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Search Results (4)

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Authors = Dominique Makowski ORCID = 0000-0001-5375-9967

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24 pages, 3042 KiB  
Article
Exploring the Role of News Outlets in the Rise of a Conspiracy Theory: Hydroxychloroquine in the Early Days of COVID-19
by Robert Dickinson, Dominique Makowski, Harm van Marwijk and Elizabeth Ford
COVID 2024, 4(12), 1873-1896; https://doi.org/10.3390/covid4120132 - 27 Nov 2024
Viewed by 2185
Abstract
Improper use of hydroxychloroquine to treat COVID-19 has been linked to 17,000 preventable deaths. This content analysis study investigates the emergence of this conspiracy theory, the role of the news media in perpetuating and disseminating it, and whether coverage differed by outlet political [...] Read more.
Improper use of hydroxychloroquine to treat COVID-19 has been linked to 17,000 preventable deaths. This content analysis study investigates the emergence of this conspiracy theory, the role of the news media in perpetuating and disseminating it, and whether coverage differed by outlet political alignment. We searched Nexis for relevant media from 17–31 March 2020. A total of 128 media pieces were coded qualitatively and thematically analysed. The news media amplified the voices of right-wing political elites and used a variety of manipulative tactics in reporting on hydroxychloroquine. Powerful ingroup/outgroup mechanisms polarised the American public and created a schism between Trump supporters and the public health apparatus that reflected the political asymmetry in reporting on hydroxychloroquine. The widespread use of optimistic framings and anecdotal evidence contributed to public misunderstandings of the evidence. Therefore, strategic and interventionist public health efforts are required to combat misinformation. This study informs discussions of how politicised media coverage catalyses conspiracism. Full article
(This article belongs to the Special Issue COVID and Public Health)
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9 pages, 230 KiB  
Editorial
Where Are We Going with Statistical Computing? From Mathematical Statistics to Collaborative Data Science
by Dominique Makowski and Philip D. Waggoner
Mathematics 2023, 11(8), 1821; https://doi.org/10.3390/math11081821 - 12 Apr 2023
Cited by 1 | Viewed by 2396
Abstract
The field of statistical computing is rapidly developing and evolving. Shifting away from the formerly siloed landscape of mathematics, statistics, and computer science, recent advancements in statistical computing are largely characterized by a fusing of these worlds; namely, programming, software development, and applied [...] Read more.
The field of statistical computing is rapidly developing and evolving. Shifting away from the formerly siloed landscape of mathematics, statistics, and computer science, recent advancements in statistical computing are largely characterized by a fusing of these worlds; namely, programming, software development, and applied statistics are merging in new and exciting ways. There are numerous drivers behind this advancement, including open movement (encompassing development, science, and access), the advent of data science as a field, and collaborative problem-solving, as well as practice-altering advances in subfields such as artificial intelligence, machine learning, and Bayesian estimation. In this paper, we trace this shift in how modern statistical computing is performed, and that which has recently emerged from it. This discussion points to a future of boundless potential for the field. Full article
(This article belongs to the Special Issue Advances in Statistical Computing)
12 pages, 7243 KiB  
Article
The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2
by Dominique Makowski, An Shu Te, Tam Pham, Zen Juen Lau and S. H. Annabel Chen
Entropy 2022, 24(8), 1036; https://doi.org/10.3390/e24081036 - 27 Jul 2022
Cited by 1 | Viewed by 3919
Abstract
Complexity quantification, through entropy, information theory and fractal dimension indices, is gaining a renewed traction in psychophsyiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of [...] Read more.
Complexity quantification, through entropy, information theory and fractal dimension indices, is gaining a renewed traction in psychophsyiology, as new measures with promising qualities emerge from the computational and mathematical advances. Unfortunately, few studies compare the relationship and objective performance of the plethora of existing metrics, in turn hindering reproducibility, replicability, consistency, and clarity in the field. Using the NeuroKit2 Python software, we computed a list of 112 (predominantly used) complexity indices on signals varying in their characteristics (noise, length and frequency spectrum). We then systematically compared the indices by their computational weight, their representativeness of a multidimensional space of latent dimensions, and empirical proximity with other indices. Based on these considerations, we propose that a selection of 12 indices, together representing 85.97% of the total variance of all indices, might offer a parsimonious and complimentary choice in regards to the quantification of the complexity of time series. Our selection includes CWPEn, Line Length (LL), BubbEn, MSWPEn, MFDFA (Max), Hjorth Complexity, SVDEn, MFDFA (Width), MFDFA (Mean), MFDFA (Peak), MFDFA (Fluctuation), AttEn. Elements of consideration for alternative subsets are discussed, and data, analysis scripts and code for the figures are open-source. Full article
(This article belongs to the Section Entropy and Biology)
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20 pages, 664 KiB  
Review
Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial
by Tam Pham, Zen Juen Lau, S. H. Annabel Chen and Dominique Makowski
Sensors 2021, 21(12), 3998; https://doi.org/10.3390/s21123998 - 9 Jun 2021
Cited by 273 | Viewed by 57460
Abstract
The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide [...] Read more.
The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2. Full article
(This article belongs to the Special Issue Brain Signals Acquisition and Processing)
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