共查询到20条相似文献，搜索用时 15 毫秒
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Personality psychologists are increasingly documenting dynamic, within-person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality-relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text-based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human-annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self-reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model-predicted emotion and self-reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology's evolution into a dynamic science. © 2020 European Association of Personality Psychology 相似文献
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This conceptual paper examines the promises and critical challenges posed by contemporary personality measurement using big data. More specifically, the paper provides (i) an introduction to the type of technologies that give rise to big data, (ii) an overview of how big data is used in personality research and how it might be used in the future, (iii) a framework for approaching big data in personality science, (iv) an exploration of ideas that connect psychometric reliability and validity, as well as principles of fairness and privacy, to measures of personality that use big data, (v) a discussion emphasizing the importance of collaboration with other disciplines for personality psychologists seeking to adopt big data methods, and finally, (vi) a list of practical considerations for researchers seeking to move forward with big data personality measurement and research. It is expected that this paper will provide insights, guidance, and inspiration that helps personality researchers navigate the challenges and opportunities posed by using big data methods in personality measurement. © 2020 European Association of Personality Psychology 相似文献
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David P. Ngidi 《Journal of Psychology in Africa》2013,23(1):149-152
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. 相似文献
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Clemens Stachl Florian Pargent Sven Hilbert Gabriella M. Harari Ramona Schoedel Sumer Vaid Samuel D. Gosling Markus Bühner 《欧洲人格杂志》2020,34(5):613-631
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. 相似文献
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Dominik Rüegger Mirjam Stieger Marcia Nißen Mathias Allemand Elgar Fleisch Tobias Kowatsch 《欧洲人格杂志》2020,34(5):687-713
Smartphones promise great potential for personality science to study people's everyday life behaviours. Even though personality psychologists have become increasingly interested in the study of personality states, associations between smartphone data and personality states have not yet been investigated. This study provides a first step towards understanding how smartphones may be used for behavioural assessment of personality states. We explored the relationships between Big Five personality states and data from smartphone sensors and usage logs. On the basis of the existing literature, we first compiled a set of behavioural and situational indicators, which are potentially related to personality states. We then applied them on an experience sampling data set containing 5748 personality state responses that are self-assessments of 30 minutes timeframes and corresponding smartphone data. We used machine learning analyses to investigate the predictability of personality states from the set of indicators. The results showed that only for extraversion, smartphone data (specifically, ambient noise level) were informative beyond what could be predicted based on time and day of the week alone. The results point to continuing challenges in realizing the potential of smartphone data for psychological research. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology 相似文献
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Previous research has long advocated that emotional and behavioral disorders are related to general personality traits, such as the Five Factor Model (FFM). The addition of section III in the latest Diagnostic and Statistical Manual of Mental Disorders (DSM) recommends that extremity in personality traits together with maladaptive interpersonal functioning, such as lack of empathy, are used for identifying psychopathology and particularly personality disorders (PD). The objective of the present study was to measure dispositions for DSM categories based on normal personality continuums, and to conceptualize these with empathy traits. We used a validated FFM-count method based on the five personality factors (neuroticism, extraversion, openness, agreeableness, and conscientiousness), and related these to 4 empathy traits (emphatic concern, perspective-taking, fantasy, and personal distress). The results showed that FFM-based PD scores overall could be conceptualized using only two of the empathy traits, low emphatic concern and high personal distress. Further, specific dispositions for personality disorders were characterized with distinct empathy traits (e.g., histrionic with high fantasy, and paranoid with low perspective-taking). These findings may have both theoretical and practical implications in capturing potential for personality disorders with ease and efficiency. 相似文献
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Oshrit Kaspi‐Baruch 《创造性行为杂志》2019,53(3):325-338
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. 相似文献
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The Big Five is a useful model of attributes now commonly used in cross‐cultural research, but without the support of strong measurement invariance (MI) evidence. The Big Six has been proposed as a cross‐culturally informed update, and the broader Big Two (Social Self‐Regulation and Dynamism) draws on even more cross‐cultural evidence. However, neither has been rigorously tested for cross‐cultural MI. Here a Big Six inventory (36QB6) and measures of the Big Five and Big Two derived from it were tested and refined for cross‐cultural usability in samples from 26 nations, divided into three subsets. Confirmatory factor analysis of the models in the first subset of nations demonstrated fit as strong in translation as typical personality measures achieve in their nation of origin (although poor per standard benchmarks). Items that performed inconsistently across cultures were removed, and alternates considered in a second subset of nations. Fit and invariance were improved for refined 30‐item QB6, 25‐item Big Five and 14‐item Big Two measures in the third subset of nations. For all models, decrease in comparative fit index between MI levels was larger than .01, indicating lack of support for higher levels. Configural and factorial invariance were relatively stronger, compared to scalar and full. Copyright © 2014 European Association of Personality Psychology 相似文献
