Canonical analysis of longitudinal and repeated measures data with stationary weights

When measuring the same variables on different occasions, two procedures for canonical analysis with stationary compositing weights are developed. The first, SUMCOV, maximizes the sum of the covariances of the canonical variates subject to norming constraints. The second, COLLIN, maximizes the largest root of the covariances of the canonical variates subject to norming constraints. A characterization theorem establishes a model building approach. Both methods are extended to allow for Cohort Sequential Designs. Finally a numerical illustration utilizing Nesselroade and Baltes data is presented.The authors wish to thank John Nesselroade for permitting us to use the data whose analysis we present.     

Back to basics: Percentage agreement measures are adequate, but there are easier ways

Percentage agreement measures of interobserver agreement or "reliability" have traditionally been used to summarize observer agreement from studies using interval recording, time-sampling, and trial-scoring data collection procedures. Recent articles disagree on whether to continue using these percentage agreement measures, and on which ones to use, and what to do about chance agreements if their use is continued. Much of the disagreement derives from the need to be reasonably certain we do not accept as evidence of true interobserver agreement those agreement levels which are substantially probable as a result of chance observer agreement. The various percentage agreement measures are shown to be adequate to this task, but easier ways are discussed. Tables are given to permit checking to see if obtained disagreements are unlikely due to chance. Particularly important is the discovery of a simple rule that, when met, makes the tables unnecessary. If reliability checks using 50 or more observation occasions produce 10% or fewer disagreements, for behavior rates from 10% through 90%, the agreement achieved is quite improbably the result of chance agreement.     

Axiomatic and numerical conjoint measurement: An evaluation of diagnostic efficacy

Synthetic data are used to examine how well axiomatic and numerical conjoint measurement methods, individually and comparatively, recover simple polynomial generators in three dimensions. The study illustrates extensions of numerical conjoint measurement (NCM) to identify and model distributive and dual-distributive, in addition to the usual additive, data structures. It was found that while minimum STRESS was the criterion of fit, another statistic, predictive capability, provided a better diagnosis of the known generating model. That NCM methods were able to better identify generating models conflicts with Krantz and Tversky's assertion that, in general, the direct axiom tests provide a more powerful diagnostic test between alternative composition rules than does evaluation of numerical correspondence. For all methods, dual-distributive models are most difficult to recover, while consistent with past studies, the additive model is the most robust of the fitted models.Douglas Emery is now at the Krannert Graduate School of Management, Purdue University, West Lafayette, IN, on leave from the University of Calgary.     

Two-step weighted least squares factor analysis of dichotomized variables

A two-step weighted least squares estimator for multiple factor analysis of dichotomized variables is discussed. The estimator is based on the first and second order joint probabilities. Asymptotic standard errors and a model test are obtained by applying the Jackknife procedure.     

人工智能辅助下的心理健康新型测评

近年来,人工智能技术的飞速发展及应用催生了"智能化心理健康测评"这一领域。智能化心理健康测评能够弥补传统方法的不足,降低漏诊率并提高诊断效率,这对于心理健康问题的普查及预警具有重大意义。目前,智能化心理健康测评处于初步发展阶段,研究者基于在线行为数据、便携式设备数据等开展主要以数据驱动为导向的探索研究,旨在实现更高的预测准确率,但是测评结果的可解释性等指标尚不够理想。未来的智能化心理健康测评需要强调心理学领域知识和经验的深度介入,提高测评的针对性和精细化程度,加强信效度检验,这对于智能化心理健康测评工具的进一步发展和应用至关重要。     

人工智能辅助的自闭症早期患者的筛查与诊断

自闭症谱系障碍(AutisticSpectrumDisorders,ASD)的症状早在婴幼儿期就会显现,越早发现,越早干预,治疗效果越好。传统自闭症早期筛查与诊断在评估方法、流程上存在局限,无法满足大规模筛查和诊断需求。随着人工智能技术的快速发展,使用智能化方法进行自闭症早期大规模无感筛查与诊断逐渐成为可能。近10年间,国内外对自闭症智能化识别方法的探索在经典任务行为、面部表情和情绪、眼动、脑影像、运动控制和运动模式、多模态6个领域积累了丰富的研究成果。未来研究应围绕构建国内自闭症早期智能医学筛查与诊断体系,开发针对婴幼儿患者的筛查工具,构建融合多模态数据的自闭症婴幼儿智能化识别模型,建立结合脑影像技术的自闭症精细化诊断方法等方面来开展。     

Parsing as a Cue-Based Retrieval Model

This paper develops a novel psycholinguistic parser and tests it against experimental and corpus reading data. The parser builds on the recent research into memory structures, which argues that memory retrieval is content-addressable and cue-based. It is shown that the theory of cue-based memory systems can be combined with transition-based parsing to produce a parser that, when combined with the cognitive architecture ACT-R, can model reading and predict online behavioral measures (reading times and regressions). The parser's modeling capacities are tested against self-paced reading experimental data (Grodner & Gibson, 2005), eye-tracking experimental data (Staub, 2011), and a self-paced reading corpus (Futrell et al., 2018).     

Computer‐based training to teach observers to accurately score problem behavior using fast forwarding at 5x normal speed

The current study evaluated whether a computer‐based training program could improve observers' accuracy in scoring discrete instances of problem behavior at 5x normal speed using a multiple‐baseline design across subjects. During pretraining and posttraining, observers attempted to score multiple examples of problem behavior at 5.0x without feedback. During training, participants scored multiple examples of problem behavior at 5.0x with automated feedback. Researchers measured omission (missing problem behavior) and commission (scoring other behavior as problem behavior) errors and the total duration of scoring time to determine the observers' accuracy and efficiency, respectively. After training, all participants scored instances of problem behavior with less than 11% error using 5.0x. The time required to score the videos across 90‐min observations was reduced by 66%. Results extend previous evaluations of fast forwarding by demonstrating that the training program could be used to teach observers to accurately score problem behavior using a speed faster than 3.5x.