,差异均有统计学意义（P＜0．05）。 ROCK 活性可能对 ACS 患者预后观察、评估有重要意义。