Exploring the Impact of Rater Effects on Person Fit in Rater-Mediated Assessments

Researchers have documented the impact of rater effects, or raters’ tendencies to give different ratings than would be expected given examinee achievement levels, in performance assessments. However, the degree to which rater effects influence person fit, or the reasonableness of test-takers’ achievement estimates given their response patterns, has not been investigated. In rater-mediated assessments, person fit reflects the reasonableness of rater judgments of individual test-takers’ achievement over components of the assessment. This study illustrates an approach to visualizing and evaluating person fit in assessments that involve rater judgment using rater-mediated person response functions (rm-PRFs). The rm-PRF approach allows analysts to consider the impact of rater effects on person fit in order to identify individual test-takers for whom the assessment results may not have a straightforward interpretation. A simulation study is used to evaluate the impact of rater effects on person fit. Results indicate that rater effects can compromise the interpretation and use of performance assessment results for individual test-takers. Recommendations are presented that call researchers and practitioners to supplement routine psychometric analyses for performance assessments (e.g., rater reliability checks) with rm-PRFs to identify students whose ratings may have compromised interpretations as a result of rater effects, person misfit, or both.     

Hierarchical Logistic Regression: Accounting for Multilevel Data in DIF Detection

The purpose of this study was to examine the performance of differential item functioning (DIF) assessment in the presence of a multilevel structure that often underlies data from large-scale testing programs. Analyses were conducted using logistic regression (LR), a popular, flexible, and effective tool for DIF detection. Data were simulated using a hierarchical framework, such as might be seen when examinees are clustered in schools, for example. Both standard and hierarchical LR (accounting for multilevel data) approaches to DIF detection were employed. Results highlight the differences in DIF detection rates when the analytic strategy matches the data structure. Specifically, when the grouping variable was within clusters, LR and HLR performed similarly in terms of Type I error control and power. However, when the grouping variable was between clusters, LR failed to maintain the nominal Type I error rate of .05. HLR was able to maintain this rate. However, power for HLR tended to be low under many conditions in the between cluster variable case.     

基于Logistic回归模型的小学生上网研究

以西安市小学为样本,对小学生上网进行研究。研究表明:小学生上网时长、上网用途、家庭对小学生上网态度和学生的学习兴趣是影响小学生上网的主要因素。     

Assessment of Person Fit Using Resampling‐Based Approaches

De la Torre and Deng suggested a resampling‐based approach for person‐fit assessment (PFA). The approach involves the use of the statistic, a corrected expected a posteriori estimate of the examinee ability, and the Monte Carlo (MC) resampling method. The Type I error rate of the approach was closer to the nominal level than that of the traditional approach of using along with the assumption of a standard normal null distribution. This article suggests a generalized resampling‐based approach for PFA that allows one to employ or another person‐fit statistic (PFS) based on item response theory, the corrected expected a posteriori estimate or another ability estimate, and the MC method or another resampling method. The suggested approach includes the approach of de la Torre and Deng as a special case. Several approaches belonging to the generalized approach perform very similarly to the approach of de la Torre and Deng's in two simulation studies and in applications to three real data sets, irrespective of the PFS used. The generalized approach promises to be useful to those interested in resampling‐based PFA.     

基于逻辑回归模型的银行存款营销业务研究

现如今,存款营销成为了银行吸收存款的主要经营模式,如何通过现有数据建立模型来判断客户是否订阅存款业务,从而帮助商业银行更好地分配人力资源,提高业务量,以满足现阶段营销活动对提高营销成功率的期望.利用数据挖掘方法通过多种模型选择,并建立逻辑回归模型,然后对模型进行成功率判断以及变量重要性分析,最终了解到影响营销成功与否的关键因素,综合模型分析的结果,给出营销方案建议,最后提出现有研究的不足和期望.     

