Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges

2020 ◽  
Vol 34 (5) ◽  
pp. 632-648
Author(s):  
Leo Alexander ◽  
Evan Mulfinger ◽  
Frederick L. Oswald
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
European Association ◽  
Reliability And Validity ◽  
Personality Measurement ◽  
Personality Psychology ◽  
Personality Research ◽  
The Future ◽  
Challenges And Opportunities ◽  
Conceptual Paper

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

Objections to the HEXACO Model of Personality Structure—and why those Objections Fail

2020 ◽  
Vol 34 (4) ◽  
pp. 492-510 ◽  
Author(s):  
Michael C. Ashton ◽  
Kibeom Lee
Keyword(s):  
Social Media ◽  
European Association ◽  
Personality Structure ◽  
Personality Psychology ◽  
Personality Research ◽  
Target Article ◽  
Social Media Platforms ◽  
Dimensional Models ◽  
Hexaco Model

The six–dimensional HEXACO model of personality structure and its associated inventory have increasingly been used in personality research. But in spite of the evidence supporting this structure and demonstrating its advantages over five–dimensional models, some researchers continue to use and promote the latter. Although there has been little overt, organized argument against the adoption of the HEXACO model, we do hear sporadic offerings of reasons for retaining the five–dimensional systems, usually in informal conversations, in manuscript reviews, on social media platforms, and occasionally in published works. In this target article, we list all of the objections to the HEXACO model that we have heard of, and we then explain why each objection fails. © 2020 European Association of Personality Psychology

A Framework for Testing Causality in Personality Research

2018 ◽  
Vol 32 (3) ◽  
pp. 254-268 ◽  
Author(s):  
Giulio Costantini ◽  
Marco Perugini
Keyword(s):  
Theoretical Model ◽  
European Association ◽  
Laboratory Research ◽  
Personality Psychology ◽  
Causal Explanations ◽  
Personality Research ◽  
Conceptual Clarity ◽  
Candidate Target ◽  
Ecological Momentary ◽  
Over Time

Causal explanations in personality require conceptual clarity about alternative causal conditions that could, even in principle, affect personality. These causal conditions crucially depend on the theoretical model of personality, each model constraining the possibility of planning and performing causal research in different ways. We discuss how some prominent models of personality allow for specific types of causal research and impede others. We then discuss causality from a network perspective, which sees personality as a phenomenon that emerges from a network of behaviours and environments over time. From a methodological perspective, we propose a three–step strategy to investigate causality: (1) identify a candidate target for manipulation (e.g. using network analysis), (2) identify and test a manipulation (e.g. using laboratory research), and (3) deliver the manipulation repeatedly for a congruous amount of time (e.g. using ecological momentary interventions) and evaluate its ability to generate trait change. We discuss how a part of these steps was implemented for trait conscientiousness and present a detailed plan for implementing the remaining steps. Copyright © 2018 European Association of Personality Psychology

scholarly journals Personality Research and Assessment in the Era of Machine Learning

2020 ◽  
Vol 34 (5) ◽  
pp. 613-631 ◽  
Author(s):  
Clemens Stachl ◽  
Florian Pargent ◽  
Sven Hilbert ◽  
Gabriella M. Harari ◽  
Ramona Schoedel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Personality Assessment ◽  
Latent Variables ◽  
Learning Models ◽  
Personality Psychology ◽  
Fine Grained ◽  
Personality Research ◽  
Key Issues ◽  
Complex Relationships ◽  
Machine Learning Models

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.

Digital Epidemiology of Innovation (Preprint)

2021 ◽  
Author(s):  
Ivan Triana ◽  
LUIS PINO ◽  
Dennise Rubio
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Management ◽  
Medical Practice ◽  
Data Science ◽  
Medical Literature ◽  
The Future ◽  
Digital Epidemiology ◽  
Definition Of ◽  
And Control

UNSTRUCTURED Bio and infotech revolution including data management are global tendencies that have a relevant impact on healthcare. Concepts such as Big Data, Data Science and Machine Learning are now topics of interest within medical literature. All of them are encompassed in what recently is named as digital epidemiology. The purpose of this article is to propose our definition of digital epidemiology with the inclusion of a further aspect: Innovation. It means Digital Epidemiology of Innovation (DEI) and show the importance of this new branch of epidemiology for the management and control of diseases. In this sense, we will describe all characteristics concerning to the topic, current uses within medical practice, application for the future and applicability of DEI as conclusion.

