Multilevel Reliability Measures of Latent Scores Within an Item Response Theory Framework

Abstract

This paper evaluated multilevel reliability measures in two-level nested designs (e.g., students nested within teachers) within an item response theory framework. A simulation study was implemented to investigate the behavior of the multilevel reliability measures and the uncertainty associated with the measures in various multilevel designs regarding the number of clusters, cluster sizes, and intraclass correlations (ICCs), and in different test lengths, for two parameterizations of multilevel item response models with separate item discriminations or the same item discrimination over levels. Marginal maximum likelihood estimation (MMLE)-multiple imputation and Bayesian analysis were employed to evaluate the accuracy of the multilevel reliability measures and the empirical coverage rates of Monte Carlo (MC) confidence or credible intervals. Considering the accuracy of the multilevel reliability measures and the empirical coverage rate of the intervals, the results lead us to generally recommend MMLE-multiple imputation. In the model with separate item discriminations over levels, marginally acceptable accuracy of the multilevel reliability measures and empirical coverage rate of the MC confidence intervals were found in a limited condition, 200 clusters, 30 cluster size, .2 ICC, and 40 items, in MMLE-multiple imputation. In the model with the same item discrimination over levels, the accuracy of the multilevel reliability measures and the empirical coverage rate of the MC confidence intervals were acceptable in all multilevel designs we considered with 40 items under MMLE-multiple imputation. We discuss these findings and provide guidelines for reporting multilevel reliability measures.     

Asymptotic Standard Errors of Generalized Partial Credit Model True Score Equating Using Characteristic Curve Methods

In this study, the delta method was applied to estimate the standard errors of the true score equating when using the characteristic curve methods with the generalized partial credit model in test equating under the context of the common-item nonequivalent groups equating design. Simulation studies were further conducted to compare the performance of the delta method with that of the bootstrap method and the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion empirical standard errors as well as those yielded by the bootstrap method and the multiple imputation method under all the manipulated conditions.     

A Fuzzy Goal Programming Formulation with Multiple Target Levels

The essential activity of a manager is decision making, which is becoming more and more complex, mainly in the multi‐criteria problems. Multi‐choice goal programming (MCGP) is considered as a robust tool in operational research to solve this type of problem. However, in real world problems, determining precise targets for the goals is a difficult task. To deal with such situation, Tabrizi introduced and used in 2012 the concept of membership functions in the MCGP model in order to model the targets fuzziness of each goal. In their model, they considered just only one type of functions (triangular form), which does not reflect adequately the decision maker's preferences that are considered as an essential element for modelling the goal's fuzziness. Their model is called Fuzzy MCGP. In this paper, new ideas are presented to reformulate MCGP model to tackle all types of functions by introducing the (decision maker's) preferences. The concept of indifference thresholds is used in the new formulation for characterizing the imprecision and the preferences associated with all types of the goals. The proposed formulation provides useful insight about the solution of a new class of problems. A numerical example is given to demonstrate the validity and strength of the new formulation. Copyright © 2013 John Wiley & Sons, Ltd.     

认知诊断缺失数据处理方法的比较：零替换、多重插补与极大似然估计法

数据缺失在测验中经常发生, 认知诊断评估也不例外, 数据缺失会导致诊断结果的偏差。首先, 通过模拟研究在多种实验条件下比较了常用的缺失数据处理方法。结果表明：(1)缺失数据导致估计精确性下降, 随着人数与题目数量减少、缺失率增大、题目质量降低, 所有方法的PCCR均下降, Bias绝对值和RMSE均上升。(2)估计题目参数时, EM法表现最好, 其次是MI, FIML和ZR法表现不稳定。(3)估计被试知识状态时, EM和FIML表现最好, MI和ZR表现不稳定。其次, 在PISA2015实证数据中进一步探索了不同方法的表现。综合模拟和实证研究结果, 推荐选用EM或FIML法进行缺失数据处理。     

