How Can Big Data Science Transform the Psychological Sciences?

Albritton, Betsy H.; Tonidandel, Scott

doi:10.1017/sjp.2020.45

Cited by 3 publications

(3 citation statements)

References 26 publications

(23 reference statements)

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“…Indeed, several elements of robust science are common features of Big Data methods (Grand et al, 2018). Most psychological studies suffer from small underpowered samples, and publication pressures incentivize Questionable Research Practices (QRP’s) like p-hacking (Albritton & Tonidandel, 2020). Big data methods, in contrast, tend to have large samples making statistical significance testing superfluous, thereby eliminating the need for p-hacking.…”

Section: Discussionmentioning

confidence: 99%

An introductory guide for conducting psychological research with big data.

Vezzoli¹,

Zogmaister²

2023

Psychological Methods

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Big Data can bring enormous benefits to psychology. However, many psychological researchers show skepticism in undertaking Big Data research. Psychologists often do not take Big Data into consideration while developing their research projects because they have difficulties imagining how Big Data could help in their specific field of research, imagining themselves as "Big Data scientists," or for lack of specific knowledge. This article provides an introductory guide for conducting Big Data research for psychologists who are considering using this approach and want to have a general idea of its processes. By taking the Knowledge Discovery from Database steps as the fil rouge, we provide useful indications for finding data suitable for psychological investigations, describe how these data can be preprocessed, and list some techniques to analyze them and programming languages (R and Python) through which all these steps can be realized. In doing so, we explain the concepts with the terminology and take examples from psychology. For psychologists, familiarizing with the language of data science is important because it may appear difficult and esoteric at first approach. As Big Data research is often multidisciplinary, this overview helps build a general insight into the research steps and a common language, facilitating collaboration across different fields.

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Section: Discussionmentioning

confidence: 99%

An introductory guide for conducting psychological research with big data.

Vezzoli¹,

Zogmaister²

2023

Psychological Methods

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“…Algorithms can also be probed to understand their predictions despite common concerns that algorithms are black boxes with unforeseen, harmful biases. A growing body of research suggests that computer scoring can guard against human biases in personnel selection when we leverage existing tools that identify what is driving the decision or classification within the algorithm and those biased features are removed from the training data (Albritton & Tonidandel, 2020; Ribeiro et al., 2016; Tay et al., 2021). Given these advantages, practitioners continue to develop consulting firms and technology startups that rely on NLP, deep learning, and other ML approaches to improve and automate personnel selection.…”

Section: Study 1: Algorithmic Construct Generalizability: Scoring Nov...mentioning

confidence: 99%

Improving measurement and prediction in personnel selection through the application of machine learning

Koenig,

Tonidandel,

Thompson

et al. 2023

Personnel Psychology

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Machine learning (ML) is being widely adopted by organizations to assist in selecting personnel, commonly by scoring narrative information or by eliminating the inefficiencies of human scoring. This combined article presents six such efforts from operational selection systems in actual organizations. The findings show that ML can score narrative information collected from candidates either in writing or orally in response to assessment questions (called constructed response) as accurately and reliably as human judges, but much more efficiently, making such responses more feasible to include in personnel selection and often improving validity with little or no adverse impact. Moreover, algorithms can generalize across assessment questions, and algorithms can be created to predict multiple outcomes simultaneously (e.g., productivity and turnover). ML has even been demonstrated to make job analysis more efficient by determining knowledge and skill requirements based on job descriptions. Collectively, the studies in this article illustrate the likely major impact that ML will have on the practice and science of personnel selection from this point forward.

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“…This shows that psychological research in the past has focused more on the explanation of a certain psychological response. Big data technology can predict and intervene certain psychological reactions on the basis of interpretation ( 22 ). Furthermore, the research method based on big data technology can conduct an overall analysis of all data, avoid the sampling error of previous research methods from the sample to the overall promotion, reduce the experimental error caused by improper control of experimental conditions, and greatly improve the efficiency and scale of data processing.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Impacts on the Psychological Changes of Chinese Returning College Students After the Outbreak of the 2019 Coronavirus Disease

Xue

Pyong

et al. 2022

Front. Public Health

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This work aims to analyze the impacts on the psychological changes of Chinese returning college students after the outbreak of the 2019 coronavirus disease (COVID-19). A questionnaire survey is used to take 1,482 college students who returned to school after the epidemic as the research objects. The Chinese college students' knowledge of the epidemic, alienation in physical education class, school happiness, and expectations for a healthy life in the future are investigated and analyzed. The research results manifest that Chinese returning college students have relatively poor awareness of COVID-19, and the overall degree of alienation in physical education classes after the epidemic is low, with an average score of 3.55 ± 1.018. The overall level of school happiness is high, with an average score of 4.94 ± 0.883; the overall level of expectation for a healthy life in the future is high, with an average score of 3.50 ± 0.840. It denotes that the epidemic has a great psychological impact on returning college students, and it is necessary to strengthen mental health education for college students after COVID-19. It provides a sustainable theoretical reference for the formulation of psychological intervention measures for returning college students.

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How Can Big Data Science Transform the Psychological Sciences?

Cited by 3 publications

References 26 publications

An introductory guide for conducting psychological research with big data.

An introductory guide for conducting psychological research with big data.

Improving measurement and prediction in personnel selection through the application of machine learning

Analysis of the Impacts on the Psychological Changes of Chinese Returning College Students After the Outbreak of the 2019 Coronavirus Disease

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