The Personality Panorama: Conceptualizing Personality Through Big Behavioural Data

Personality psychology has long been grounded in data typologies, particularly in the delineation of behavioural, life outcome, informant-report, and self-report sources of data from one another. Such data typologies are becoming obsolete in the face of new methods, technologies, and data philosophies. In this article, we discuss personality psychology's historical thinking about data, modern data theory's place in personality psychology, and several qualities of big data that urge a rethinking of personality itself. We call for a move away from self-report questionnaires and a reprioritization of the study of behaviour within personality science. With big data and behavioural assessment, we have the potential to witness the confluence of situated, seamlessly interacting psychological processes, forming an inclusive, dynamic, multiangle view of personality. However, big behavioural data come hand in hand with important ethical considerations, and our emerging ability to create a ‘personality panopticon’ requires careful and thoughtful navigation. For our research to improve and thrive in partnership with new technologies, we must not only wield our new tools thoughtfully, but humanely. Through discourse and collaboration with other disciplines and the general public, we can foster mutual growth and ensure that humanity's burgeoning technological capabilities serve, rather than control, the public interest. © 2020 European Association of Personality Psychology     

Personality Research and Assessment in the Era of Machine Learning

The increasing availability of high-dimensional, fine-grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.     

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology     

个体化医疗和大数据时代的机遇和挑战

随着分子生物学的发展,高通量测序技术的引入和“大数据”处理能力的提高,现代医学正在经历巨大的变革,由传统的标准化医疗模式向个体化医疗模式转变。随着人类基因组计划和DNA元素百科全书计划的完成及人类和肿瘤基因图谱的绘制,我们正逐步解开人类基因组的奥秘。这极大地推进了人们对疾病,尤其是对肿瘤的认识。作为疾病诊断的“金标准”,病理学也在经历着深刻的变革,逐渐向个体化病理学发展。个体化医疗时代的到来,使医学各领域和社会多方面都面临新的机遇和挑战。     

Factor Retention Using Machine Learning With Ordinal Data

Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2–6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.     

Big Five Personality and Creativity: The Moderating Effect of Motivational Goal Orientation

This study examined the association between the big five personality dimensions and creativity through the moderation of motivational goal orientation. One hundred and ninety students engaged in full‐time employment completed questionnaires, which were used to assess the variables of interest. Regression moderation analyses supported some of the expected hypotheses. The associations between the big five dimensions and creativity were moderated by learning motivational goal orientation. Individuals high in extroversion, emotional stability, and low in conscientiousness, are most creative when they are oriented toward learning. In addition, openness fully predicted creativity, without the moderation of goal orientation. The results are discussed in terms of the interactional nature of personality and goal orientation theory.     

Bringing Back the Person into Behavioural Personality Science Using Big Data

Behaviour and the individual person are important but widely neglected topics of personality psychology. We argue that new technologies to collect and new methods to analyse Big (Behavioural) Data have the potential to bring back both more behaviour and the individual person into personality science. The call for studying the individual person in the history of personality science, the related idiographic/nomothetic divide, as well as attempts to reconcile these two approaches are briefly reviewed. Furthermore, different meanings of the term idiographic and some unique selling points that emphasize the importance of idiographic research are highlighted. A nonexhaustive literature review shows that a wealth of behaviours are considered in extant personality studies using such Big Data but only in a nomothetic way. Against this background, we demonstrate the potential of Big Data collection and analysis with regard to four idiographic research topics: (i) unique manifestations of common traits and the resurgence of personal dispositions, (ii) idiographic prediction, (iii) intraindividual consistency versus variability of behaviour and (iv) intraindividual personality trait change through intervention. Methodological, ethical and legal pitfalls of doing Big Data research with individual persons as well as potential countermeasures are considered.     

