A local-influence-based diagnostic approach to a speeded item response theory model

Summary.  An item response theory model for dealing with omitted responses in a test is proposed. In this model formulation, non-response does not only depend on an examinee's ability and on item difficulty, but additionally also on 'test speededness'. Using a local-influence-based diagnostic approach, the sensitivity of the model regarding assumptions concerning the drop-out mechanism is explored. The methodology proposed is applied to the Chilean Sistema de Medición de la Calidad de la Educación mathematics test case-study.     

实施分段项目教学 强化专业核心技能

过硬的专业核心技能是使高职学生达到人才培养目标的关键。为确保学生的教育质量,以机电一体化专业为例,提出了以项目为中心的六个模块、三个教学阶段和三项技能证书要求的"六、三、三"教学模式,从而使学生在职业资格等级考核中顺利达标。     

生产系统基础数据集成研究

采用可配置技术,根据不同顾客需要自动生成个性化产品的生产明细表是企业未来努力方向。通过PDM和ERP计算机系统软件,分别对技术系统和生产系统中的信息流、工作流和相关业务领域的信息系统实行集成,满足技术系统和生产系统的产品的不同管理需要,在产品的形成过程和生命周期中,达到产品开发、设计与制造全过程的信息集成,实现设计明细表数据与生产明细表数据快速转化的应用前景。     

关于申请科研课题的几点思考

科研人员申请科研课题都希望获得成功 ,但高水平的课题并不一定能够获得支持。申请者必须敢于申请 ,直面竞争 ,找准申请课题的切入点 ,科学选题 ,提交一份高质量的开题报告 ,才有可能获得支持     

Four comments for the paper from Liu et al. (2019)

We give comments for the paper from Liu et al. (2019) about the Item Response Theory (IRT) model under consideration, the justification to compute Marginal likelihood, about what we learn with the data analysis performed and finally, about the computational issues in this paper.     

训诂术语“犹”的释义特点与字典辞书义项的设立

训诂术语＂犹＂的释义特点是＂义隔而通之＂,虽然有少数带＂犹＂的训诂是同义相训,但它本质上是一个为提示随文释义而设立的术语。而目前的一些字典辞书,却以这种训诂资料为主要的甚或是唯一的依据来确立义项,因而造成失误。文中举例分析了这类义项,指出,以带术语＂犹＂的训诂资料为主要依据来确立义项要慎重。     

Sensitivity analysis and choosing between alternative polytomous IRT models using Bayesian model comparison criteria

Polytomous Item Response Theory (IRT) models are used by specialists to score assessments and questionnaires that have items with multiple response categories. In this article, we study the performance of five model comparison criteria for comparing fit of the graded response and generalized partial credit models using the same dataset when the choice between the two is unclear. Simulation study is conducted to analyze the sensitivity of priors and compare the performance of the criteria using the No-U-Turn Sampler algorithm, under a Bayesian approach. The results were used to select a model for an application in mental health data.     

Discussion on “A comparison of Monte Carlo methods for computing marginal likelihoods of item response theory models”

In this discussion, the sensitivity of the result by the choice of parameters a and b in one of approaches reviewed by the authors to calculate the marginal likelihood for 2 parameter logistic item response theory model is investigated using a small simulation study.     

Missing data methods in official statistics in the United Kingdom: Some recent developments

In recent years an increase in nonresponse rates in major government and social surveys has been observed. It is thought that decreasing response rates and changes in nonresponse bias may affect, potentially severely, the quality of survey data. This paper discusses the problem of unit and item nonresponse in government surveys from an applied perspective and highlights some newer developments in this field with a focus on official statistics in the United Kingdom (UK). The main focus of the paper is on post-survey adjustment methods, in particular adjustment for item nonresponse. The use of various imputation and weighting methods is discussed in an example. The application also illustrates the close relationship between missing data and measurement error. JEL classification C42, C81     

Normalization rules for item response models: Simulation results

ABSTRACT

This research examines the statistical methodology that is used to estimate the parameters in item response models. An integral part of an item response model is the normalization rule that is used to identify the distributional parameters. The main result shown here is that only Verhelst–Glas normalizations that arbitrarily set one difficulty and one dispersion parameter to unity are consistent with the basic assumptions underlying the two-parameter logistic model. Failure to employ this type of normalization will lead to scores that depend on the item composition of the test and differential item difficulty (DIF) will compromise the validity of the estimated ability scores when different groups are being compared. It is also shown that some of the tests for DIF fail when the data are generated by an IRT model with a random effect. Most of the results are based on simulations of a four item model. Because the data generation mechanism is known, it is possible to determine the effect on ability scores and parameter estimates when different normalizations or different distribution parameter values are used.