基于逻辑回归的毕业设计成绩影响因素分析

采用逻辑回归方法综合分析继续教育学生毕业设计成绩的影响因素,结果表明学生的平时成绩、年龄和性别是影响毕业设计成绩的主要因素。建议在今后的日常教学中,要引导学生加强平时课程学习,培养低年龄段学生学习兴趣,并督促帮助男同学努力学习。     

Evaluating Model Fit and Structural Coefficient Bias: A Bayesian Approach to Multilevel Bifactor Model Misspecification

Appropriate model specification is fundamental to unbiased parameter estimates and accurate model interpretations in structural equation modeling. Thus detecting potential model misspecification has drawn the attention of many researchers. This simulation study evaluates the efficacy of the Bayesian approach (the posterior predictive checking, or PPC procedure) under multilevel bifactor model misspecification (i.e., ignoring a specific factor at the within level). The impact of model misspecification on structural coefficients was also examined in terms of bias and power. Results showed that the PPC procedure performed better in detecting multilevel bifactor model misspecification, when the misspecification became more severe and sample size was larger. Structural coefficients were increasingly negatively biased at the within level, as model misspecification became more severe. Model misspecification at the within level affected the between-level structural coefficient estimates more when data dependency was lower and the number of clusters was smaller. Implications for researchers are discussed.     

基于Logistic回归变量筛选的BP神经网络及应用

BP神经网络是一种使用非线性可导函数作为传递函数的前馈神经网络,具有较高的精确度,但过多的预测变量会影响BP神经网络的准确性。采用Logistic回归变量筛选方法能在一定程度上提高分类准确性,提高模型效率。对2013年沪深两市A股分类评级进行了研究,证明基于Logistic回归变量筛选的神经网络提高了两极类别分类的准确性。     

基于有序Logistic回归模型的上海市大学生就业焦虑影响因素分析

高校毕业生就业形势日趋严峻,如何缓解大学生就业焦虑已成为当下心理学研究热点。文章利用半结构化访谈和问卷调查,收集上海部分高校大学生就业焦虑的影响因素,并基于有序Logistic回归模型分析各因素对大学生就业焦虑影响程度。研究结果表明,上海市高校大学生就业 焦虑影响因素包括自身因素和非自身因素。自身因素中,知识储备、能力欠缺和学校辅导对大学生焦虑程度起正向影响,学校名气对焦虑程度起负向影响;非自身因素中,同龄竞争和就业形势对上 海市大学生就业焦虑程度均起正向影响;相较于自身因素,非自身因素对毕业生就业焦虑影响效果更明显。     

我国民间投资的偏最小二乘回归研究

近年来，民间投资在我国经济增长中所起的作用越来越重要，其增长率几乎已经成为经济增长的“睛雨表”。针对影响民间投资的众多因素，我们用偏最小二乘法进行了回归分析，结果显示，提高最终消费率、降低银行贷款利率等措施将对提高民间投资总额起到积极作用。     

基于参数法对湖南省物流园区用地规模的预测

在当今物流业快速发展、物流园区建设快速扩张且取得较大成功的同时,物流园区的建设在规划上贪大求全,造成土地资源的浪费以及较高的园区闲置率等问题也随之而突显。以湖南省为例,利用参数法对湖南省物流需求总量及14个地州市2020年的物流园区用地规模进行预测,结果表明,2020年湖南省物流园区用地规模总需求为6 674.05hm2。该文旨在通过对湖南省物流用地规模的预测,为湖南省合理安排物流用地、实现物流业的科学发展提供参考。     

“新生代海归”就业质量的影响因素研究——基于有序Logistic—ISM模型的实证分析

随着越来越多80后和90后"新生代海归"选择回国发展,他们的就业质量随之引起了社会各界的广泛关注。本研究以203名"新生代海归"为研究对象,探讨社会人口学背景、海外教育经历、国内求职活动三方面对其客观及主观就业质量的影响。研究首先运用有序Logistic回归模型确定"新生代海归"就业质量的影响因素,发现回国时间、留学形式、留学区域、留学年限、学历、求职频率、面试次数、就业区域和单位类型影响客观层面的就业质量—即平均月薪;而独生子女、回国时间、留学形式、逆文化适应、第一份工作寻找时间、面试次数、offer次数、就业区域和单位类型等则影响主观层面的就业质量—即工作契合度、工作满意度和离职意向;同时,客观就业质量能够影响主观就业质量。根据上述发现,本研究使用ISM模型建立影响因素之间的关联关系与层次结构,发现社会人口学和部分海外教育经历因素在"新生代海归"的就业质量模型中发挥深层根源性作用,海外教育结果(如学历、逆文化适应等)是处于中间的关键连接因素,国内求职活动是影响就业质量的表层直接原因。基于上述分析,本研究最后提出了促进"新生代海归"高质量就业的建议。     