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旨在考察嫉妒与人格维度及人格因素的关系 ,通过对 2 31名被试者施测Bringle自我报告嫉妒量表 ,Hup ka爱情嫉妒量表 ,White习惯及关系嫉妒量表 ,Buunk嫉妒量表和大 5人格量表 (NEO -PI-R) ,对施测结果进行相关分析和逐步回归分析 ,发现在大 5人格量表中所包含的神经质 ,外向性 ,开放性 ,顺同性和严谨性 5种人格维度中 ,只有神经质维度与嫉妒呈现一致性密切关系 ;外向性维度与嫉妒的一致性关系尚不明确 ;开放性维度、顺同性维度和严谨性维度与嫉妒关系并不密切。在 30种人格因素中 ,除焦虑、自我意识、正性情绪和信任 4种人格因素与嫉妒呈现一致性密切关系外 ,其他人格因素与嫉妒的关系不明显甚至无关。研究结果表明 :在 5种人格维度中 ,神经质人格维度在嫉妒的形成和发展中起主要影响作用 ;在 30种人格因素中 ,焦虑、自我意识、正性情绪和信任 4种人格因素是影响嫉妒心理和嫉妒行为的重要因素 相似文献
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女性人格特征的服装服饰刻板印象研究 总被引：1，自引：0，他引：1
采用服装服饰偏好语义区分量表对与女性人格特征相关联的服装服饰刻板印象进行了研究。实验由１０７名男女大学生,针对给定的刺激人物的四种不同人格特征,在服装服饰量表上作出印象判断。结果证实,存在与不同人格特点相关联的服装服饰刻板印象,并揭示出在四种人格特征的服装服饰刻板印象中,所存在的具体的服饰符号特征。实验还发现,服装服饰刻板印象中存在着性别差异。作为知觉者的女性对女性刺激人物的印象较作为知觉者的男性表现出极端化的特点。 相似文献
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西方心理学者对笔相学家的笔迹分析提出质疑,研究表明需要我们重新考虑笔迹分析的适用性,而其利用因素分析技术对笔迹与人格关系的大量研究发现,笔迹特征与EPQ中的E因素、N因素相关显著。我国古代丰富的笔迹学思想和当代笔相学家的实践经验为我们从心理学角度研究笔迹与人格之间的关系,提供了宝贵的资料,但我们在这一领域运用心理学方法的研究还刚刚起步。目前国内外心理学者在研究取向、笔迹特征选择和研究目的上还存在一定局限,对此加深认识,将会对我们具有重要的借鉴意义。 相似文献
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In the last two decades, a burgeoning literature has begun to clarify the processes underlying personality traits and momentary trait‐relevant behaviour. However, such work has almost exclusively investigated these questions in young adults. During the same period, much has been learned about adult personality trait development but with scant attention to the momentary processes that contribute to development. The current work connects these two topics, testing developmental questions about adult age differences and thus examining how age matters to personality processes. The study examines how four important situation characteristics are experienced in everyday life and how situations covary with Big Five trait‐relevant behaviour (i.e. situation–behaviour contingencies). Two samples were collected (total N = 316), each assessing three age groups: young, middle‐aged, and older adults. Using experience sampling method, participants completed reports four or five times per day across a representative period of daily life. Results suggested age differences in how situations are experienced on average, in the variability around these average situation experiences, and in situation–behaviour contingencies. The results therefore highlight that, across adulthood, age groups experience chronically different situations, differ in how much the situations they experience vary moment to moment, and differ in how much situation experience predicts their enactment of traits. © 2019 European Association of Personality Psychology 相似文献
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目的:探讨医科大学生优秀学业表现的特征人格以及学业成绩性别差异的人格心理学原因。方法:以卡特尔16PF个性测验为测试工具,应用Spearman相关系数对医科大学生人格特质与学业成绩综合得分进行相关分析。结果:医科大学男女生的开放性分别为5.80±0.89和4.47±1.41,差别有统计学显著性(p0.001)。大学生学业成绩的高低和大学生的自律性和稳定性的相关系数分别为0.271和0.263。结论:医科男女大学生学业成绩差异主要与其开放性大小有关,大学生在校期间学业成绩分值的高低与人格特质中的自律性和稳定性大小成正比,提示加强大学生自律性和稳定性的训练有助于塑造大学生良好的人格特质。 相似文献
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为了分析高血压患者在生活满意度以及人格特质方面与健康人群的异同,选取天津市某综合医院高血压患者123例作为研究组,选取同期健康人群130例作为对照组进行调查研究。结果显示,高血压患者生活满意度显著低于对照组,在神经质和掩饰量表得分显著高于对照组;高血压患者的神经质倾向与生活满意度A和B呈显著负相关;神经质低分组的高血压... 相似文献
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Does the big five predict learning approaches? 总被引：2，自引：0，他引：2
The present study examines if the big five personality traits can statistically predict learning approaches. Four hundred and twenty (286 female and 134 male) university students from Shanghai, PR China volunteered to participate in the study. The participants responded to the NEO Five-Factor Inventory and the Study Process Questionnaire. A cross-examination of the results from zero-order correlation, t-tests, multivariate analysis, and multiple-regression procedures indicated that the big five personality traits predict learning approaches to a certain degree. In this prediction, the conscientiousness and openness traits contributed the most in accounting for the differences in students' learning approaches. Conscientiousness is a good predictor for both the deep and the achieving approaches. Openness significantly predicted the deep approach to learning. Neuroticism is a good predictor for the surface approach to learning, whereas the agreeableness trait clearly predicted a learning approach that is not achieving. Finally, no distinct pattern was identified regarding the relationship of extraversion to any of the learning approaches. 相似文献
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The aim of the present study was to test the cross-cultural validity of the five-factor nature of personality. For this aim, an indigenous, psychometrically strong instrument measuring the basic personality dimensions within Turkish culture and language was developed through three consecutive studies. The first study aimed to reveal the adjectives that have been most frequently used to define people in the Turkish culture. In the second study, factor analysis of these personality characteristics revealed big five personality factors, along with the sixth factor, which had been called as the Negative Valence factor. The adjectives that most strongly represented and differentiated each factor constituted 45-item “Basic Personality Traits Inventory”. Finally, in the third study, psychometric characteristics of the Basic Personality Traits Inventory were examined. Factor structure and psychometric properties of this instrument confirmed that five-factor nature of personality may not hold true in every culture. 相似文献