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.     

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).     

社交媒体使用与错失焦虑的关系：一项元分析

社交媒体使用与错失焦虑均是当下生活中较为常见的现象, 诸多研究探讨了两者间的内在联系, 但研究结果却存在很大差异。为明确两者之间的整体关系, 以及产生分歧的原因, 对检索后获得的65项研究(70个独立样本)使用随机效应模型进行了元分析。结果发现：社交媒体使用与错失焦虑存在显著正相关(r = 0.38, 95% CI [0.34, 0.41]); 二者的相关强度受社交媒体使用测量指标和社交媒体类型的调节, 但不受性别、年龄、错失焦虑测量工具和个体主义指数的调节。结果一定程度上澄清了大众传播的社会认知理论和数字恰到好处假说的争论, 表明社交媒体使用程度越高的人往往也会伴随着较高水平的错失焦虑。防止社交媒体过度使用, 尤其是引导大众合理使用以图像为中心并且开放度较高的社交媒体有助于错失焦虑的缓解。     

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.     

错失恐惧对亲社会行为的影响

网络信息的纷繁复杂,让人们担忧因错失信息而导致自身的利益受损,本研究探讨这种现象的加剧对个体的亲社会行为的影响。本研究分别使用问卷法（研究1）和实验法（研究2）,探究错失恐惧对亲社会行为的影响以及在其过程中人际安全感的中介作用和基本心理需要满足的调节作用。结果发现：（1）个体的错失恐惧程度负向预测了其亲社会行为;（2）人际安全感在错失恐惧对亲社会行为的影响中起中介作用,高的错失恐惧水平会降低个体的人际安全感,从而减少个体的亲社会行为;（3）基本心理需要满足在错失恐惧对亲社会行为的负向预测作用中起调节作用,当个体的基本心理需要满足程度低的时候,错失恐惧对亲社会行为的消极影响显著增强。     

多级评分认知诊断模型述评

认知诊断评估旨在探讨个体内部的知识掌握结构,并提供关于学生优缺点的详细诊断信息,以促进个体的全面发展。当前研究者已开发了大量0-1评分的认知诊断模型,但对于多级评分认知诊断模型的研究还比较少。本文对已有的多级评分认知诊断模型进行了归纳,介绍了模型的假设,计量特征以及适用范围,为实际应用者和研究者在多级评分认知诊断模型的比较和选用上提供借鉴和参考。最后,对未来关于多级评分诊断模型的研究方向进行了展望。     

Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.     

Testing two variances for superiority/non-inferiority and equivalence: Using the exhaustion algorithm for sample size allocation with cost

The equality of two group variances is frequently tested in experiments. However, criticisms of null hypothesis statistical testing on means have recently arisen and there is interest in other types of statistical tests of hypotheses, such as superiority/non-inferiority and equivalence. Although these tests have become more common in psychology and social sciences, the corresponding sample size estimation for these tests is rarely discussed, especially when the sampling unit costs are unequal or group sizes are unequal for two groups. Thus, for finding optimal sample size, the present study derived an initial allocation by approximating the percentiles of an F distribution with the percentiles of the standard normal distribution and used the exhaustion algorithm to select the best combination of group sizes, thereby ensuring the resulting power reaches the designated level and is maximal with a minimal total cost. In this manner, optimization of sample size planning is achieved. The proposed sample size determination has a wide range of applications and is efficient in terms of Type I errors and statistical power in simulations. Finally, an illustrative example from a report by the Health Survey for England, 1995–1997, is presented using hypertension data. For ease of application, four R Shiny apps are provided and benchmarks for setting equivalence margins are suggested.