Consensus Rorschachs and Related Procedures for Studying Interpersonal Patterns

A psychological "type" characterized by repetitive and compulsive action tendencies but not by obsessive thoughts was delineated. It was shown how this syndrome could be identified through differential inter-test patterning on projective techniques. The possibility of improving psychodiagnostic accuracy by stipulating expected configurations among tests in a battery was noted.     

Patterns of Aberrant Eating Among Pre-adolescent Children in Foster Care

The paper reports epidemiological and phenomenological investigations of aberrant eating among 347 pre-adolescent children in court-ordered foster and kinship care, in New South Wales, Australia. A quarter of children displayed clinically significant aberrant eating problems, with no evidence of gender or age effects. Two distinct patterns were identified. The first is a pattern of excessive eating and food acquisition and maintenance behaviors without concurrent obesity (termed Food maintenance syndrome), resembling the behavioral correlates of Hyperphagic Short Stature (Psychosocial Dwarfism). Various data suggest this pattern is primarily triggered by acute stress, including maltreatment in care, against a background of complex psychopathology and developmental disabilities. The second is a cluster of pica-type eating behaviors that correlates with self-injurious behavior, and is closely associated with developmental disabilities. The paper includes recommendations for clinicians working with pre-adolescent children in care.     

Bayesian SEM for Specification Search Problems in Testing Factorial Invariance

Specification search problems refer to two important but under-addressed issues in testing for factorial invariance: how to select proper reference indicators and how to locate specific non-invariant parameters. In this study, we propose a two-step procedure to solve these issues. Step 1 is to identify a proper reference indicator using the Bayesian structural equation modeling approach. An item is selected if it is associated with the highest likelihood to be invariant across groups. Step 2 is to locate specific non-invariant parameters, given that a proper reference indicator has already been selected in Step 1. A series of simulation analyses show that the proposed method performs well under a variety of data conditions, and optimal performance is observed under conditions of large magnitude of non-invariance, low proportion of non-invariance, and large sample sizes. We also provide an empirical example to demonstrate the specific procedures to implement the proposed method in applied research. The importance and influences are discussed regarding the choices of informative priors with zero mean and small variances. Extensions and limitations are also pointed out.     

Variable-Length Stopping Rules for Multidimensional Computerized Adaptive Testing

Psychometrika - In computerized adaptive testing (CAT), a variable-length stopping rule refers to ending item administration after a pre-specified measurement precision standard has been satisfied....     

Optimal Online Calibration Designs for Item Replenishment in Adaptive Testing

The maintenance of item bank is essential for continuously implementing adaptive tests. Calibration of new items online provides an opportunity to efficiently replenish items for the operational item bank. In this study, a new optimal design for online calibration (referred to as D-c) is proposed by incorporating the idea of original D-optimal design into the reformed D-optimal design proposed by van der Linden and Ren (Psychometrika 80:263–288, 2015) (denoted as D-VR design). To deal with the dependence of design criteria on the unknown item parameters of new items, Bayesian versions of the locally optimal designs (e.g., D-c and D-VR) are put forward by adding prior information to the new items. In the simulation implementation of the locally optimal designs, five calibration sample sizes were used to obtain different levels of estimation precision for the initial item parameters, and two approaches were used to obtain the prior distributions in Bayesian optimal designs. Results showed that the D-c design performed well and retired smaller number of new items than the D-VR design at almost all levels of examinee sample size; the Bayesian version of D-c using the prior obtained from the operational items worked better than that using the default priors in BILOG-MG and PARSCALE; and Bayesian optimal designs generally outperformed locally optimal designs when the initial item parameters of the new items were poorly estimated.

     

Procedures for Identifying Persons with Potential for Public School Administrative Positions by William C. Schutz

This investigation is based on over 400 American Group Psychotherapy Association members involved in 41 intensive, two-day training experiences for mental health professionals. The participants completed a questionnaire immediately after their group sessions to evaluate the process and leadership variables that contributed to a constructive learning experience. A similar questionnaire was mailed to participants three to four months later to explore the impact of training on group interventions within their clinical practices. One third of the trainees responded to the follow-up survey. Overall, the findings suggest that successful outcomes are related to a range of group processes, such as self-disclosure, feedback, and interpersonal support, as well as personal qualities and technical expertise modeled by the leaders of the training groups. Both the immediate and delayed assessments demonstrate that the groups were regarded as highly valuable learning opportunities.     

