Gisela Govaart

Bio: Gisela Govaart is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Hindsight bias & Epistemology. The author has an hindex of 1, co-authored 1 publications receiving 7 citations. Previous affiliations of Gisela Govaart include Charité & Max Planck Society.

A community-sourced glossary of open scholarship terms

Abstract: Open scholarship has transformed research, and introduced a host of new terms in the lexicon of researchers. The ‘Framework for Open and Reproducible Research Teaching’ (FORRT) community presents a crowdsourced glossary of open scholarship terms to facilitate education and effective communication between experts and newcomers.

Reflexivity in quantitative research: A rationale and beginner's guide

Abstract: Reflexivity is the act of examining one's own assumption, belief, and judgement systems, and thinking carefully and critically about how these influence the research process. The practice of reflexivity confronts and questions who we are as researchers and how this guides our work. It is central in debates on objectivity, subjectivity, and the very foundations of social science research and generated knowledge. Incorporating reflexivity in the research process is traditionally recognized as one of the most notable differences between qualitative and quantitative methodologies. Qualitative research centres and celebrates the participants' personal and unique lived experience. Therefore, qualitative researchers are readily encouraged to consider how their own unique positionalities inform the research process and this forms an important part of training within this paradigm. Quantitative methodologies in social and personality psychology, and more generally, on the other hand, have remained seemingly detached from this level of reflexivity and general reflective practice. In this commentary, we, three quantitative researchers who have grappled with the compatibility of reflexivity within our own research, argue that reflexivity has much to offer quantitative methodologists. The act of reflexivity prompts researchers to acknowledge and centre their own positionalities, encourages a more thoughtful engagement with every step of the research process, and thus, as we argue, contributes to the ongoing reappraisal of openness and transparency in psychology. In this paper, we make the case for integrating reflexivity across all research approaches, before providing a ‘beginner's guide’ for quantitative researchers wishing to engage reflexively with their own work, providing concrete recommendations, worked examples, and reflexive prompts.

The case for formal methodology in scientific reform.

Abstract: Current attempts at methodological reform in sciences come in response to an overall lack of rigor in methodological and scientific practices in experimental sciences. However, most methodological reform attempts suffer from similar mistakes and over-generalizations to the ones they aim to address. We argue that this can be attributed in part to lack of formalism and first principles. Considering the costs of allowing false claims to become canonized, we argue for formal statistical rigor and scientific nuance in methodological reform. To attain this rigor and nuance, we propose a five-step formal approach for solving methodological problems. To illustrate the use and benefits of such formalism, we present a formal statistical analysis of three popular claims in the metascientific literature: (i) that reproducibility is the cornerstone of science; (ii) that data must not be used twice in any analysis; and (iii) that exploratory projects imply poor statistical practice. We show how our formal approach can inform and shape debates about such methodological claims.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series.

Abstract: Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.