Bringing Back the Person into Behavioural Personality Science Using Big Data

2020 ◽  
Vol 34 (5) ◽  
pp. 670-686
Author(s):  
Karl–Heinz Renner ◽  
Stephanie Klee ◽  
Timo von Oertzen
Keyword(s):  
Big Data ◽  
Literature Review ◽  
New Technologies ◽  
Research Topics ◽  
Personality Psychology ◽  
Individual Person ◽  
New Methods ◽  
Data Collection And Analysis ◽  
History Of ◽  
The Individual

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.

scholarly journals The Personality Panorama: Conceptualizing Personality through Big Behavioural Data

2020 ◽  
Vol 34 (5) ◽  
pp. 599-612 ◽  
Author(s):  
Ryan L. Boyd ◽  
Paola Pasca ◽  
Kevin Lanning
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Historical Thinking ◽  
Self Report ◽  
Ethical Considerations ◽  
Personality Psychology ◽  
The Public ◽  
New Methods ◽  
Behavioural Assessment ◽  
The Face

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

ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

2021 ◽  
Vol 13 (2) ◽  
pp. 1-27
Author(s):  
A. Khalemsky ◽  
R. Gelbard
Keyword(s):  
Big Data ◽  
Visual Presentation ◽  
Trend Detection ◽  
Dynamic Visualization ◽  
Segmentation Process ◽  
Key Points ◽  
Wide Range ◽  
History Of ◽  
The Individual ◽  
Over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.

scholarly journals Personality Psychology

Psychology ◽  
2013 ◽  
Author(s):  
Robert D. Latzman ◽  
Yuri Shishido
Keyword(s):  
Social Psychology ◽  
Individual Differences ◽  
Personality Psychology ◽  
Individual Person ◽  
Psychological Science ◽  
Research Programs ◽  
Broad Field ◽  
Diverse Field ◽  
The Individual ◽  
Dynamic Organization

The title of “Godfather of Personality” may well be ascribed to Gordon Allport, who was the first to make public efforts to promote the “field of personality” in the 1930s (see Allport and Vernon 1930, cited under Gordon Allport). Personality psychology—located within what many argue is the broadest, most encompassing branch of psychological science—can be defined as the study of the dynamic organization, within the individual, of psychological systems that create the person’s characteristic patterns of behaviors, thoughts, and feelings (see Allport 1961, also cited under Gordon Allport). The field of personality psychology is concerned with both individual differences—that is, the way in which people differ from one another—and intrapersonal functioning, the set of processes taking place within any individual person. The area of personality psychology is often grouped with social psychology in research programs at universities; however, these are quite different approaches to understanding individuals. While social psychology attempts to understand the individual in interpersonal or group contexts (i.e., “when placed in Situation A, how do people, in general, respond?”), personality psychology investigates individual differences (i.e., “how are people similar and different in how they respond to the same situation?”). Personality psychology has a long history and, as such, is an extremely large and broad field that includes a large number of approaches. Discerning readers will quickly note that the current chapter is largely focused on what has come to be the most commonly studied perspective, the trait approach. Those readers interested in other approaches are referred to a number of resources focusing on Other Approaches within the diverse field.

scholarly journals A Model to Evaluate the Organizational Readiness for Big Data Adoption

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Javaneh Ramezani ◽  
Mahdi Nasrollahi
Keyword(s):  
Principal Component Analysis ◽  
Big Data ◽  
Literature Review ◽  
New Technologies ◽  
Principal Component ◽  
Organizational Readiness ◽  
Decision Makers ◽  
Comprehensive Literature Review ◽  
The Hierarchical Structure ◽  
Best Worst Method

Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption.

Thick Big Data

2020 ◽  
Author(s):  
Dariusz Jemielniak
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Access ◽  
Cultural Analysis ◽  
Additional Advantage ◽  
Corporate World ◽  
New Methods ◽  
Novel Approach ◽  
The Social ◽  
History Of

The social sciences are becoming datafied. The questions that have been considered the domain of sociologists, now are answered by data scientists, operating on large datasets, and breaking with the methodological tradition for better or worse. The traditional social sciences, such as sociology or anthropology, are thus under the double threat of becoming marginalized or even irrelevant; both because of the new methods of research, which require more computational skills, and because of the increasing competition from the corporate world, which gains an additional advantage based on data access. However, sociologists and anthropologists still have some important assets, too. Unlike data scientists, they have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data needs Thick Data. This book presents the available arsenal of new tools for studying the society quantitatively, but also show the new methods of analysis from the qualitative side and encourages their combination. In shows that Big Data can and should be supplemented and interpreted through thick data, as well as cultural analysis, in a novel approach of Thick Big Data.The book is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and successfully build mixed-methods approaches.

scholarly journals New Methods in the History of Medicine: Streamlining Workflows to Enable Big-Data History Projects