计算机化自适应测验选题策略述评

计算机化自适应测验(computerized adaptive testing, CAT)是基于测量理论和计算机技术的一种测验模式。它根据考生的作答反应自适应地选择测验项目。选题策略是CAT的重要组成部分之一, 关系到测量效率、测验安全和测验信、效度等重要问题。根据CAT是否具有非统计约束对传统CAT和认知诊断CAT的选题策略进行了分类介绍, 未来研究应进一步提高选题策略的综合表现、深入探讨多级评分项目和认知诊断CAT的选题策略。     

计算机自适应测验中测验安全控制方法评述

计算机自适应测验在实际应用中曾经受到了测验安全问题质疑。国内外学者主要从两种研究思路提出了测验安全控制的方法：一是控制项目的最大曝光率, 沿着这个思路发展出来的方法有SH法、项目合格方法、多重最大曝光率法等; 二是改进选题策略, 沿着这个思路发展的方法主要是 分层法及其变式。此外, 近年来出现了测验安全控制方法之间相结合的研究思路。本文从均方误差、项目曝光率、题库利用率等指标论述了测验安全控制方法的优缺点, 并概述了这些测验安全控制方法的研究发展历程与发展思路, 展望了今后的研究趋势。     

计算机化多阶段自适应测验研究述评

摘 要 计算机化多阶段自适应测验是基于计算机技术的测验形式,它将题目集合作为测试单元,通过多阶段自适应的形式对被试进行测试和评分。近年来通过研究各种测验形式,发现其比计算机化自适应测验和传统纸笔测验突显出更大优势。与传统纸笔测验相比,其具有参数不变性、能力估计更精确等优势。与计算机化自适应测验相比,其具有可控制题目特性、被试可检查题目等优势。如何减小测量误差,使其应用更加便捷、有效,是未来研究的发展方向。     

Statistical Foundations for Computerized Adaptive Testing with Response Revision

Psychometrika - The compatibility of computerized adaptive testing (CAT) with response revision has been a topic of debate in psychometrics for many years. The problem is to provide test takers...     

Bayesian Risk Assessment in Genetic Testing for Autosomal Dominant Disorders with Age-Dependent Penetrance

Risk assessment is an essential component of genetic counseling and testing, and the accuracy of risk assessment is critical for decision making by consultands. However, it has been shown that genetic risk calculations may have high error rates in practice. Risk calculations for autosomal dominant disorders are frequently complicated by age-dependent penetrance and sensitivities of less than 100% in genetic testing. We provide methods of risk calculation for prototypical pedigrees of a family at risk for an autosomal dominant disorder with age-dependent penetrance. Our risk calculations include scenarios in which the sensitivity of genetic testing is less than 100%, and in which the sensitivity of genetic testing varies for different family members at risk. Our Bayesian methods permit autosomal dominant disease probabilities to be calculated accurately, taking into account all relevant information. Our methods are particularly useful for hereditary cancer syndromes, in which genetic testing can seldom achieve 100% sensitivity. Our methods can be applied to many different scenarios, including those where the sensitivity of genetic testing varies for different family members at risk. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.     

Two Simulated Feasibility Studies in Computerised Adaptive Testing

This paper describes two simulated feasibility studies conducted in support of a project to implement computerised adaptive testing on microcomputers for college placement purposes. The first study compares adaptive testing with alternative conventional peaked tests. The second study explores two stopping rules: fixed test length and fixed standard error of measurement. These studies typify the kind of psychometric research required to initiate and support operational adaptive testing programs.
Cet article décrit deux études de modèles de faisabilité conduites pour rendre fiable un projet de construction de batterie de tests informatisés sur micro, en vue ďorganiser le placement dans les collèges. La première étude compare cette catégorie de tests avec les tests conventionnels les plus connus. La deuxième explore deux règies: la longueur imposée du test et ľerreur standard imposée dans la mesure. Ces études fixent le type de recherche psychométrique requis par ľinitiation et le support opérationnel de programmes de tests informatisés.     

计算机化自适应测验在认知诊断中的应用

作为认知诊断与计算机化自适应测验相结合的产物, 认知诊断计算机化自适应测验(Cognitive Diagnostic Computerized Adaptive Testing, CD-CAT)是对被试知识状态的自适应。它既有传统CAT所面临的普遍性问题, 也有在认知诊断中遇到的特殊问题：由于认知诊断中涉及属性这一概念, CD-CAT与传统CAT有很大的差别。本文紧紧围绕属性引起的差异, 分别从认知诊断模型、题库建设、起始规则、选题策略、被试知识状态估计和终止规则等几部分详细介绍CD-CAT的研究进展和存在的问题。     

认知诊断计算机化自适应测验中的项目增补

项目的增补对认知诊断计算机化自适应测验(CD-CAT)题库的开发与维护至关重要。借鉴单维项目反应理论(IRT)中联合极大似然估计方法(JMLE)的思路, 提出联合估计算法(JEA), 仅依赖被试在旧题和新题上的作答反应联合地、自动地估计新题的属性向量和新题的项目参数。研究结果表明：当项目参数相对较小且样本量相对较大时, JEA算法在新题属性向量和新题项目参数估计精度方面表现不错; 而且样本大小、项目参数大小以及项目参数初值都影响着JEA算法的表现。     

Abstract: Identifying Longitudinal Patterns of Adherence to Treatment for Obstructive Sleep Apnea

