Discounting: A practical guide to multilevel analysis of choice data

Multilevel modeling provides the ability to simultaneously evaluate the discounting of individuals and groups by examining choices between smaller sooner and larger later rewards. A multilevel logistic regression approach is advocated in which sensitivity to relative reward magnitude and relative delay are considered as separate contributors to choice. Examples of how to fit choice data using multilevel logistic models are provided to help researchers in the adoption of these methods.     

农村留守儿童焦虑情绪的异质性:基于潜在剖面分析

采用中学生心理健康诊断量表（MHT）对湖南省1292名留守儿童的焦虑情绪进行施测,并用潜在剖面分析方法探究留守儿童的焦虑情绪问题是否存在异质性,以及留守儿童相关的人口学因素对焦虑情绪异质性的作用机制。结果发现,留守儿童的焦虑情绪存在明显的异质性,可以分为三种潜在类别：“严重焦虑情绪型”、“中等程度焦虑情绪型”和“低焦虑情绪型”;留守男生、小学五年级、高中二年级、留守时间越长、留守初始年龄越小的留守儿童更容易焦虑情绪严重;无论是父亲外出、母亲外出还是双亲外出对留守儿童的焦虑情绪严重性的影响是一致的。由此,应根据留守儿童的性别、年级、留守时长和留守的初始年龄等情况特点,开展针对性的预防和干预措施,缓解留守儿童焦虑。     

A Computational Model of the Self‐Teaching Hypothesis Based on the Dual‐Route Cascaded Model of Reading

The self‐teaching hypothesis describes how children progress toward skilled sight‐word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self‐teach new orthographic knowledge. We present a new computational implementation of self‐teaching within the dual‐route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self‐teaching under a variety of circumstances. The new model (ST‐DRC) uses DRC’s sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight‐word reading. ST‐DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular‐word learning. Beyond modeling orthographic learning and self‐teaching, ST‐DRC’s performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned.     

The longitudinal association between internalizing symptoms and academic achievement among immigrant and non‐immigrant children in Norway

The aim of this study was to explore the longitudinal relation between internalizing symptoms and academic achievement, as two processes of children and youth development, among children in Norway, and whether having an immigrant background moderated this association. Data collected from 4,458 students in Norway in four waves over three years were analyzed with multi‐group latent growth curve modeling (LGM ). Results showed that internalizing symptoms level remained unchanged over time both for immigrant and non‐immigrant children, while levels of academic achievement increased only for children of immigrants with both parents born outside of Norway. Further analyses supported a reciprocal relation between internalizing symptoms and academic achievement and revealed that the initial level of academic achievement predicted the rate of change in internalizing symptoms over time, but not vice versa. Moreover, immigrant background did not moderate the associations in the model, however, children of immigrants with both parents born abroad initially had lower levels of academic achievement, but showed an increase in academic achievement, compared to their non‐immigrant peers as well as to peers with one native‐born parent after controlling for gender and their grade at the first observation. The implications for policy and practice were discussed.     

Latent class analysis of indicators of intolerance of uncertainty

Intolerance of Uncertainty (IU ) is a transdiagnostic vulnerability factor involved in depression and anxiety symptoms and disorders. IU encompasses Prospective IU (“Unforeseen events upset me greatly”) and Inhibitory IU (“The smallest doubt can stop me from acting”). Research has yet to explore whether subgroups or classes of people exist characterized by different profiles of IU . This study used latent class analysis to identify such subgroups and examined if different classes of IU were distinct in terms of several cognitive vulnerabilities and psychological symptoms. Data were obtained from 519 students completing a 12‐item measure of IU . Four subgroups were identified, characterized by low IU , predominantly Prospective IU , predominantly Inhibitory IU , and high IU , respectively. People in the high IU class reported cognitive vulnerabilities and depression and anxiety more than people in the low IU class. Inhibitory IU was more strongly associated with poor outcomes than was Prospective IU .     

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male’s emotion dynamics over time, but not in the female’s—which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.     

Latent Growth Curve and Repeated Measures ANOVA Contrasts: What the Models are Telling You

The relationship between the latent growth curve and repeated measures ANOVA models is often misunderstood. Although a number of investigators have looked into the similarities and differences among these models, a cursory reading of the literature can give the impression that they are very different models. Here we show that each model represents a set of contrasts on the occasion means. We demonstrate that the fixed effects parameters of the estimated basis vector latent growth curve model are merely a transformation of the repeated measures ANOVA fixed effects parameters. We further show that differences in fit in models that estimate the same means structure can be due to the different error covariance structures implied by the model. We show these relationships both algebraically and through using data from a simulation.