Maximum likelihood estimation of the joint covariance matrix for sections of tests given to distinct samples with application to test equating

Consider an old testX consisting ofs sections and two new testsY andZ similar toX consisting ofp andq sections respectively. All subjects are given testX plus two variable sections from either testY orZ. Different pairings of variable sections are given to each subsample of subjects. We present a method of estimating the covariance matrix of the combined test (X ₁, ...,X _s ,Y ₁, ...,Y _p ,Z ₁, ...,Z _q ) and describe an application of these estimation techniques to linear, observed-score, test equating.The author is indebted to Paul W. Holland and Donald B. Rubin for their encouragement and many helpful comments and suggestions that contributed significantly to the development of this paper.This research was supported by the Program Statistics Research Project of the ETS Research Statistics Group.     

Parental divorce as a factor in children''s perception of human relationships: An analysis of human movement responses in the Rorschach test

The subjects were 108 Swedish children between 10 and 12 years old, who constituted a divorce and a non-divorce group of 27 girls and 27 boys each. Rorschach Tests were administered individually and human movement responses were analysed. Non-divorce children perceived more cooperative and fewer hostile interactions than the divorce children. Responses containing figures engaged in hostile interaction were most common among divorce girls, whereas the divorce boys had difficulties in perceiving any interaction whatsoever. The different effect of parental divorce on the ability of girls and boys to develop satisfactory social relationships is discussed.     

Two simple classes of mastery scores based on the beta-binomial model

Two simple classes of mastery scores which are suitable for hand calculations are presented for beta-binomial test score distributions combined with linear and cubic referral success. The models provide a simple way to explore the consequences of selecting an arbitrary mastery score. Such assessment would be useful whenever the test user is not willing to posta priori a loss ratio, but wishes to look at the various consequences before aiming at a particular score.     

人际关系适应特征的情境评价方法研究

本研究运用情境评价法对人际关系适应特征进行了实验研究 ,结果表明 :1以 Schutz提出的人际关系六因素为评价标准 ,采用情境评价法比问卷形式能更有效地揭示人际适应特征 ;2在情境评价中 ,以合作为主的情境设置比以竞争为主的情境设置更利于反映出人际适应特征 ;3情境评价采用定向、组织、交流和问题解决等阶段的过程设计符合情境评价的实际进程 ,有助于分阶段展开被试的行为特征 ,提高情境评价的可控性和准确性。     

《中学生心理健康诊断测验》结构的探索及修改

在浙江省8个城市的6所高中和6所初中,采取分层整群抽样方法选取初一到高三600名学生,用修改后的《心理健康诊断测验》(MHT)进行了测查,着重检验该测验的结构。结果发现,因素分析所获取的因素与原测验相一致,但对原测验的项目进行了一些删减。因素分析和相关分析结果以及内部一致性系数显示,修改后的问卷具有较高的信度,其效度也符合心理测量的要求。     

初级军官心理选拔的预测性

目的：通过建立院校学员胜任特征及初级军官评价模型,探讨初级军官心理选拔检测系统的预测性。方法：某军校340名男性本科学员入校时完成初级军官心理选拔检测,毕业前进行院校胜任特征评价,对162名学员毕业后1~5年进行部队工作表现追踪评价。结果：中国MBTI-G人格类型测验对院校学员管理能力、影响力、约束力、口头表达、军人气质、人际关系等有较好的预测性,预测符合率72.13%;中国军人明尼苏达个性调查表6项精神障碍维度对情绪稳定性有较好的预测,预测符合率71.90%;院校学员一般能力倾向测验对学业成绩、文字表达、口头表达、人际关系和决策能力等胜任特征也有一定的预测性,预测符合率66.70%;三项测验院校胜任特征总预测符合率达81.70%,部队胜任特征总预测符合率76~87%。结论：为我军初级军官心理选拔提供了实用工具,为提高心理选拔预测准确性提供了科学依据     

