Resources

Readings, links, and tools

Four words that are not synonyms

The four terms below follow the definitions in Nosek et al. (2025). They are not interchangeable, and the day depends on the difference: a finding can survive every robustness check and still be one that nobody can recompute from the original data, or reproduce cleanly and still fail on new data. The ‘in this workshop’ column says where each concept appears in the session.

Term Definition In this workshop
Reproducibility Same question · same data · same analysis → same result Tested by the SCORE programme (Alipourfard et al. 2021); the result reported by the original author (OA) was not reproducible (Miske et al. 2026)
Robustness Same question · same data · justifiable alternative analysis → stable result The Multi100 design (Aczel et al. 2026); the five-analyst table; the specification work in Part 3
Replicability Same question · independent new data → same result Tyner et al. (Tyner et al. 2026); discussed in Part 1
Repeatability Umbrella term covering all three above Used in the Nosek et al. glossary as the overarching category

Core readings

The three Nature 2026 papers (SCORE)

Vocabulary

Estimands and analytical choices

Preregistration

These three are the pre-session reading list for the preregistration segment of the day, and all are open access. None is required, because everything is taught on the day, but they let you arrive with the ideas already in hand.

  • Nosek, Ebersole, DeHaven & Mellor (2018). The preregistration revolution. PNAS, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114 – a short primer on the idea on which preregistration depends: predicting a result versus explaining one after the fact, and the case, like ours, where the data already exist.

  • The AsPredicted eight-question form – the short template you complete in the session, listed on OSF as the “Preregistration Template from AsPredicted.org”. Live form: https://aspredicted.org; OSF listing: https://help.osf.io/article/158-create-a-preregistration.

  • van den Akker, Weston, Campbell, et al. (2021). Preregistration of secondary data analysis: A template and tutorial. Meta-Psychology, 5, 2625. https://doi.org/10.15626/MP.2020.2625 – the fuller template built for exactly our situation, reanalysing data that already exist; the tool that a full version of today’s exercise would use.

Reading the multiverse

The case study paper

The SCORE programme


Companion curriculum

Nine modules carry most of the teaching in this course, from a first line of R through to a reanalysis you can version and archive. The workshop at the centre is compressed by design, so it works the single hands-on case and leaves the skills that surround it to the modules; each of them stands on its own and can be taken without the others. The curriculum overview sets out the three strands that the modules group into, and a suggested reading order.

  • Getting started with R – for participants arriving with no R at all; best read before the day.
  • Quarto and reproducible documents – the medium in which every page of this site is written: what a .qmd file is and what rendering does to it, the YAML header and the chunk options that matter, figures, tables and cross-references, citations drawn from a bibliography, and publishing the finished document, all of it worked through on files you can open in this project rather than on a toy example.
  • The statistical methods behind the workshop – every model fitted on the case’s own data with real outputs read line by line, from a first lm() through fixed and random effects to multilevel, weighted, Bayesian and ordered-beta models, ending with meta-analysis and the common effect-size axis.
  • Draw the arrows you believe – causal graphs written as R formulas with dagify(): confounders, mediators and colliders fitted rather than only described, the EU-frames graph whose adjustment set is what two-way fixed effects absorb, and what fixed effects cannot fix.
  • The multiverse: origins, uses, and critics – where specification-curve thinking came from, the three purposes a multiverse can serve, and the 2025–26 critical debate read against this workshop’s own grid.
  • Simulation as a method – a simulated person-level twin with a known recipe, built to carry the original analysis: parameter recovery, composition against context, recovering individual effects, weights that matter, the degrees-of-freedom story run live, a null multiverse, a lineup, and design precision by simulation.
  • One claim, two levels – the published specification fitted at the individual level on a validated simulated twin, with the real-data values beside it: individual circumstance against national context, and what declaring a level of analysis does to the evidence.
  • Reproducibility repositories – research-data archives, the OSF nodes behind this case, and preregistration templates in more depth.
  • Git and GitHub for reproducible research – version control and publishing your own report, the hands-on practice kept out of the core day so it can be learned on its own terms.

One paper, all three Rs

The target article (Teney 2016) was put through all three SCORE activities: reproduction, robustness reanalysis, and replication. Few papers are, which is why this one case can carry the whole day. The parent node for the full dossier is https://osf.io/h7432/, and the table below summarises each component.

