Multilevel multidimensional item response model with a multilevel latent covariate

In a pre‐test–post‐test cluster randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre‐test scores and other related covariates while estimating an intervention effect at post‐test. In many applications in education, the total post‐test and pre‐test scores, ignoring measurement error, are used as response variable and covariate, respectively, to estimate the intervention effect. However, these test scores are frequently subject to measurement error, and statistical inferences based on the model ignoring measurement error can yield a biased estimate of the intervention effect. When multiple domains exist in test data, it is sometimes more informative to detect the intervention effect for each domain than for the entire test. This paper presents applications of the multilevel multidimensional item response model with measurement error adjustments in a response variable and a covariate to estimate the intervention effect for each domain.     

Multidimensional adaptive testing

Maximum likelihood and Bayesian procedures for item selection and scoring of multidimensional adaptive tests are presented. A demonstration using simulated response data illustrates that multidimensional adaptive testing (MAT) can provide equal or higher reliabilities with about one-third fewer items than are required by one-dimensional adaptive testing (OAT). Furthermore, holding test-length constant across the MAT and OAT approaches, substantial improvements in reliability can be obtained from multidimensional assessment. A number of issues relating to the operational use of multidimensional adaptive testing are discussed.     

A Multidimensional Scaling Analysis of Perceptions of Academic Dishonesty Among Fifth-Grade Students

A study was conducted to investigate the perceptions of academic dishonesty in fifth-grade students. Two methods were used to gather data: a sorting task, which was used to indirectly assess the students' perceptions, and a rating scale task, which was used to externally validate the results of the sorting task. Results of the multidimensional scaling analysis yielded two dimensions, the first being tests/homework and papers, and the second, more ambiguous appearing to differentiate based on seriousness.     

完美主义结构研究述评

早期研究者如Adler、Homey和Ellis认为完美主义是一种单维的人格结构。后来的研究者如Hewitt、Frost等多持多维观点,认为完美主义是由不同因素组成的复杂结构。目前越来越多学者认同完美主义的二维观,认为完美主义有积极和消极之分。今后研究应考虑完美主义的文化因素,同时应从心理咨询、心理治疗和心理健康教育效果的角度来佐证某种结构观的合理性。     

A marginal maximum likelihood method for the vector threshold model to analyze dichotomous choice data

Abstract: At least two types of models, the vector model and the unfolding model can be used for the analysis of dichotomous choice data taken from, for example, the pick any/ n method. The previous vector threshold models have a difficulty with estimation of the nuisance parameters such as the individual vectors and thresholds. This paper proposes a new probabilistic vector threshold model, where, unlike the former vector models, the angle that defines an individual vector is a random variable, and where the marginal maximum likelihood estimation method using the expectation-maximization algorithm is adopted to avoid incidental parameters. The paper also attempts to discuss which of the two models is more appropriate to account for dichotomous choice data. Two sets of dichotomous choice data are analyzed by the model.     

基于双因子模型的测验总分和维度分的合成方法

双因子模型可以同时包含一个全局因子和多个局部因子,在描述多维测验结构时有其独特优势,近些年应用越来越广泛。文章基于双因子模型,提出了4种合成总分和维度分的方法,分别是:原始分法,加和法,全局题目加权加和法和局部题目加权加和法,并采用模拟的方法,在样本量、测验长度、维度间相关变化的条件下考察了这些方法与传统多维IRT方法的表现。最后,通过实证研究对结果进行了验证。结果显示:(1)全局加权加和法和局部加权加和法,尤其是局部加权加和法合成的总分和维度分与真值最接近、信度最高。(2)在维度间相关较高,测验长度较长的条件下,局部加权加和法的结果较好,部分条件下甚至优于多维IRT法。(3)仅有局部加权加和法合成的维度分能够反应维度间真实的相关关系。     

多维测验分数的报告与解释:基于双因子模型的视角

在心理、行为、管理和教育等社科领域,经常使用多维测验。本文评介并比较了各种多维测验的测量模型;总结了基于双因子模型计算得到的统计指标;根据不同研究目的,提出了两个兼顾简洁性和精确性的多维测验分析流程。作为例子,在马基雅维利主义人格量表的研究中,通过双因子模型分析了如何报告、解释多维测验分数以及如何利用多维建模进行后续分析。     

冲击事件对员工主动离职的影响

冲击事件(shocks)指激发员工主动离职心理决策过程的具体事件。国外学者主要从冲击事件各维度间的关系和各维度与离职路径的关系两个方面研究冲击事件对主动离职的影响, 实证研究表明冲击事件对员工主动离职有显著影响。针对现有研究的不足, 未来研究可以从明晰冲击事件, 优化研究方法, 细分研究对象, 加强冲击事件本土化研究等方面进行探索。     

复杂决策规则下MIRT的分类准确性和分类一致性

介绍多维项目反应理论模型下分类准确性和分类一致性指标, 采用蒙特卡罗方法实现复杂决策规则下指标计算, 并从数学上证明分类准确性指标两类估计量在均匀先验和相同决策规则条件下依概率收敛于同一真值。研究结果表明：分类准确性指标可以比较准确地评价分类结果的准确性; 分类一致性指标可以较好地评价分类结果的重测一致性; 在一定条件下, 基于能力量尺的指标优于基于原始总分的指标; 纵使测验维度增加, 估计精度仍比较好; 随着测验长度和维度间相关增加, 分类准确性和分类一致性更高。指标可以用来评价标准参照测验或计算机分类测验的多种决策规则下分类信度和效度。