A robust data-driven approach identifies four personality types across four large data sets

Gerlach, Martin; Farb, Beatrice; Revelle, William; Amaral, Luı́s A. Nunes

doi:10.1038/s41562-018-0419-z

Cited by 158 publications

(156 citation statements)

References 39 publications

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“…Data-driven 36 profiling allows uncovering complex structures that are "hidden" in the data. It has been 37 used as a diagnostic tool in various fields, such as functional imaging (Krohne et al, 2019), 38 psychology (Gerlach et al, 2018) or logopedics (Sharma et al, 2019). The idea of using 39 computational data analysis that applies principles of the knowledge discovery from 40 databases (KDD; Frawley et al, 1992) has recently gained attention in the field of 41 audiology, for example in connection to hearing-aid features (Lansbergen & Dreschler,42 2020; Mellor et al, 2018).…”

Section: Introduction 34mentioning

confidence: 99%

Robust data-driven auditory profiling towards precision audiology

Sanchez-Lopez

Fereczkowski

Neher

et al. 2020

Preprint

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Data-driven profiling allows uncovering complex hidden structures in a dataset and has been used as a diagnostic tool in various fields of work. In audiology, the clinical characterization of hearing deficits for hearing-aid fitting is typically based on the pure-tone audiogram only. Implicitly, this relies on the assumption that the audiogram can predict a listener's supra-threshold hearing abilities. Sanchez-Lopez et al. [Trends in hearing vol. 22 (2018)] hypothesized that the hearing deficits of a given listener, both at hearing threshold and at supra-threshold sound levels, result from two independent types of "auditory distortions". The authors performed a data-driven analysis of two large datasets with results from numerous tests, which led to the identification of four distinct "auditory profiles". However, the definition of the two types of distortion was challenged by differences between the two datasets in terms of the selected tests and type of listeners included in the datasets. Here, a new dataset was generated with the aim of overcoming those limitations. A heterogeneous group of listeners (N = 75) was tested using measures of speech intelligibility, loudness perception, binaural processing abilities and spectro-temporal resolution. The subsequent data analysis allowed refining the auditory profiles proposed by Sanchez-Lopez et al. (2018). Besides, a robust iterative data-driven method is proposed here to reduce the influence of the individual data in the definition of the auditory profiles. The updated auditory profiles may provide a useful basis for improved hearing rehabilitation, e.g. through profile-based hearing-aid fitting.

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Section: Introduction 34mentioning

confidence: 99%

Robust data-driven auditory profiling towards precision audiology

Sanchez-Lopez

Fereczkowski

Neher

et al. 2020

Preprint

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“…A parallel of the LCA/LTA approach is the modeling of personality types (for example, byAsendorpf, Borkenau, Ostendorf, & van Aken, 2001;Gerlach, Farb, Revelle, & Nunes Amaral, 2018), where ratings on the…”

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confidence: 99%

Categorizing People by Their Preference for Religious Styles: Four Types Derived from Evaluation of Faith Development Interviews

Streib

Chen

Hood

2019

The International Journal for the Psychology of Religion

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This article presents an empirical complement to Streib's (2001) model of religious styles that was published in this journal. Now, after our research teams in Bielefeld and Chattanooga were able to conduct and evaluate more than 700 faith development interviews (FDI), which was made possible by generous grants from the German Research Foundation (DFG; Grants STR570/5-3; STR570/15-1; STR570/17-1; STR570/20-1) and from the John Templeton Foundation (Grant#55249), we could move forward to the typological modeling of FDI results of three subsamples.

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“…Nevertheless, Jung's ideas about personality have been, since long, abandoned in favor of empirically proven personality theories (Chamorro-Premuzic, Von Stumm & Furnham, 2015), whose links with the human brain have been proved (DeYoung et al, 2010). In this sense, Gerlach et al (2018) used data from 1.5 million participants, that had answered different instruments based on the Five Factor Model (FFM) of personality, and although they claim to have identified four distinct personality types, they also acknowledge that: 1there is considerable overlap between types;…”

Section: Discussionmentioning

confidence: 99%

Learning styles and the human brain: what does the evidence tell us?

Álvarez-Montero¹

2018

Preprint

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Learning styles are a widespread idea that has high levels of acceptance in education and psychology. The promises of adopting the construct range from gains in academic performance, to the development of respect for the self and others. Nevertheless, from a scientific perspective it remains highly controversial. Most studies indicate that matching teaching to the learning styles of students does not improve learning, and that their psychometric instruments do not show enough reliability and validity. In this sense, this paper investigated if the postulates of learning styles are consistent with the way the human brain process information. Moreover, the trend of the accumulated evidence about learning styles was analyzed, using a simple algorithm, to determine if they are a proven, debatable, improbable or denied phenomenon. Results show: (1) that learning styles, along with the multiple intelligence theory and the left or right-brained hypothesis, are not compatible with what is currently know about the inner workings of the brain; (2) that the trend of the evidence, although still limited, does not favor learning styles; (3) that as a phenomenon styles are classified as improbable.

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A robust data-driven approach identifies four personality types across four large data sets

Cited by 158 publications

References 39 publications

Robust data-driven auditory profiling towards precision audiology

Robust data-driven auditory profiling towards precision audiology

Categorizing People by Their Preference for Religious Styles: Four Types Derived from Evaluation of Faith Development Interviews

Learning styles and the human brain: what does the evidence tell us?

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