Maximum likelihood estimation of latent interaction effects with the LMS method

In the context of structural equation modeling, a general interaction model with multiple latent interaction effects is introduced. A stochastic analysis represents the nonnormal distribution of the joint indicator vector as a finite mixture of normal distributions. The Latent Moderated Structural Equations (LMS) approach is a new method developed for the analysis of the general interaction model that utilizes the mixture distribution and provides a ML estimation of model parameters by adapting the EM algorithm. The finite sample properties and the robustness of LMS are discussed. Finally, the applicability of the new method is illustrated by an empirical example. This research has been supported by a grant from the Deutsche Forschungsgemeinschaft, Germany, No. Mo 474/1 and Mo 474/2. The data for the empirical example have been provided by Andreas Thiele of the University of Frankfurt, Germany. The authors are indebted to an associate editor and to three anonymous reviewers ofPsychometrika whose comments and suggestions have been very helpful.     

Culturally Informed and Flexible Family-Based Treatment for Adolescents: A Tailored and Integrative Treatment for Hispanic Youth

The increasing utilization of evidence-based treatments has highlighted the need for treatment development efforts that can craft interventions that are effective with Hispanic substance abusing youth and their families. The list of evidence-based treatments is extremely limited in its inclusion of interventions that are explicitly responsive to the unique characteristics and treatment needs of young Hispanics and that have been rigorously tested with this population. Some treatments that have been tested with Hispanics do not articulate the manner in which cultural characteristics and therapy processes interact. Other treatments have emphasized the important role of culture but have not been tested rigorously. The value of well designed interventions built upon an appreciation for unique patient characteristics was highlighted by Beutler et al. (1996) when they argued that "psychotherapy is comprised of a set of complex tasks, and practitioners need comprehensive knowledge of how different processes used in psychotherapy interact with patient characteristics in order to make treatment decisions that will maximize and optimize therapeutic power" (p. 30). A focus on how treatment processes interact with patient characteristics is particularly relevant in the Hispanic population because of the considerable heterogeneity beneath the Hispanic umbrella. Our new program of clinical research focuses on articulating how the varied profiles with regard to immigration stressors, acculturation processes, values clashes, sense of belonging to the community, discrimination, and knowledge about issues important to adolescent health can be more effectively addressed by a culturally informed treatment.     

Adaptive logics using the minimal abnormality strategy are $$\Pi^1_1$$ -complex

In this article complexity results for adaptive logics using the minimal abnormality strategy are presented. It is proven here that the consequence set of some recursive premise sets is -complete. So, the complexity results in (Horsten and Welch, Synthese 158:41–60, 2007) are mistaken for adaptive logics using the minimal abnormality strategy.     

Speech perception engages a general timer: Evidence from a divided attention word identification task

Time is essential to speech. The duration of speech segments plays a critical role in the perceptual identification of these segments, and therefore in that of spoken words. Here, using a French word identification task, we show that vowels are perceived as shorter when attention is divided between two tasks, as compared to a single task control condition. This temporal underestimation pattern is consistent with attentional models of timing and hence demonstrates that vowel duration is explicitly estimated using a central general-purpose timer.     

铆题比例对等值精度的影响

在非等组铆测验设计中,铆题量占测验长度的多大比例比较合适,这个比例随测验长度的增大可否发生变化？这些是实际工作者和研究者非常关心的问题。该文在固定被试数和测验长度的条件下,探查铆题量所占测验长度比例（简称铆题比例）的变化对等值精度的影响,讨论了在实际等值中如何在等值精度和铆题比例之间取得平衡的问题。并在模拟研究的条件下,给出了几个反应实际等值精度的指标。     

时距估计中的重复刺激效应

Treisman及其同事最早发现人类对时距的判断很容易受到重复刺激的影响而发生扭曲或偏离,并认为时距估计中的重复刺激具有两种效应。着重介绍了时距估计中重复刺激效应产生的机制及其理论模型、重复刺激效应的影响因素。提出了时距估计中重复刺激效应未来研究中值得关注的几个主要问题。     

时间复制任务中的计时中断效应

以计时中断范式和时间复制任务相结合,考察了时间复制任务中是否存在计时中断效应。结果表明：（1）计时中断范式中1700ms和2300ms时距复制均出现高估现象,说明两种时距均低于相邻的高低估现象的转换点;（2）计时中断范式中1700ms和2300ms的时距复制任务没有支持计时标量特性;（3）计时中断范式中1700ms和2300ms的时间复制任务出现了计时中断效应。     

多阶段混合增长模型的方法及研究现状

多阶段混合增长模型(PGMM)可对发展过程中的阶段性及群体异质性特征进行分析,在能力发展、行为发展及干预、临床心理等研究领域应用广泛。PGMM可在结构方程模型和随机系数模型框架下定义,通常使用基于EM算法的极大似然估计和基于马尔科夫链蒙特卡洛模拟的贝叶斯推断两种方法进行参数估计。样本量、测量时间点数、潜在类别距离等因素对模型及参数估计有显著影响。未来应加强PGMM与其它增长模型的比较研究;在相同或不同的模型框架下研究数据特征、类别属性等对参数估计方法的影响。     

Resiliency in the Face of Adversity: A Short Longitudinal Test of the Trait Hypothesis

Resilience represents coping with adversity and is in line with a more positive paradigm for viewing responses to adversity. Most research has focused on resilience as coping—a state-based response to adversity. However, a competing hypothesis views resilience or resiliency as a trait that exists across time and types of adversity. We tested undergraduates enrolled in social work classes at a large southern university at two time periods during a single semester using measures of adversity, positive and negative affect, and trait-based resiliency. Consistent with the trait-based resiliency, and in contrast to state-based resilience, resiliency scores were not strongly correlated with adversity at both testing points but were with positive affect, and resiliency scores remained the same over time despite adversity variations. There was no gender or ethnic group difference in resilience scores. Black/African Americans reported significantly less negative affect and more positive affect than White/Caucasians.     

Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach

Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one‐step‐ahead strategies in which the stimulus on each trial is selected to maximize inference on the next trial only. A lingering question in the field has been how much additional benefit would be gained by optimizing beyond the next trial. A range of technical challenges has prevented this important question from being addressed adequately. This study applies dynamic programming (DP), a technique applicable for such full‐horizon, “global” optimization, to model‐based perceptual threshold estimation, a domain that has been a major beneficiary of adaptive methods. The results provide insight into conditions that will benefit from optimizing beyond the next trial. Implications for the use of adaptive methods in cognitive science are discussed.