考试焦虑影响考试成绩的基本模式

以整合的思路、建构包括九个中介变量的考试焦虑影响考试成绩的结构方程模型。用AM和MG两种路径分析方式相结合的方法．发现考试焦虑对中介变量的影响甚于中介变量对考试成绩的影响。考试自我效能感、成绩-接近目标、认知干扰是受考试焦虑影响和影响考试成绩程度比较一致的中介变量(简称为“一致性”的中介变量),成绩-回避目标定向、担忧、学习和考试技能变量屑于“非一致性”的中介变量。掌握目标定向、情绪性和应付反应作为中介变量不具有实际意义。     

不同铆测验设计下多维IRT等值方法的比较

实际应用中测验往往具有多维结构, 如果仍采用单维IRT方法进行等值, 会得到不准确的结果。因此对于多维结构的测验, 需要使用多维IRT等值方法来实现参数的转换。基于共同题设计, 文章通过模拟研究的方法, 考察了不同铆测验设计下几种多维IRT等值方法的表现, 同时考虑了测验长度、两个维度题目数量的比例、铆测验长度、铆测验的选择策略、两个维度之间的相关和等值群体的能力水平差异六个因素的影响。所比较的多维IRT等值方法有：均值/均值(MM)方法, 均值/标准差(MS)方法, Stoking-Lord (SL)方法, Haebara (HB)方法, 最小平方(LS)方法。结果显示：(1) SL, HB和LS方法得到的等值误差均方根最小, 且在各条件下表现较为稳定。(2) MM和MS方法在非等组条件下呈现出很大的误差均方根。(3)铆测验设计对SL, HB和LS方法的等值结果没有显著影响。(4)在两个维度之间的相关较高, 测验长度和铆测验长度较长, 等值群体的能力水平没有差异的条件下, SL, HB和LS方法得到的等值误差均方根最小。     

Empirical Priors in Polytomous Computerized Adaptive Tests: Risks and Rewards in Clinical Settings

The use of empirical prior information about participants has been shown to substantially improve the efficiency of computerized adaptive tests (CATs) in educational settings. However, it is unclear how these results translate to clinical settings, where small item banks with highly informative polytomous items often lead to very short CATs. We explored the risks and rewards of using prior information in CAT in two simulation studies, rooted in applied clinical examples. In the first simulation, prior precision and bias in the prior location were manipulated independently. Our results show that a precise personalized prior can meaningfully increase CAT efficiency. However, this reward comes with the potential risk of overconfidence in wrong empirical information (i.e., using a precise severely biased prior), which can lead to unnecessarily long tests, or severely biased estimates. The latter risk can be mitigated by setting a minimum number of items that are to be administered during the CAT, or by setting a less precise prior; be it at the expense of canceling out any efficiency gains. The second simulation, with more realistic bias and precision combinations in the empirical prior, places the prevalence of the potential risks in context. With similar estimation bias, an empirical prior reduced CAT test length, compared to a standard normal prior, in 68% of cases, by a median of 20%; while test length increased in only 3% of cases. The use of prior information in CAT seems to be a feasible and simple method to reduce test burden for patients and clinical practitioners alike.     

Evaluating Equating Transformations in IRT Observed-Score and Kernel Equating Methods

Test equating is a statistical procedure to ensure that scores from different test forms can be used interchangeably. There are several methodologies available to perform equating, some of which are based on the Classical Test Theory (CTT) framework and others are based on the Item Response Theory (IRT) framework. This article compares equating transformations originated from three different frameworks, namely IRT Observed-Score Equating (IRTOSE), Kernel Equating (KE), and IRT Kernel Equating (IRTKE). The comparisons were made under different data-generating scenarios, which include the development of a novel data-generation procedure that allows the simulation of test data without relying on IRT parameters while still providing control over some test score properties such as distribution skewness and item difficulty. Our results suggest that IRT methods tend to provide better results than KE even when the data are not generated from IRT processes. KE might be able to provide satisfactory results if a proper pre-smoothing solution can be found, while also being much faster than IRT methods. For daily applications, we recommend observing the sensibility of the results to the equating method, minding the importance of good model fit and meeting the assumptions of the framework.