“大五”与五因素模型：两种不同的人格结构

随着词汇学取向的“大五”结构和理论取向的五因素模型的出现,人格研究者就人格分类系统的问题达成了初步的共识。“大五”结构和五因素模型在形式和内容上有很多相似之处,但二者在历史渊源、内容形式、基本性质、研究走向等方面都存在一定的差异。文章试图从以上方面明确二者的差异,以澄清相关的混淆和误解,并在文章结语部分对两种取向研究对中国人人格结构研究的启示进行了详细的分析     

青少年人格、人口学变量与主观幸福感的关系模型

采用分层随机抽样的方法,抽取了379名中学生和大学生样本,在多元相关分析的基础上,结合文献分析,采用结构方程建模技术,探讨了我国青少年学生人口学变量、人格维度和主观幸福感(SWB)的结构关系。结果表明,人口学变量中的年级和经济状况变量既与SWB有直接的关系,又通过大五人格中的神经质维度或外倾性维度对SWB有间接的效应,其中,经济状况对主观幸福感的正面效应较大,而性别和城乡变量则仅通过神经质和外倾性对SWB有微弱的间接效应;大五人格维度中,宜人性与SWB没有显著关系,开放性和严谨性通过神经质或外倾性与SWB存在间接效应,神经质和外倾性则对SWB有较强的直接预测力;结构方程模型验证了人格、人口学变量与SWB的这种关系。     

道家人格的测量

以对道家人格的界定及道家人格结构理论模型为基础,从道家经典著作中收集到486个描述人的词汇,经由意义分析和初测保留了45个项目,选取了中国人整体思维方式量表作为对道家认知思维方式的测量。道家人格量表在四个不同年龄样本中的探索及验证性因素分析均支持了一阶十维、二阶＂真＂、＂伪＂的二维结构。道家人格量表内部一致性系数和重测信度系数分别介于0.63～0.88之间和0.66～0.89之间。各题项与其所属维度间及各维度间的相关分别介于0.48～0.89之间和-0.11～0.53之间。未来研究需要进一步探究道家人格各维度的丰富内涵并编制具有针对性的道家认知思维方式量表。     

Students' Personality Traits and Learning Approaches

This study examined students' usage of deep and surface approaches to learning as well as the relationship between students' personality attributes and their approaches to learning. Participants were 284 undergraduate education students registered with a South African regular university (females = 195, males = 89). They completed the Revised two-factor Study Process Questionnaire (R-SPQ-2F) (Biggs, Kember, & Leung (2001) and Eysenck Personality Questionnaire (EPQ) (Eysenck & Eysenck, 1975, 1985). Data were analysed for differences in proportion endorsing learning approach type (deep, surface) and the association between learning approach and personality attributes. The students self-reported to use predominantly deep learning strategies and lower rather than high surface strategies. The personality attributes of Extraversion and Neuroticism were unrelated to self-reported primary learning strategy.     

应征公民心理选拔的人格评估

应征公民人格评估的目的是对精神分裂症病前人格特征进行预测性评价。采取定性与定量两种研究方法编制征兵专用人格问卷,并对其进行指标验证。结果发现：①量表应答得分区间可较好地区分正常新兵与精神分裂症被试;②二阶因子分析将人格分量表部分划分为3个维度8个因子;③精神分裂症患者人格分量表分数各指标均显著高于正常新兵;④总预测符合率和预测合格符合率均在98%以上,预测淘汰符合率最低为70.13%。上述结果表明,本研究编制的中国士兵人格问卷（CSPQ）具有良好的信度和效度,适用于我国应征公民心理检测以及我军士兵的人格测试     

Personality and intimate partner aggression in dating relationships: the role of the "Big Five"

Although personality is shown to predict negative relationship experiences, few researchers have used a structural model of personality to study the ways that personality contributes to intimate partner aggression (IPA). This study investigates the five-factor model of personality and its associations with both the use and receipt of psychological, physical, and sexual IPA in 179 men and 301 women. Each of the five factors of personality was associated with at least one type of IPA perpetration or victimization. The dimensions of neuroticism and agreeableness were the strongest predictors of IPA particularly for women. Results are discussed in terms of why personality should be considered as a predictor for both the use and receipt of IPA, why sex differences emerged, and future research that should be conducted.     

