潜变量建模的贝叶斯方法

贝叶斯统计是统计学的两大流派之一,近年来贝叶斯统计在社会及行为科学领域日益流行。鉴于国内心理学界对贝叶斯统计应用仍不广泛,本文尝试从非技术性的角度对贝叶斯统计用于潜变量建模的过程进行简要介绍。主要涉及贝叶斯与频率论在统计学基本概念上的对比;贝叶斯统计的基本原理和分析过程。最后以一个验证性因子分析为例,简要介绍贝叶斯统计用于潜变量建模的分析过程。希望本文能为国内心理学者进行潜变量建模提供新的视角。     

Identifying the Career-Interest Profiles of Junior-High-School Students Through Latent Profile Analysis

Exploring the career-interest profiles of students has both practical and theoretical significance; however, a minimal amount of research has been conducted to address this issue. The present study combined latent profile analysis and differentiation values to investigate the career-interest profiles of 13,853 junior-high-school students. The results indicate that while the students’ interests could be stratified into 25 profiles, 44.6% of students could be classified the as having low-differentiation profiles (such as like or dislike all types of vocational activities) and 24.9% of students could be classified the as having the artistic-social (like both artistic and social types of activities) profile. In addition, the proportions of females and males differed significantly among the profiles, but different grades did not. The proportion of males was higher for all three low-differentiation groups than of female proportions. Implications for career practices and future research are proposed.     

Evaluation of Student Performance through a Multidimensional Finite Mixture IRT Model

In the Italian academic system, a student can enroll for an exam immediately after the end of the teaching period or can postpone it; in this second case the exam result is missing. We propose an approach for the evaluation of a student performance throughout the course of study, accounting also for nonattempted exams. The approach is based on an item response theory model that includes two discrete latent variables representing student performance and priority in selecting the exams to take. We explicitly account for nonignorable missing observations as the indicators of attempted exams also contribute to measure the performance (within-item multidimensionality). The model also allows for individual covariates in its structural part.     

A Hierarchical Rater Model for Longitudinal Data

Research studies in psychology and education often seek to detect changes or growth in an outcome over a duration of time. This research provides a solution to those interested in estimating latent traits from psychological measures that rely on human raters. Rater effects potentially degrade the quality of scores in constructed response and performance assessments. We develop an extension of the hierarchical rater model (HRM), which yields estimates of latent traits that have been corrected for individual rater bias and variability, for ratings that come from longitudinal designs. The parameterization, called the longitudinal HRM (L-HRM), includes an autoregressive time series process to permit serial dependence between latent traits at adjacent timepoints, as well as a parameter for overall growth. We evaluate and demonstrate the feasibility and performance of the L-HRM using simulation studies. Parameter recovery results reveal predictable amounts and patterns of bias and error for most parameters across conditions. An application to ratings from a study of character strength demonstrates the model. We discuss limitations and future research directions to improve the L-HRM.     

Thank goodness it’s Friday: weekly pattern of workplace incivility

Background and objectives: Recent research has shown day-level differences in an individual’s experience of uncivil behavior; however, it is unknown if that experience follows a consistent weekly change pattern. This study extends incivility theory and research by applying latent growth curve (LGC) modeling to diary study data to understand day-to-day changes in incivility.

Design: The authors took a theory-driven approach, reviewing both mood and recovery theory that would support a decrease in incivility over the working week.

Methods: Diary survey methodology was used, with a morning and evening survey completed on five consecutive workdays by 171 (73% of the 235 who initially volunteered, 95% of those who completed any surveys) employees in the legal industry. LGC analysis was used to identify patterns of experienced incivility, mood (both measured after work), and recovery (assessed the following morning).

Results: Regardless of job demands and gender, a weekly pattern was identified with the likelihood of experiencing incivility (coded as 0?=?none, 1?=?some) decreasing from Monday to Friday by .78 each day (p?<?.001) in a relatively linear fashion with a slope factor of .34 (SE?=?0.23; p?>?.05), indicating invariance between individuals. This weekly pattern was not explained by changes in mood or recovery.

Conclusions: Results emphasize the impact of contextual factors such as time on workplace incivility and the need to consider weekly rhythms of other behaviors that are likely to affect employee well-being and productivity. Although limited to one week of data per person, the findings are likely to be relevant to studies of other forms of interpersonal mistreatment, such as social undermining and interpersonal conflict.     

大学新生主观社会地位与抑郁的变化轨迹:一项多元潜变量增长模型分析

采用整群取样的方法在全国按东部、中部和西部共抽取了7所高校的1983名新生,进行了历时四个月的四次追踪测试。使用潜变量增长模型建模,分别考察了新生主观社会地位和抑郁的变化轨迹,并就两者间的变化关系进行了分析。结果发现新生入学后四个月内：（1）主观社会地位呈阶段化线性增长,其起始水平和第一阶段的增长速度存在显著个体间差异;（2）抑郁呈二次方增长,其起始水平和增长速度存在显著个体间差异;（3）入学后主观社会地位的下滑速度能有效预测新生抑郁水平的上升速度。研究基于情绪压制理论,对主观社会地位与抑郁间的变化关系进行了分析。     

连续性公共物品困境中信息结构对决策行为的影响

选取240名大学二年级的学生,通过局域网信息反馈,进行了模拟投资的小组实验。结果发现,1、在连续性公共物品困境和自协调序列规则下,先前决策者的合作行为在决策的早期和中期起到了明显的榜样示范作用,并导致后续决策者的合作行为增加;2、这种示范作用在决策后期不起作用,被试出于搭便车或者担心自己利益受损的心理会表现出较高的竞争行为;3、先前决策者的竞争行为在整个决策过程中都起到明显的示范作用,使后续决策者的合作率保持在较低的水平;4、在没有任何信息反馈的情况下,被试倾向于在初期就做出投资决策,并表现出一定程度的合作行为。     

Higher-order latent trait models for cognitive diagnosis

Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling theϑ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example. This research was funded by National Institute of Health grant R01 CA81068. We would like to thank William Stout and Sarah Hartz for many useful discussions, three anonymous reviewers for helpful comments and suggestions, and Kikumi Tatsuoka and Curtis Tatsuoka for generously sharing data.     

意识和无意识活动的脑区能态理论

本文在脑的四个功能系统学说的基础上,提出脑区能态理论,研究有意识、无意识和潜意识。讨论有意识、无意识、潜意识的脑机制,指出有意识、无意识、潜意识的统一性。     

Structural Validation of the Hamilton Depression Rating Scale

Despite the prolific use of the Hamilton Depression Rating Scale (HDRS), there have been no prior studies using confirmatory factor analysis (CFA) to compare competing HDRS latent structures. The current study undertakes this task, examining CFAs on 397 older adults on the most stable latent models previously posited and a new rationally derived model by Cole and Motivala. CFAs were evaluated in their original first-order structure and in hierarchically related counterparts. All hierarchical models performed significantly better than their first-order counterparts. Moreover, the hierarchical Cole and Motivala model was significantly better on all comparative criteria than any of the other models. However, none of the tested models obtained sufficient fit with the data suggesting psychometric, scoring, or content limitations of the scale.