Advances in transparency and reproducibility in the social sciences

Freese, Jeremy; Rauf, Tamkinat; Voelkel, Jan G.

doi:10.1016/j.ssresearch.2022.102770

Cited by 14 publications

(13 citation statements)

References 97 publications

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“…Its scientific potential manifests itself, among other occasions, in the possibility of replicating studies, the re-purposing of old research data, and opportunities for better cooperation across academia, government, and the private sector (Engzell and Rohrer 2021;Gregory et al 2020;Zenk-Möltgen et al 2018). Furthermore, open social science data can be a solution for known and occurring problems, such as when only falsely significant results are published (i.e., p-hacking) or when hypotheses are made to fit certain significant patterns found in the data (known as publication bias) (Breznau 2021;Freese, Rauf and Voelkel 2022). Archived data can also enable a more profound historical perspective on social science practices (McLeod & O'Connor 2020).…”

Section: Open Data In the Social Sciencesmentioning

confidence: 99%

“…Moreover, significant disparities exist in terms of open data practices within the social sciences. The fields of economics and psychology, in particular, have embraced a more extensive and formalized open data policy as a common practice (Freese et al 2022). Political science has also made notable strides in this regard (Zenk-Möltgen et al 2018).…”

Section: Open Data In the Social Sciencesmentioning

confidence: 99%

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Five Suggestions Towards User-Centred Data Repositories in the Social Sciences

Kruithof,

Vanroelen,

Van den Borre

2024

Data Science Journal

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Data repositories for the social sciences are facing some discipline-specific challenges. This essay provides an overview of the four key challenges. In addition, five suggestions are made to strengthen domain-specific online data repositories in the social sciences, supported by good practices. Using a user-centred approach, these suggestions aim to further open up the social sciences data landscape. The essay first addresses insights regarding the sharing of quantitative and qualitative data in light of the specific needs and issues regarding these two types of data. Thereafter, we suggest (i) 'data labs' for novice data re-users, (ii) advocate for the repository as an online meeting place for researchers, (iii&iv) argue for two concrete metadata approaches for quantitative and qualitative data separately, (v) provide ideas for handling epistemological challenges of data sharing, and (vi) introduce the data repository as a possible contact medium for research participants.

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Section: Open Data In the Social Sciencesmentioning

confidence: 99%

Section: Open Data In the Social Sciencesmentioning

confidence: 99%

Five Suggestions Towards User-Centred Data Repositories in the Social Sciences

Kruithof,

Vanroelen,

Van den Borre

2024

Data Science Journal

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“…At the fundamental level, "opening" science means making knowledge more transparent, accessible, reproducible, and reliable. Many relatively simple solutions and practices that support these goals have been popularised, e.g., open-access publishing, preprints, preregistration, replications, and transparency in data management (Altman & Cohen 2022;Firebaugh 2007;Freese et al 2022;Nosek et al 2015). During the last decade, we also witnessed large-scale initiatives aimed at reforming the more broadly defined scholarly knowledge infrastructure (Altman & Cohen 2022;Edwards et al 2013), such as building institutional networks, open data infrastructures and data hubs, such as Open Data Infrastructure for Social Science and Economic Innovations (Odissei) in the Netherlands, Dataverse Network, and openICPSR (Freese & King 2018;Gerring et al 2020;Kapiszewski & Karcher 2020;King 2007;Moshontz et al 2018).…”

Section: Opening the Knowledge Ecosystemmentioning

confidence: 99%

“…The current debate on open science (Altman & Cohen 2022;Fecher & Wagner 2016;Freese, Rauf & Voelkel 2022;Friesike et al 2014) provides a good moment to discuss these challenges and consider new approaches to scientific cooperation and knowledge production. The response to the pandemic showed that science could be dynamic and integrate global-scale efforts (Altman & Cohen 2022;Callaway 2020).…”

Section: Introductionmentioning

confidence: 99%

Accelerating Social Science Knowledge Production with the Coordinated Open-Source Model

Turek¹

2023

Preprint

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With the growing complexity of knowledge production, social science must accelerate and open up to maintain explanatory power and responsiveness. This requires redesigning the front-end of research to build an open and expandable knowledge infrastructure that stimulates broad collaborations, enables breaking down inertia and path dependencies of conventional approaches, and boosts discovery and innovation. This article discusses the coordinated open-source model as a promising organisational scheme that can supplement conventional research infrastructure in certain areas. The model offers flexibility, decentralization, and community-based development and aligns with open science ideas, such as reproducibility and transparency. Similar solutions have been successfully applied in natural science, but social science lags behind. I present the model's design, and consider its potential and limitations (e.g., regarding development, sustainability and coordination). I also discuss applications of the coordinated open-source model in various areas, including the first open-source survey harmonization project in social science.

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“…Our work extends current efforts to increase transparency in social science (Freese, Rauf, and Voelkel 2022). Specifically, we use "multiverse analysis," a procedure that exhausts all possible combinations that arise from a set of reasonable analytic choices (Berk, Brown, and Zhao 2010;Muñoz and Young 2018;Western 1996).…”

mentioning

confidence: 99%

Understanding Patterns and Trends in Income Mobility through Multiverse Analysis

Engzell

Mood

2023

Am Sociol Rev

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Rising inequalities in rich countries have led to concerns that the economic ladder is getting harder to climb. Yet, research on trends in intergenerational income mobility finds conflicting results. To better understand this variation, we adopt a multiverse approach that estimates trends over 82,944 different definitions of income mobility, varying how and for whom income is measured. Our analysis draws on comprehensive register data for Swedish cohorts born 1958 to 1977 and their parents. We find that income mobility has declined, but for reasons neglected by previous research: improved gender equality in the labor market raises intergenerational persistence in women’s earnings and the household incomes of both men and women. Dominant theories that focus on childhood investments have blinded researchers to this development. Methodologically, we show how multiverse analysis can be used with abduction—inference to the best explanation—to improve theory-building in social science.

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Advances in transparency and reproducibility in the social sciences

Cited by 14 publications

References 97 publications

Five Suggestions Towards User-Centred Data Repositories in the Social Sciences

Five Suggestions Towards User-Centred Data Repositories in the Social Sciences

Accelerating Social Science Knowledge Production with the Coordinated Open-Source Model

Understanding Patterns and Trends in Income Mobility through Multiverse Analysis

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