scholarly journals Big data in IBD: big progress for clinical practice

Gut ◽  
2020 ◽  
Vol 69 (8) ◽  
pp. 1520-1532 ◽  
Author(s):  
Nasim Sadat Seyed Tabib ◽  
Matthew Madgwick ◽  
Padhmanand Sudhakar ◽  
Bram Verstockt ◽  
Tamas Korcsmaros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Treatment Options ◽  
Health And Safety ◽  
Fine Tuning ◽  
Molecular Networks ◽  
Data Generation ◽  
Intrinsic Factors ◽  
Challenges And Opportunities ◽  
Efficient Integration

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

Modern Statistical Modeling in Machine Learning and Big Data Analytics

2020 ◽  
pp. 135-151
Author(s):  
Niloofar Ramezani
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Statistical Modeling ◽  
Statistical Models ◽  
Data Analytics ◽  
High Dimensional Data ◽  
Big Data Analytics ◽  
High Dimensional ◽  
Statistical Techniques ◽  
The Future

Machine learning, big data, and high dimensional data are the topics we hear about frequently these days, and some even call them the wave of the future. Therefore, it is important to use appropriate statistical models, which have been established for many years, and their efficiency has already been evaluated to contribute into advancing machine learning, which is a relatively newer field of study. Different algorithms that can be used within machine learning, depending on the nature of the variables, are discussed, and appropriate statistical techniques for modeling them are presented in this chapter.

scholarly journals Towards More Rigorous Personality Trait–Outcome Research

2016 ◽  
Vol 30 (4) ◽  
pp. 292-303 ◽  
Author(s):  
René Mõttus
Keyword(s):  
Personality Traits ◽  
Personality Trait ◽  
Outcome Research ◽  
European Association ◽  
Causal Inferences ◽  
Personality Psychology ◽  
Factor Loadings ◽  
Personality Research ◽  
Trait Level

Much of personality research attempts to identify causal links between personality traits and various types of outcomes. I argue that causal interpretations require traits to be seen as existentially and holistically real and the associations to be independent of specific ways of operationalizing the traits. Among other things, this means that, to the extents that causality is to be ascribed to such holistic traits, items and facets of those traits should be similarly associated with specific outcomes, except for variability in the degrees to which they reflect the traits (i.e. factor loadings). I argue that, before drawing causal inferences about personality trait–outcome associations, the presence of this condition should be routinely tested by, for example, systematically comparing the outcome associations of individual items or facets, or sampling different indicators for measuring the same purported traits. Existing evidence suggests that observed associations between personality traits and outcomes at least sometimes depend on which particular items or facets have been included in trait operationalizations, calling trait–level causal interpretations into question. However, this has rarely been considered in the literature. I argue that when outcome associations are specific to facets, they should not be generalized to traits. Furthermore, when the associations are specific to particular items, they should not even be generalized to facets. Copyright © 2016 European Association of Personality Psychology

scholarly journals Assessing Personality–Situation Interplay in Personnel Selection: Toward More Integration into Personality Research

2017 ◽  
Vol 31 (5) ◽  
pp. 424-440 ◽  
Author(s):  
Filip Lievens ◽  
Wendy Johnson
Keyword(s):  
Personnel Selection ◽  
European Association ◽  
Personality Psychology ◽  
Situational Judgement Tests ◽  
Personality Research ◽  
Research Themes ◽  
Job Candidates ◽  
Assessment Centre ◽  
Display Behaviour ◽  
Situational Judgement

Over the years, the personnel selection field has developed methods to assess trait expression in particular situations, but these approaches have evolved mostly outside the field of personality psychology. In this article, I review available personnel selection evidence regarding two such approaches: (i) situational judgement tests that present short scenarios and ask job candidates how they would handle the situations and (ii) assessment centre exercises requiring candidates to display behaviour in specified interactive situations. I describe these approaches and discuss their relations with personality research. I posit that adapting these approaches to personality research creates methodological diversity to address key research themes related to within–person variability, trait–behaviour links, personality disorders, and personality expression and perception. Copyright © 2017 European Association of Personality Psychology

scholarly journals The future of the International Schools for Young Astronomers (ISYA)

2018 ◽  
Vol 13 (S349) ◽  
pp. 452-458 ◽  
Author(s):  
Itziar Aretxaga
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
International Schools ◽  
Virtual Networks ◽  
Internet Connectivity ◽  
New Era ◽  
Face To Face ◽  
The Future ◽  
Fast Internet

AbstractThe 5-decade old ISYA program is evaluated in the context of the experience gathered in the field: 41 schools organized in 27 countries with a total of more than 1400 students to date. In the new era of fast internet connectivity, social media, virtual networks, big data and machine learning, the value of face-to-face graduate schools for regions with limited up-to-date astrophysics research is presented, together with the plan to develop the ISYA program into the next decade.

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