组织型插图对不同认知方式个体说明性文本阅读的影响

采用图文相继呈现、文本逐句阅读的范式,通过反应时技术与出声思维的方法考察了组织型插图对不同认知方式个体说明性文本阅读过程与阅读结果的影响,结果发现,与单一文本相比,场独立个体在有插图条件下的阅读成绩显著提高,而阅读时间没有增加,并产生了更多的推理、细化和提问策略;而场依存个体在有插图条件下的阅读成绩显著提高,阅读时间也显著增多,而理解策略没有变化。这说明组织型插图对两种认知方式个体说明性文本的阅读均起到促进作用,但场独立个体没有因为增加组织型插图而付出更多的加工努力,而是通过调整理解策略的方式来提高阅读效果;而场依存个体在阅读过程中没有根据组织型插图调整理解策略,阅读效果的提高是以付出更多的加工努力为代价的。     

广西肝恶性肿瘤患者住院费用的研究

为探讨肝恶性肿瘤患者住院总费用情况,对广西2010年～2012年间肝恶性肿瘤患者的住院费用资料进行统计学分析,比较肝恶性肿瘤患者3年住院总费用的变化情况,并采用多重线性回归分析方法,分析肝恶性肿瘤患者住院总费用的影响因素.结果显示广西肝恶性肿瘤住院患者逐年增多,住院费用逐年增高;住院天数、有无手术、有无合并症、有无院内感染、性别、年龄6个因素是住院总费用的影响因素.我们应加大预防控制肝恶性肿瘤的力度,建立单病种诊疗规范和费用控制标准,推进临床路径管理,以减轻患者的经济负担.     

迎接心理学发展的第三波

心理学自冯特始便存在两种类型,自然科学的心理学和人文的心理学.本土心理学依据“一种心智,多种心态”的原则,建构了儒家关系主义理论和自我曼陀罗模型,这种含摄文化的理论解决了冯特以科学方法研究文化的难题.批判实在论和多重哲学典范的结合,奠定了华人本土心理学科学哲学基础,在此基础上,华人本土心理学将迎来心理学发展的第三波.     

Improvement of Thematic Classification in Offender Profiling: Classifying Serbian Homicides Using Multiple Correspondence,Cluster, and Discriminant Function Analyses

This paper investigates thematic classification of homicides for the purpose of behavioural investigative analysis (e.g. offender profiling). Previous research has predominantly used smallest space analysis (SSA) to conceptualise and classify offences into thematic groups based on crime scene behaviour data. This paper introduces a combined approach utilising multiple correspondence analysis (MCA), cluster analysis (CA), and discriminant function analysis (DFA) to define and differentiate crime scenes into expressive or instrumental and impersonal or personal crimes. MCA is used to derive the latent structural dimensions in the crime data and produce quantitative scores for each offence along these dimensions. Two‐step CA was then utilised to classify offences. Offence dimensional scores were then used to predict cluster membership under DFA, producing cluster centroids corresponding to MCA dimensions. Centroids were plotted on the MCA correspondence map to simultaneously conceptualise crime classification and the latent structure of the Serbian crime data. Classification of offences based on MCA dimensional scores were 91.5% accurate. This MCA–CA–DFA approach may reduce some of the more subjective aspects of SSA methodology used in classification, whilst producing a product more amenable to objective and cumulative review. Implications for offender profiling research utilising SSA and this approach are discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

基于结构方程模型的多重中介效应分析

多重中介模型是指存在多个中介变量的模型。多重中介模型可以分析特定中介效应、总的中介效应和对比中介效应。指出了目前多重中介模型分析普遍存在的问题,包括分析不完整、使用Sobel检验带来的局限。建议通过增加辅助变量的方法进行完整的多重中介效应分析,使用偏差校正的Bootstrap方法进行中介检验。总结出一个多重中介SEM分析流程,并有示例和相应的MPLUS程序。随后展望了辅助变量和中介效应检验方法的发展方向。