Increasing adherence to medical recommendations is crucial for improving health outcomes and reducing costs of health care. To improve adherence, we have to better understand behavior change over time. The focus of this study was adherence to treatment for obstructive sleep apnea (OSA). Adherence to positive airway pressure (PAP), the most common treatment for OSA, is poor. This study involved an international sample of 161 participants, each with approximately 180 nights of data, and had three phases. First, a separate time series analysis was performed for each individual. Time series parameters included the mean (average hours of use per night), level (the intercept), slope (the rate of change over time), variance (variability in use), and autocorrelation (a measure of dependency). Second, a dynamic cluster analysis was performed to find homogenous subgroups of individuals with similar adherence patterns. A four-cluster solution was found, and the subgroups were labeled (see Figure 1): Great Users (17.2%; high mean and level, no slope), Good Users (32.8%; moderate mean and level, no slope), Poor Users (22.7%; low mean and level, negative slope), and Slow Decliners (moderate mean and level, negative slope, high variance). Third, participants in the identified subgroups were compared on a number of variables that were not involved in the clustering to establish external validity. Some notable findings at later time points include the following: Great Users reported the most self-efficacy (confidence to use PAP), Poor Users reported the most sleepiness, and Great Users reported the highest quality of sleep. Combining time series analysis and dynamic cluster analysis is a useful way to evaluate adherence patterns at both the individual level and subgroup level. Psychological variables relevant to adherence patterns, such as self-efficacy, could be the focus of interventions to increase PAP usage.     

认知诊断计算机化自适应测验中在线标定方法的开发

项目增补对认知诊断计算机化自适应测验(CD-CAT)中的题库维护至关重要。在传统CAT中, 在线标定方法经常用于估计新题的项目参数。然而直到现在, 在CD-CAT领域还没有任何关于在线标定的论文公开发表。为将传统CAT中3种有代表性的在线标定方法(Method A、OEM和 MEM)推广至CD-CAT (CD-Method A、CD-OEM和CD-MEM)建立分析基础, 并采用模拟方法对这3种方法进行比较。研究表明：CD-Method A方法在项目参数的返真性方面优于其它两种方法; 自适应标定设计较随机标定设计可以提高项目参数的返真质量。     

A New Online Calibration Method for Multidimensional Computerized Adaptive Testing

Multidimensional-Method A (M-Method A) has been proposed as an efficient and effective online calibration method for multidimensional computerized adaptive testing (MCAT) (Chen & Xin, Paper presented at the 78th Meeting of the Psychometric Society, Arnhem, The Netherlands, 2013). However, a key assumption of M-Method A is that it treats person parameter estimates as their true values, thus this method might yield erroneous item calibration when person parameter estimates contain non-ignorable measurement errors. To improve the performance of M-Method A, this paper proposes a new MCAT online calibration method, namely, the full functional MLE-M-Method A (FFMLE-M-Method A). This new method combines the full functional MLE (Jones & Jin in Psychometrika 59:59–75, 1994; Stefanski & Carroll in Annals of Statistics 13:1335–1351, 1985) with the original M-Method A in an effort to correct for the estimation error of ability vector that might otherwise adversely affect the precision of item calibration. Two correction schemes are also proposed when implementing the new method. A simulation study was conducted to show that the new method generated more accurate item parameter estimation than the original M-Method A in almost all conditions.     

定长CD-CAT中两种新的题目曝光控制方法

提出了两种适用于定长CD-CAT的题目曝光控制方法（HIRP、HIRT）,这些方法在保证较高分类准确率的同时还有较合理的题目曝光率,新方法由二分化方法和RP及RT方法进行结合并适当调整而得到。模拟研究比较了其与RP、RT、SM、SMIE、RHA和SDBS的表现,结果表明: （1）HIRP的分类准确率和题目曝光率均好于SM、SMIE和SDBS;（2）HIRT的题目曝光率较RP、SM、SMIE、RHA和SDBS稍差,但分类准确率更高;（3）HIRP的分类准确率低于RT和RP,但题目曝光控制要更好。     

A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing

Statistical methods for identifying aberrances on psychological and educational tests are pivotal to detect flaws in the design of a test or irregular behavior of test takers. Two approaches have been taken in the past to address the challenge of aberrant behavior detection, which are (1) modeling aberrant behavior via mixture modeling methods, and (2) flagging aberrant behavior via residual based outlier detection methods. In this paper, we propose a two-stage method that is conceived of as a combination of both approaches. In the first stage, a mixture hierarchical model is fitted to the response and response time data to distinguish normal and aberrant behaviors using Markov chain Monte Carlo (MCMC) algorithm. In the second stage, a further distinction between rapid guessing and cheating behavior is made at a person level using a Bayesian residual index. Simulation results show that the two-stage method yields accurate item and person parameter estimates, as well as high true detection rate and low false detection rate, under different manipulated conditions mimicking NAEP parameters. A real data example is given in the end to illustrate the potential application of the proposed method.