Misspecification in Latent Change Score Models: Consequences for Parameter Estimation,Model Evaluation,and Predicting Change

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.     

品牌印象形成与改变的双重加工模型检验

为了探讨品牌印象的双重结构及其形成与改变的心理机制, 在双重加工模型理论基础上, 采用2×2×3完全随机被试间实验设计, 先后进行两个实验研究。结果显示：外显品牌印象的形成与改变遵循规则加工, 内隐品牌印象的形成与改变遵循联想加工, 说明二者是相互独立的两个心理结构, 为双重加工的双重系统观点提供了新证据。Smith和DeCoster (2000)的双重加工模型未考虑被试对信息的卷入程度对联想加工和规则加工的影响, 使其理论解释力不够, 此研究结果正好在这方面取得了进展。     

The Latent Change Score Model: A More Flexible Approach to Modeling Time in Self-Regulated Learning

Purpose

This research advances understanding of empirical time modeling techniques in self-regulated learning research. We intuitively explain several such methods by situating their use in the extant literature. Further, we note key statistical and inferential assumptions of each method while making clear the inferential consequences of inattention to such assumptions.

Design/Methodology/Approach

Using a population model derived from a recent large-scale review of the training and work learning literature, we employ a Monte Carlo simulation fitting six variations of linear mixed models, seven variations of latent common factor models, and a single latent change score model to 1500 simulated datasets.

Findings

The latent change score model outperformed all six of the linear mixed models and all seven of the latent common factor models with respect to (1) estimation precision of the average learner improvement, (2) correctly rejecting a false null hypothesis about such average improvement, and (3) correctly failing to reject true null hypothesis about between-learner differences (i.e., random slopes) in average improvement.

Implications

The latent change score model is a more flexible method of modeling time in self-regulated learning research, particularly for learner processes consistent with twenty-first-century workplaces. Consequently, defaulting to linear mixed or latent common factor modeling methods may have adverse inferential consequences for better understanding self-regulated learning in twenty-first-century work.

Originality/Value

Ours is the first study to critically, rigorously, and empirically evaluate self-regulated learning modeling methods and to provide a more flexible alternative consistent with modern self-regulated learning knowledge.

     

Bayesian Model Averaging for Propensity Score Analysis

This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averaging approach via Markov chain Monte Carlo sampling (MCMC) to account for uncertainty in both parameters and models. A detailed study of our approach examines the differences in the causal estimate when incorporating noninformative versus informative priors in the model averaging stage. We examine these approaches under common methods of propensity score implementation. In addition, we evaluate the impact of changing the size of Occam’s window used to narrow down the range of possible models. We also assess the predictive performance of both Bayesian model averaging propensity score approaches and compare it with the case without Bayesian model averaging. Overall, results show that both Bayesian model averaging propensity score approaches recover the treatment effect estimates well and generally provide larger uncertainty estimates, as expected. Both Bayesian model averaging approaches offer slightly better prediction of the propensity score compared with the Bayesian approach with a single propensity score equation. Covariate balance checks for the case study show that both Bayesian model averaging approaches offer good balance. The fully Bayesian model averaging approach also provides posterior probability intervals of the balance indices.     

Nonlinear Regime-Switching State-Space (RSSS) Models

Nonlinear dynamic factor analysis models extend standard linear dynamic factor analysis models by allowing time series processes to be nonlinear at the latent level (e.g., involving interaction between two latent processes). In practice, it is often of interest to identify the phases—namely, latent “regimes” or classes—during which a system is characterized by distinctly different dynamics. We propose a new class of models, termed nonlinear regime-switching state-space (RSSS) models, which subsumes regime-switching nonlinear dynamic factor analysis models as a special case. In nonlinear RSSS models, the change processes within regimes, represented using a state-space model, are allowed to be nonlinear. An estimation procedure obtained by combining the extended Kalman filter and the Kim filter is proposed as a way to estimate nonlinear RSSS models. We illustrate the utility of nonlinear RSSS models by fitting a nonlinear dynamic factor analysis model with regime-specific cross-regression parameters to a set of experience sampling affect data. The parallels between nonlinear RSSS models and other well-known discrete change models in the literature are discussed briefly.     

双重攻击模型的实证研究

本研究通过两个实验对双重攻击模型进行了验证.实验一借助实验性分离的逻辑,发现性别对内隐攻击性和外显攻击性存在不同影响,初步证明内隐攻击性和外显攻击性的分离论.实验二通过实证性因素分析,证明双重攻击模型具有较高的拟合参数.本研究证明内隐攻击性和外显攻击性分属两个不同的结构,构成双重攻击模型.     