Component What Outcome
whp4b Original materials collected by COS View-only
cf8en Author-provided code – two Stata do-files (eb_all.do, eb_ml_ESR.do) supplied on request; data cannot be shared under GESIS terms (statement file gxk9r) Code available; data restricted
8fwrt Power analysis for the replication
ezhcs Push-button reproduction (with author code) Failed – file names in the code did not match the files available for download; 18 files referenced, 16 listed on the website
4rjbf Source-data reproduction “Not reproduced” – original effect size could not be extracted or re-calculated
gr8u6 Multi100 robustness – 5 independent reanalysts All 5 coefficients negative (claim-consistent direction); partial r from −0.006 to −0.452
zt5y2 Secondary-data replication (Eurobarometer + Eurostat, different waves; preregistered; N = 352,114 person-year obs.) Not replicated – sign flip: positive coefficient (b = 0.002, t = 2.004, p = 0.046) in both Analysis One and Analysis Two
6zqct Analyst’s maintained fork (workshop fetch target) – carries the working materials and today’s data https://osf.io/6zqct/
8rtwe Official Multi100 archival record for analyst C6HJR https://osf.io/8rtwe/

Three Rs, three different verdicts on one paper: reproduction failed (twice, differently); robustness held in sign but spanned two orders of magnitude in effect size; replication returned a sign flip. The glossary states that the three indicators are near-uncorrelated. A single case built entirely from public artefacts makes that concrete.


If you are at Newcastle University

The workshop is open to anyone, so support of the kind below exists wherever you are: the library at your own institution and its research-software or research-data team are the places to ask. As one worked example, participants based at Newcastle University can draw on the following.

Resource URL
RSE training (Research Software Engineering) https://rse.ncldata.dev/events
NCL Library – open research pages https://www.ncl.ac.uk/library/academic-support/research/open-research/
Research Data Newcastle https://data.ncl.ac.uk
UKRN Primers (open research primers) https://www.ukrn.org/primers/
ReproducibiliTea Newcastle Search ‘ReproducibiliTea Newcastle’ – a journal club for open and reproducible science

Tool documentation

Tool URL
Positron (IDE) https://positron.posit.co
Quarto https://quarto.org
quarto-live (webR extension) https://r-wasm.github.io/quarto-live/
osfr package https://docs.ropensci.org/osfr/
fixest package https://lrberge.github.io/fixest/
ggdag package https://r-causal.github.io/ggdag/

References

Aczel, Balazs, Barnabas Szaszi, Harry T. Clelland, et al. 2026. “Investigating the Analytical Robustness of the Social and Behavioural Sciences.” Nature 652 (8108): 135–42. https://doi.org/10.1038/s41586-025-09844-9.
Alipourfard, Nazanin, Beatrix Arendt, Daniel M Benjamin, et al. 2021. Systematizing Confidence in Open Research and Evidence (SCORE). 46mnb_v1. SocArXiv. https://doi.org/10.31235/osf.io/46mnb.
Auspurg, Katrin, and Josef Brüderl. 2021. “Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the Many Analysts, One Data Set Project.” Socius 7 (January): 23780231211024421. https://doi.org/10.1177/23780231211024421.
Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021. “What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory.” American Sociological Review 86 (3): 532–65. https://doi.org/10.1177/00031224211004187.
Miske, Olivia, Anna Lou Abatayo, Mason Daley, et al. 2026. “Investigating the Reproducibility of the Social and Behavioural Sciences.” Nature 652 (8108): 126–34. https://doi.org/10.1038/s41586-026-10203-5.
Nosek, Brian A., and Timothy M. Errington. 2020. “What Is Replication?” PLOS Biology 18 (3): e3000691. https://doi.org/10.1371/journal.pbio.3000691.
Nosek, Brian A, Timothy M Errington, Noah Haber, Theresa Stankov, and Andrew H Tyner. 2025. A Brief Glossary of Terms about Repeatability: Replicability, Robustness, and Reproducibility. mqfp4_v1. MetaArXiv. https://doi.org/10.31222/osf.io/mqfp4_v1.
Silberzahn, R., E. L. Uhlmann, D. P. Martin, et al. 2018. “Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results.” Advances in Methods and Practices in Psychological Science 1 (3): 337–56. https://doi.org/10.1177/2515245917747646.
Teney, Céline. 2016. “Does the EU Economic Crisis Undermine Subjective Europeanization? Assessing the Dynamics of CitizensEU Framing Between 2004 and 2013.” European Sociological Review 32 (5): 619–33. https://doi.org/10.1093/esr/jcw008.
Tyner, Andrew H., Anna Lou Abatayo, Mason Daley, et al. 2026. “Investigating the Replicability of the Social and Behavioural Sciences.” Nature 652 (8108): 143–50. https://doi.org/10.1038/s41586-025-10078-y.