Author
Rogier A. Kievit
Other affiliations: University of Cambridge, University College London, Cognition and Brain Sciences Unit ...read more
Bio: Rogier A. Kievit is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Cognition & Mental health. The author has an hindex of 36, co-authored 144 publications receiving 5371 citations. Previous affiliations of Rogier A. Kievit include University of Cambridge & University College London.
Topics: Cognition, Mental health, Medicine, Neurocognitive, Population
Papers
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01 Apr 2019
TL;DR: This tutorial paper provides basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provides open-source code for their streamlined implementation in R, Python and Matlab.
Abstract: Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired 'inference at a glance' nature of barplots and other similar visualization devices. These "raincloud plots" can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.
796 citations
TL;DR: Representational geometry is a framework that enables us to relate brain, computation, and cognition and review recent insights into perception, cognition, memory, and action.
732 citations
TL;DR: This article proposes that researchers preregister their studies and indicate in advance the analyses they intend to conduct, and proposes that only these analyses deserve the label “confirmatory,” and only for these analyses are the common statistical tests valid.
Abstract: The veracity of substantive research claims hinges on the way experimental data are collected and analyzed. In this article, we discuss an uncomfortable fact that threatens the core of psychology’s academic enterprise: almost without exception, psychologists do not commit themselves to a method of data analysis before they see the actual data. It then becomes tempting to fine tune the analysis to the data in order to obtain a desired result—a procedure that invalidates the interpretation of the common statistical tests. The extent of the fine tuning varies widely across experiments and experimenters but is almost impossible for reviewers and readers to gauge. To remedy the situation, we propose that researchers preregister their studies and indicate in advance the analyses they intend to conduct. Only these analyses deserve the label “confirmatory,” and only for these analyses are the common statistical tests valid. Other analyses can be carried out but these should be labeled “exploratory.” We illustrate our proposal with a confirmatory replication attempt of a study on extrasensory perception.
709 citations
01 Jan 2018
TL;DR: This tutorial paper provides basic demonstrations of the strength of raincloud plots and similar approaches, outlines potential modifications for their optimal use, and provides open-source code for their streamlined implementation in R, Python and Matlab.
Abstract: Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired 'inference at a glance' nature of barplots and other similar visualization devices. These "raincloud plots" can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab ( https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.
505 citations
TL;DR: In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, where the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measure attribute is seen as the effect of the observable variables as discussed by the authors.
459 citations
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TL;DR: The meaning of the terms "method" and "method bias" are explored and whether method biases influence all measures equally are examined, and the evidence of the effects that method biases have on individual measures and on the covariation between different constructs is reviewed.
Abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
8,719 citations
TL;DR: It is argued that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades, and it is shown thatLMEMs generalize best when they include the maximal random effects structure justified by the design.
6,878 citations
University College London1, Camden and Islington NHS Foundation Trust2, Royal Melbourne Hospital3, University of Exeter4, University of Plymouth5, University of Cambridge6, University of Manchester7, Tel Aviv University8, Goa Medical College9, Johns Hopkins University10, University of California, Davis11, Kaiser Permanente12, University College Hospital, Ibadan13, University of Montpellier14, Dalhousie University15, University of Southern California16, Oslo University Hospital17, University of Washington18
TL;DR: Author(s): Livingston, Gill; Huntley, Jonathan; Sommerlad, Andrew ; Sommer Glad, Andrew; Ames, David; Ballard, Clive; Banerjee, Sube; Brayne, Carol; Burns, Alistair; Cohen-Mansfield, Jiska; Cooper, Claudia; Costafreda, Sergi G; Dias, Amit; Fox, Nick; Gitlin, Laura N; Howard, Robert; Kales, Helen C;
3,559 citations
TL;DR: A survey of factor analytic studies of human cognitive abilities can be found in this paper, with a focus on the role of factor analysis in human cognitive ability evaluation and cognition. But this survey is limited.
Abstract: (1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.
2,388 citations
TL;DR: An eight-step new-statistics strategy for research with integrity is described, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.
Abstract: We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data- analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.
2,339 citations