家庭因素对大学生心理健康的影响研究

本研究以问卷调查的方式试图对父母婚姻状况、家庭子女数、父母的文化及工作单位等家庭因素对大学生心理健康状况的影响进行量化的研究.通过对北京某大学6165份有效数据的分析,结果表明家庭的多种因素对大学生的心理健康产生了显著的影响.     

自我控制的内隐效应研究

自我控制对个体获得成功和幸福至关重要.自我控制是一个多因素相互作用的系统,本研究试图探究自我效能信念和自我控制行为之间是否存在内隐效应.研究采用Greenwald 的内隐联结测验(IAT)方法和Inquisit专业软件,设计一个中国汉语的自我控制信念和行为的IAT,以25名大学生为被试.结果为自我效能信念和自我控制行为之间的IAT效应值大于0,p<0.000, 具有统计学显著意义.结论是自我控制存在内隐效应,自我效能信念和自我控制行为之间有内隐层面的相互作用.本研究对自我控制机制的研究和提高自我控制能力水平的教育训练实践具有参考价值.     

试题难度的事前认知任务分析

试题的难度常常用试题的通过率来表示,这种事后检验的方法虽然简单易行,其数量指标严格确定,但是对考生水平相当敏感,也不能在试题设计中起到主导作用.本研究根据认知任务分析的基本原则,设计了对试题难度进行事前评定的框架和严格的实施程序,并以高等数学试题为分析对象进行了检验.根据数学试题的特点,设定了要素数量、要素辨识难度、原理数量、原理辨识难度和认知负荷等指标.结果显示,各试题事前难度值与其通过率之间存在极其显著的相关.评定指标之间、评定者之间也存在极其显著的一致性,说明本研究的事前评定方法具有充分的效度和信度.     

基于MJT的道德结构验证性分析研究

从科尔伯格提出道德认知发展概念至今,已经出现对道德认知发展的几种不同解释,其中较为著名的包括阶段论、水平论和认知图式论.本研究通过对一份道德判断测验的数据进行分析,采用建立模型的实证方法,试图验证已有的几种道德认知结构理论的正确性.研究表明:道德发展过程的确包含六个阶段,它们是按照一定顺序发展的.同时,道德发展过程中还出现三个水平,并先后建立了三种不同的认知图式.     

Neural and genetic foundations of face recognition and prosopagnosia

Faces are of essential importance for human social life. They provide valuable information about the identity, expression, gaze, health, and age of a person. Recent face‐processing models assume highly interconnected neural structures between different temporal, occipital, and frontal brain areas with several feedback loops. A selective deficit in the visual learning and recognition of faces is known as prosopagnosia, which can be found both in acquired and congenital form. Recently, a hereditary sub‐type of congenital prosopagnosia with a very high prevalence rate of 2.5% has been identified. Recent research results show that hereditary prosopagnosia is a clearly circumscribed face‐processing deficit with a characteristic set of clinical symptoms. Comparing face processing of people of prosopagnosia with that of controls can help to develop a more conclusive and integrated model of face processing. Here, we provide a summary of the current state of face processing research. We also describe the different types of prosopagnosia and present the set of typical symptoms found in the hereditary type. Finally, we will discuss the implications for future face recognition research.     

Action Research: A Missing Component in the Emergence of Social and Cross‐Cultural Psychology as a Fully Inter‐Connected Global Enterprise

Action research, as envisaged by Lewin, uses research to solve a social problem and provide theoretical knowledge. It involves a cyclical–spiral of planning, action, and evaluation with feedback between parties, taking into account power and value differences and empowering change agents. This has been all but abandoned by social psychologists today. Contemporary ‘participatory’ action research has migrated to fields like education and development and community studies that emphasize a social constructionist epistemology with a qualitative approach of recording bottom‐up processes of social change and community self‐awareness. The globalization of psychology affords new opportunities for a return to more top‐down Lewinian traditions, where academics in the developing world – embedded within a cultural milieu of interconnectedness and relatively exempt from pressures to publish or perish – can work together with academics in wealthy countries to produce a process oriented psychology that is capable of making a difference under conditions of climate change and the end of cheap oil. Asian social psychology, where strongly indigenous psychologies have taken root in growing economies, may take a leading role in this movement.     

Long‐term immigrant adaptation: Eight‐year follow‐up study among immigrants from Russia and Estonia living in Finland

This study was a longitudinal investigation of the three different dimensions of long‐term immigrant adaptation (i.e., psychological, sociocultural, and socioeconomic adaptation) and the relationships between them in an 8‐year follow‐up with panel data. The 282 respondents were immigrants in Finland, born between 1961 and 1976, coming from the former Soviet Union. The results suggest that the adaptation of these immigrants has developed favourably. In 8 years, the respondents had improved their Finnish language skills and their position in the labour market. No differences were observed in their levels of psychological well‐being between the two assessments. Of the three adaptation dimensions assessed, sociocultural adaptation, measured as proficiency in understanding, speaking, reading, and writing Finnish, turned out to be the most significant predictor of the two other long‐term outcomes of immigrant adaptation (i.e., socioeconomic and psychological). In particular, the better the initial command of the Finnish language, the better were their socioeconomic and psychological adaptation outcomes after 8 years of residence. These results demonstrate the importance of parallel and longitudinal assessments of the different outcomes of immigrant adaptation in order to address which particular dimensions of adaptation are most critical in the beginning of acculturation in terms of determining positive development and long‐term immigrant adaptation.     

Influence analysis for the factor analysis model with ranking data

Influence analysis is an important component of data analysis, and the local influence approach has been widely applied to many statistical models to identify influential observations and assess minor model perturbations since the pioneering work of Cook (1986) . The approach is often adopted to develop influence analysis procedures for factor analysis models with ranking data. However, as this well‐known approach is based on the observed data likelihood, which involves multidimensional integrals, directly applying it to develop influence analysis procedures for the factor analysis models with ranking data is difficult. To address this difficulty, a Monte Carlo expectation and maximization algorithm (MCEM) is used to obtain the maximum‐likelihood estimate of the model parameters, and measures for influence analysis on the basis of the conditional expectation of the complete data log likelihood at the E‐step of the MCEM algorithm are then obtained. Very little additional computation is needed to compute the influence measures, because it is possible to make use of the by‐products of the estimation procedure. Influence measures that are based on several typical perturbation schemes are discussed in detail, and the proposed method is illustrated with two real examples and an artificial example.