2017 ◽  
Vol 61 (3) ◽  
pp. 477-480
Author(s):  
S. Wright Kennedy ◽  
Jessica C. Kuzmin ◽  
Benjamin Jones
Keyword(s):  
Big Data ◽  
History Of Medicine ◽  
New Methods ◽  
History Of ◽  
Data History

scholarly journals School in Digital Age: How Big Data Help to Transform the Curriculum

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 33
Author(s):  
Svetlana Nikolaevna Vachkova ◽  
Elena Yurevna Petryaeva ◽  
Roman B. Kupriyanov ◽  
Ruslan S. Suleymanov
Keyword(s):  
Data Mining ◽  
Big Data ◽  
New Technologies ◽  
School Curriculum ◽  
Digital Tools ◽  
Substantial Impact ◽  
New Methods ◽  
Data Mining Algorithms ◽  
Learning Content ◽  
Mining Algorithms

The transition to digital society is characterised by the development of new methods and tools for big data processing. New technologies have a substantial impact on the education sector. The article represents the results of applying big data to analyse and transform the learning content of Moscow’s schools. The analysis of the school curriculum comprised the following: (a) identifying one-topic lesson scripts, (b) analysing cross-disciplinary connections between subjects, (c) verifying the compliance of the lesson script digital content to the Federal Educational Standards. The analysed material included 36,644 lesson scripts. The analysis has been conducted using specifically designed digital tools featuring data mining algorithms. The article considers the issue of applying data mining algorithms to analyse school curriculum for the improvement of its quality.

Artificial intelligence and big data in tourism: a systematic literature review

2020 ◽  
Vol 11 (2) ◽  
pp. 343-367 ◽  
Author(s):  
Dimitra Samara ◽  
Ioannis Magnisalis ◽  
Vassilios Peristeras
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
New Technologies ◽  
Recent Literature ◽  
Content Type ◽  
Analysis Methodology ◽  
Tourism Sector

Purpose This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent literature. Design/methodology/approach A systematic literature review was conducted under the McKinsey’s Global Institute (Talwar and Koury, 2017) methodological perspective that identifies the four ways (i.e. project, produce, promote and provide) in which BDAI creates value. The authors enhanced this analysis methodology by depicting relevant challenges as well. Findings The findings imply that BDAI create value for the tourism sector through appropriately identified disseminations. The benefits of adopting BDAI strategies include increased efficiency, productivity and profitability for tourism suppliers combined with an extremely rich and personalized experience for travellers. The authors conclude that challenges can be bypassed by adopting a BDAI strategy. Such an adoption will stand critical for the competitiveness and resilience of existing established and new players in the tourism sector. Originality/value Besides identifying the benefits that BDAI brings in the tourism sector, the research proposes a guidebook to overcome challenges when introducing such new technologies. The exploration of the BDAI literature brings important implication for managers, academicians and consumers. This is the first systematic review in an area and contributes to the broader e-commerce marketing, retailing and e-tourism research.

scholarly journals Data Mining and Knowledge Discovery for Big Data in Cloud Environment

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1118-1131
Author(s):  
Raid Abd Alreda Shekan ◽  
Ahmed Mahdi Abdulkadium ◽  
Hiba Ameer Jabir
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Cloud Computing Environment ◽  
New Methods ◽  
History Of ◽  
New Vision ◽  
Digital Processes

In past few decades, big data has evolved as a modern framework that offers huge amount of data and possibilities for applying and/or promoting analysis and decision-making technologies with unparalleled importance for digital processes in organization, engineering and science. Because of the new methods in these domains, the paper discusses history of big data mining under the cloud computing environment. In addition to the pursuit of exploration of knowledge, Big Data revolution gives companies many exciting possibilities (in relation to new vision, decision making and business growths strategies). The prospect of developing large-data processing, data analytics, and evaluation through a cloud computing model has been explored. The key component of this paper is the technical description of how to use cloud computing and the uses of data mining techniques and analytics methods in predictive and decision support systems.

Me and My Data

2018 ◽  
Vol 48 (5) ◽  
pp. 616-626 ◽  
Author(s):  
Sarah E. Igo
Keyword(s):  
Big Data ◽  
Civil Rights ◽  
Access Control ◽  
Personal Data ◽  
First Century ◽  
Special Issue ◽  
History Of ◽  
Economic Developments ◽  
The Individual ◽  
Individual Ownership

This article examines a recent, unexamined turn in the history of personal data in the last half century: the era when it was re-envisioned as a possession of the individual whom it described or from whom it was obtained. Data—whether scientific, commercial, or bureaucratic—had often been treated as confidential or protected, but it had not typically been conceived in terms of individual ownership. But starting in the later 1960s, more and more people in the industrialized West questioned whether they or the authorities who collected or maintained their data properly had claim to that information. This question was sparked as much by political and economic developments as it was by scientific and technological ones. Citizens’ move to shore up their proprietary claims would prompt new regulations around access, control, and consent that continue to undergird contemporary ideas about personal data. A product of social movements and civil rights reforms as well as market thinking, this bid for authority over one’s “own” information would however reveal its limitations by the turn of the twenty-first century, particularly in the context of a big data economy. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

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