Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data

Psychometrika - Process data, which are temporally ordered sequences of categorical observations, are of recent interest due to its increasing abundance and the desire to extract useful...     

Collective and Individual Self-Esteem Mediate the Effect of Self-Construals on Subjective Well-Being of Undergraduate Students in China

Human psychology and behavior are influenced by culture. Self-construals reflect the individualism-collectivism dimension at the level of individual personality. The current study aimed to explore how self-construals affect subjective well-being (SWB) in China, which has a collectivist culture. Chinese undergraduates (N?=?442) participated in this study. They responded to the self-construal scale, Rosenberg self-esteem scale, collective self-esteem scale and measures of SWB. The results suggested that the type of self-construal significantly predicted SWB. Moreover, an individual’s self-esteem completely mediated the impact of independent self-construal on SWB, whereas interdependent self-construal influenced SWB directly, as well as indirectly though collective self-esteem. In addition, collective self-esteem promoted individual self-esteem, which in turn further stimulated SWB. These findings extend prior reports and shed light on how individual differences in self-construal affect SWB.     

主动控制感在大脑表征中的时空标记

主动控制感是主动动作过程中产生的控制自身动作, 进而控制外部环境的主观体验。构成动作主动控制感的核心要素是主观意图与结果反馈。本研究试图通过操控这两个核心要素的不同属性, 借助脑磁图等技术, 探寻主动控制感在大脑额-顶为主的脑网络中前-后馈的作用方式及时空特异性标记, 并建构新的认知神经理论模型。这将有利于理解人类动作的产生及后效、为相关精神类疾病的临床诊断提供更加客观的参照标准。     

On the Identifiability of Diagnostic Classification Models

Psychometrika - This paper establishes fundamental results for statistical analysis based on diagnostic classification models (DCMs). The results are developed at a high level of generality and are...     

A latent topic model with Markov transition for process data

We propose a latent topic model with a Markov transition for process data, which consists of time-stamped events recorded in a log file. Such data are becoming more widely available in computer-based educational assessment with complex problem-solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward-backward variational expectation-maximization (FB-VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem-solving item in the 2012 version of the Programme for International Student Assessment (PISA).