一个基于综合印象评分法的作文分事后调整模型

对大学英语四、六级考试作文评分进行了详细的描述,重点介绍了作文分的事后调整的原理和方法,并且给出了一个基于线性等值原理的数学推导。认为其数学模型主要是一个运用了“极大似然估计法”和“正态分”概念的统计模型。“评分标准的制定”、“用参照样卷来校准阅卷员对作文评分标准的掌握尺度”以及“阅卷员的培训和考核”构成了大学英语四、六级考试作文信度的基石;而作文分调整中基于“随机分发作文卷”、“客观题分数与作文分相关”以及“评分前后一致性”的统计方法则对出现系统性误差的阅卷员的评分结果进行了事后的校正。还提出如果拥有往次考试总体作文分均值数据,使用“加权移动平均法”可以实现对作文分的跨考次平衡。     

双重集权:财政变迁中的中央和地方关系

推行国家治理体系和治理能力的现代化是中国新时期的重要任务,其中一个关键问题就是要把握好中央和地方政府之间的权力关系。基于对不同历史时期的考察,有的学者认为中央和地方呈现此消彼长、相互竞争的状态,而有的学者认为两者间是向下授权、相互分工的关系。那么,经过新中国成立后70年的演化,中央和地方政府间关系目前呈现怎样的状态?本文以财政改革为考察对象,认为中央和地方未必是对同一种权力或资源的争夺或者分享,它们可在不同维度各自集权:从对一般性公共预算收入的支配来看,中央政府变得更加集权,中央部门对下级政府的财力干预变强;从地方收入角度来看,地方财政部门日益崛起,地方政府能够统揽和支配原先分散于各部门的预算外资金。换句话说,目前中央和地方呈现出双重集权的状态,这产生了多重的治理效应。     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.     

Support for a Modified Tripartite Dual Pathway Model of Body Image Concerns and Risky Body Change Behaviors in French Young Men

The aim of the present study was to extend the extant literature by testing a modified Tripartite Dual Pathway Model of the development of male body image and eating concerns among French young men. A sample of 147 French male college students (M _age = 22.09 years-old, range = 18–30) completed a questionnaire assessing sociocultural influences, internalization of the lean/low body fat ideal and the muscular/athletic ideal, appearance comparison, body fat and muscularity dissatisfaction, muscularity enhancement behaviors, drive for thinness, and bulimic symptoms. The revised and final model was an adequate fit to the data, and included separate pathways for muscularity- and leanness-related concerns. This model shows that sociocultural pressures perceived from the media, family members, and peers were associated, through appearance comparison and internalization of the lean and muscular ideal, with body image concerns, disordered eating, and muscularity enhancement behaviors. Results reveal a strong and direct relationship between the internalization of the muscular/athletic ideal and muscularity enhancement behaviors. These findings contribute to the refinement of sociocultural models of the development of body image concerns and unhealthy body change behaviors including disordered eating among men, provide additional support for the usefulness of these models, and extend them to non-English speaking Western contexts.     

Bivariate estimates of giftedness

The bivariate normal probability distribution is presented as a method for estimating the prevalence of giftedness. The bivariate model requires the specification of two cutoff values for two selection variables and information regarding the size of the correlation between variables. The use and interpretation of bivariate estimates of the prevalence of giftedness is discussed.     

Sequential Temporal Dependencies in Associations Between Symptoms of Depression and Posttraumatic Stress Disorder: An Application of Bivariate Latent Difference Score Structural Equation Modeling

Depression and posttraumatic stress disorder (PTSD) are highly comorbid conditions that may arise following exposure to psychological trauma. This study examined their temporal sequencing and mutual influence using bivariate latent difference score structural equation modeling. Longitudinal data from 182 emergency room patients revealed level of depression symptom severity to be positively associated with changes in PTSD intrusion, avoidance, and hyperarousal over 3 time intervals, beginning shortly after the traumatic event. Higher scores on depression anticipated increases (or worsening) in PTSD symptom severity. The pattern of influence from PTSD symptom severity to change in depression symptom severity simply followed the general trend toward health and well-being. Results are discussed in terms of the dynamic interplay and associated mechanisms of posttrauma depression and PTSD symptom severity.     

医改与医学模式转变

最近 ,国务院办公厅批转了国务院体改办等八部门共同制定的《关于城镇医药卫生体制改革的指导意见》 ,提出了一系列的卫生改革措施 ,我们认为这个文件较全面地把卫生改革的基本原则 ,主要内容概括在内 ,是一个指导性很强的文件。但其中一个较为重要的内容并没有指出 ,即新的医学模式如何在改革中得到贯彻、体现。我认为 ,医改与医学模式转变之间有着不可分割的联系。医改是医学模式转变的必然要求 ,是落实医学模式转变的契机 ;医改有利于新医学模式的发展 ,应该促进医学模式在理论和实践上的转变。1　医学进步 :医改与医学模式转变殊途同归…     

医学模式的转变与循证医学

生物-心理-社会医学模式是20世纪70年代末提出的医学模式理念,但目前我国仍未完成医学模式的转变.在20世纪最后10年,西方发达国家医学界又提出了循证医学这一概念.这就给我国医学和相关学科工作者,在认识上造成了一些混乱.就二者的关系、医学模式转变困难的客观原因进行讨论.     

An Ecological Process Model of Systems Change

In June 2007 the American Journal of Community Psychology published a special issue focused on theories, methods and interventions for systems change which included calls from the editors and authors for theoretical advancement in this field. We propose a conceptual model of systems change that integrates familiar and fundamental community psychology principles (succession, interdependence, cycling of resources, adaptation) and accentuates a process orientation. To situate our framework we offer a definition of systems change and a brief review of the ecological perspective and principles. The Ecological Process Model of Systems Change is depicted, described and applied to a case example of policy driven systems level change in publicly funded social programs. We conclude by identifying salient implications for thinking and action which flow from the Model.