管理类课程参与式教学法之虚拟变量回归分析

文章运用线性回归模型,对参与式教学模式进行实证研究,并考察对同一门课程是否采用参与式教学方法的学生成绩差异。结果显示,学生的到课率、上期成绩和教学方法对最终成绩有着显著影响,采用参与式教学方法后,实验组的学生成绩较之对照组的学生成绩有明显效果。     

幼儿园教师招聘中人与组织契合度的现状研究

招聘的最高境界是达到人与组织契合,即用人单位与应聘者在某些特征上具有相似性或一致性,双方能够满足彼此某些方面的需求。本研究运用问卷法对454名幼儿园教师的人与组织契合度现状进行了调查,结果发现:幼儿园与教师契合度总体较高;不同维度的人与组织契合度中价值观契合度最高;目标契合度和能力——要求契合度次之;需求——供给契合度最低。幼儿园与教师契合度在教师学历、教师工作年限、幼儿园星级以及幼儿园办园年限上存在显著性差异。文章从定位园所特征、创新甄选模式和完善后期管理三个方面提出基于人与组织契合理论的招聘优化策略。     

高职院校物流人才培养定位问题的研究

近几年,物流业在我国得到了快速的发展。但是,我国物流教育的发展相对落后,尤其是高职院校如何开展物流专业教育、加强物流专业建设、培养合格的物流专业人才,成为一个新课题。高职院校的物流专业在教学建设中,必须体现各自不同的市场定位。这主要表现在三个方面,即:高职院校的教育层次定位、各校设置物流专业的教育特色定位以及各校学生就业的环境区域定位。     

触发脑梗死危险因素的Logistic回归分析

目的：探讨与脑梗死急性发作有关的触发危险因素，为有效防治突发脑梗死提供依据。方法：采用病例对照研究方法对研究对象进行调查，应用非条件Logistic回归模型完成单因素及多因素分析。结果：多因素非条件Logistic逐步回归显示近期感染OR=7．52，TIA的OR=2．11，情绪波动OR=1．59和预防性治疗OR=0．15与脑梗死猝发有关联，其差别有统计学意义（P〈0.05）。结论：近期感染、TIA、情绪波动是触发脑梗死的独立危险因素，而预防性治疗则是保护因素之一。     

A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

Although logistic regression became one of the well‐known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying note on those three tests when logistic regression is applied for DIF detection.     

大学生个人情况与抑郁症之间关系的Logistic回归分析

为了研究大学生个人情况与抑郁症之间的关系,本文对湖北武汉的华中师范大学、华中科技大学、中国地质大学、中南民族大学、华中农业大学、武汉理工大学等院校的6056名学生的个人情况与抑郁症情况进行了问卷调查;并对调查结果进行了Logistic回归分析,并发现其中9个因素对抑郁症的发病有显著影响;就其原因进行了简要的探讨,并与已有的结论进行了比较论证。研究结果为进一步研究大学生患抑郁症的诱因提供了科学依据,并对大学生抑郁症的防治有较好的指导意义。     

农村教师职业流动意愿及其影响因素——基于二元Logistic回归模型的实证研究

本文基于江苏、湖北、云南3省12县24所农村学校575名教师的调查数据,运用二元Logistic回归模型,从个体因素、家庭因素、工作因素、学校因素、地区因素以及社会因素等六个方面对农村教师职业流动意愿及影响因素进行实证分析。回归结果显示,在教师个体因素中,性别、年龄、文化程度、职称、个人月收入等变量显著影响农村教师职业流动意愿;在家庭因素中,配偶工作地点、子女就学状况等变量对其流动意愿影响显著;在工作因素中,与领导的关系、晋升机会等变量显著影响农村教师职业流动意愿;在学校因素中,学校地理位置变量对其流动意愿有重要影响;在地区因素中,地区变量对其流动意愿影响显著;在社会因素中,社会压力、教师对自身职业社会地位的评价等变量显著影响农村教师职业流动意愿。     

高校引进的海外高层次人才工作满意度分析--基于Logisitic回归模型

人才引进是高校建设和发展的重要环节,研究引进的海外高层次人才工作满意度具有重要的现实意义。本文基于海外高层次人才的调查问卷数据建立Logisitc回归模型,进行工作满意度的实证分析。结果表明：宽松、自由的单位工作氛围和科研教学的自由是影响高校海外高层次人才工作满意度的两个最主要的外部因素。