The essential process in a family of measurement models

Five members of the Rasch family of latent trait models which have appeared more or less independently in the literature are brought together and identified as one model. In addition to sharing the distinguishing characteristic of the dichotomous Rasch model—separable person and item parameters and hence sufficient statistics—all five models share a common algebraic form and have as their basic element the fundamental process defined by Rasch's simple logistic expression. In these models, the sufficient statistics for person and item parameters are counts of events constructed to be indicative of the variable being measured, and the measures they enable are ‘fundamental’.     

USE OF SOCIAL-SKILLS TRAINING IN THE TREATMENT OF EXTREME ANXIETY AND DEFICIENT VERBAL SKILLS IN THE JOB-INTERVIEW SETTING

A 30-year-old recent college graduate, exhibiting extreme anxiety and deficient verbal skills in job interviews, was treated with a social-skills training procedure that included instructions, modelling, behavior rehearsal, and videotape feedback. Three target behaviors—focused responses, overt coping statements, and subject-generated questions—were presented using a multiple-baseline design. Galvanic skin-response activity was monitored during pre- and posttraining in vivo job interviews. In addition, independent judges unobstrusively rated the subject's social-communicative behaviors in his temporary work-setting before and after training. Training resulted in expected changes for all three target behaviors and a decrease in the rate of speech disturbances. Physiological data supported the subject's report that training enabled him to deal with his anxiety more effectively during job interviews. Training was found to generalize to novel interview questions and different interviewers. Furthermore, unobtrusive measures of eye contact, fluency of speech, appropriateness of verbal content, and composure supported the subject's report that training generalized to his daily social interactions on the job.     

A note on parameter estimation for Lazarsfeld's latent class analysis

As the literature indicates, no method is presently available which takes explicitly into account that the parameters of Lazarsfeld's latent class analysis are defined as probabilities and are therefore restricted to the interval [0, 1]. In the present paper an appropriate transform on the parameters is performed in order to satisfy this constraint, and the estimation of the transformed parameters according to the maximum likelihood principle is outlined. In the sequel, a numerical example is given for which the basis solution and the usual maximum likelihood method failed. The different results are compared and the advantages of the proposed method discussed.     

Psychometric latent response models

In this paper, some psychometric models will be presented that belong to the larger class oflatent response models (LRMs). First, LRMs are introduced by means of an application in the field ofcomponential item response theory (Embretson, 1980, 1984). Second, a general definition of LRMs (not specific for the psychometric subclass) is given. Third, some more psychometric LRMs, and examples of how they can be applied, are presented. Fourth, a method for obtaining maximum likelihood (ML) and some maximum a posteriori (MAP) estimates of the parameters of LRMs is presented. This method is then applied to theconjunctive Rasch model. Fifth and last, an application of the conjunctive Rasch model is presented. This model was applied to responses to typical verbal ability items (open synonym items).This paper presents theoretical and empirical results of a research project supported by the Research Council [Onderzoeksraad] of the University of Leuven (grant number 89-9) to Paul De Boeck and Luc Delbeke.     

Relating latent class membership to external variables: An overview

In this article we provide an overview of existing approaches for relating latent class membership to external variables of interest. We extend on the work of Nylund-Gibson et al. (Structural Equation Modeling: A Multidisciplinary Journal, 2019, 26, 967), who summarize models with distal outcomes by providing an overview of most recommended modeling options for models with covariates and larger models with multiple latent variables as well. We exemplify the modeling approaches using data from the General Social Survey for a model with a distal outcome where underlying model assumptions are violated, and a model with multiple latent variables. We discuss software availability and provide example syntax for the real data examples in Latent GOLD.     

Latent Profiles of Comorbid Depression as Predictors of PTSD Treatment Outcome

Posttraumatic stress disorder (PTSD) frequently co-occurs with major depressive disorder, and empirically supported PTSD treatments consistently improve depression. However, both diagnoses are heterogeneous and specific patterns of symptom overlap may be related to worse treatment outcome. Two hundred individuals with chronic PTSD participated in a doubly randomized preference trial comparing prolonged exposure and sertraline. Latent Profile Analysis was used to identify classes based on PTSD and depression symptoms prior to starting treatment. A three-class model best fit the data, with a high depression and PTSD severity class (distressed), a moderate depression and low PTSD avoidance class (depressive), and a low depression and high PTSD avoidance class (avoidant). The avoidant class showed the lowest rates of major depressive disorder diagnosis and transdiagnostic vulnerabilities to depression. Patients in the distressed class experienced more robust PTSD treatment response, with no differences between prolonged exposure and sertraline. These findings highlight the role of avoidance in nondepressed PTSD presentations while also demonstrating that co-occurring depression is not contraindicated in evidence-based PTSD treatment.     

共情的双刃剑效应：COVID-19心理热线咨询师的继发性创伤应激和替代性创伤后成长

关注新型冠状病毒肺炎流行期间的心理援助热线咨询师由共情带来的消极和积极影响及机制, 并探究从消极影响转变为积极成长的内在机制具有重要的理论和实践意义。本研究以国内疫情期间, 776名在教育部华中师范大学心理援助热线平台上工作的咨询师(平均年龄42.57岁, 平均咨询经验12.44年)为研究对象, 使用问卷法测量热线心理咨询师的共情、正念、寻求意义、继发性创伤应激(Secondary Traumatic Stress, STS)和替代性创伤后成长(Vicarious Posttraumatic Growth, VPTG)。采用潜变量结构方程模型进行分析, 结果发现：(1)咨询师的共情正向预测STS和VPTG; (2) 正念在共情和STS间起部分中介作用, 寻求意义在共情和VPTG间起部分中介作用; (3) 共情还可以分别通过STS、STS-寻求意义、正念-STS、正念-STS-寻求意义的中介路径对VPTG起正向预测作用。该结果提示要辩证看待共情带来的消极和积极影响, 关注从消极反应到积极成长的转变, 这不仅能够深化共情及其影响的理论, 还能为相关研究应用提供启示。     

随机截距潜在转变分析(RI-LTA)——个案自我转变与个案间差异的分离

传统的潜在转变分析属于单水平分析, 但其同样也可以看作二水平分析。Muthén和Asparouhov就以二水平分析的视角在单水平分析框架内提出了随机截距潜在转变分析(RI-LTA), 其中跨时间点产生的自我转变可以看作在水平1进行分析, 跨时间点不变的个案间差异可以看作在水平2进行分析, 使个案的自我转变和个案间的初始差异分离, 避免了高估保留在初始类别的概率。某研究型大学2016级本科生的追踪调查数据被用于演示使用随机截距潜在转变分析的过程。该方法的最大优势是通过引入随机截距避免了高估保留在本类别的转变概率。未来研究可以运用蒙特卡洛模拟研究探究随机截距潜在转变分析模型的适用性, 也可以用多水平分析的思路为灵感, 探究多水平随机截距潜在转变分析在统计软件中的实现。