大五人格5个维度及10个面的发展水平——基于我国15～75岁横断样本调查

基于人格毕生发展理论及中国社会文化背景,调查了我国从青少年到老年3192名被试,探究了中国人大五人格5维度及10个面毕生发展水平。总体上,年龄与神经质、焦虑、抑郁、活跃、开放性、审美、创意显著负相关,与外倾性、宜人性、尽责性、自信、利他、顺从、条理和自律显著正相关。在60岁以下的人群中,年龄大的个体神经质更低,而在大于60岁的人群中,年龄大的个体神经质反而更高;在50岁以下的人群中,年龄大的个体外倾性水平相对较高,但50岁之后年龄大的个体外倾性相对较低;整体上,年龄大的个体开放性水平相对较低,而年龄大的个体宜人性水平反而更高;年龄大的个体尽责性水平也相对较高,但较之40到49岁群体而言,50岁以上群体的尽责性则相对较低。男性和女性不同年龄群体的大五人格具有一定差异性,特别是男性的尽责性高于女性,以及女性的神经质高于男性等性别差异。进一步分析了年龄与大五人格10个面的关系,描绘了不同年龄群体10个面的发展水平。     

Compiling Measurement Invariant Short Scales in Cross-Cultural Personality Assessment Using Ant Colony Optimization

Examining the influence of culture on personality and its unbiased assessment is the main subject of cross-cultural personality research. Recent large-scale studies exploring personality differences across cultures share substantial methodological and psychometric shortcomings that render it difficult to differentiate between method and trait variance. One prominent example is the implicit assumption of cross-cultural measurement invariance in personality questionnaires. In the rare instances where measurement invariance across cultures was tested, scalar measurement invariance—which is required for unbiased mean-level comparisons of personality traits—did not hold. In this article, we present an item sampling procedure, ant colony optimization, which can be used to select item sets that satisfy multiple psychometric requirements including model fit, reliability, and measurement invariance. We constructed short scales of the IPIP-NEO-300 for a group of countries that are culturally similar (USA, Australia, Canada, and UK) as well as a group of countries with distinct cultures (USA, India, Singapore, and Sweden). In addition to examining factor mean differences across countries, we provide recommendations for cross-cultural research in general. From a methodological perspective, we demonstrate ant colony optimization's versatility and flexibility as an item sampling procedure to derive measurement invariant scales for cross-cultural research. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

心率变异性与人格的神经质之间关系研究

本研究探讨心率变异性（HRV）特征在高水平应激条件下对神经质水平的区分力。研究选取200名被试以大五人格量表测量情绪稳定性,实施了应激诱发实验,利用光体积扫描传感器采集被试者HRV指标;利用LASSO回归分析筛选参数构建对神经质的预测模型。结果显示：模型的预测数据与神经质得分高低显著正相关;不同阶段内预测数据与神经质得分显著正相关;两两比较中实验条件阶段与其他阶段预测值差异显著。研究表明,在高应激条件下,HRV是人格神经质较好的预测指标。     

Modeling fMRI Data: Challenges and Opportunities

We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data—the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.     

中学生学习倦怠与人格关系

采用问卷调查法,对1136名高中学生的学习倦怠进行分析,考察了人口学变量之间的差异,并探讨了学习倦怠各因子与人格之间的关系。结果发现:⑴影响学生学习倦怠的人口学变量有家庭所在地、性别、年级和学校类型。其中家庭所在地单独影响学习倦怠,且是学习倦怠的有效预测变量。性别、学校类型和年级相互作用影响学生的学习倦怠。⑵精神质、内外向、神经质与学习倦怠的各个因子及总分有极其显著的相关,其中N、P是学习倦怠有效的预测变量。     

Loneliness and the Big Five Personality Traits: A Meta-analysis

This preregistered meta-analysis (k = 113, total n = 93 668) addressed how the Big Five dimensions of personality (extraversion, agreeableness, conscientiousness, neuroticism, and openness) are related to loneliness. Robust variance estimation accounting for the dependency of effect sizes was used to compute meta-analytic bivariate correlations between loneliness and personality. Extraversion (r = −.370), agreeableness (r = −.243), conscientiousness (r = −.202), and openness (r = −.107) were negatively related to loneliness. Neuroticism (r = .358) was positively related to loneliness. These associations differed meaningfully in strength depending on how loneliness was assessed. Additionally, meta-analytic structural equation modelling was used to investigate the unique association between each personality trait and loneliness while controlling for the other four personality traits. All personality traits except openness remained statistically significantly associated with loneliness when controlling for the other personality traits. Our results show the importance of stable personality factors in explaining individual differences in loneliness. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology.