The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Increasing Transparency Through a Multiverse Analysis
Abstract
Empirical research inevitably includes constructing a data set by processing raw data into a form ready for statistical analysis. Data processing often involves choices among several reasonable options for excluding, transforming, and coding data. We suggest that instead of performing only one analysis, researchers could perform a multiverse analysis, which involves performing all analyses across the whole set of alternatively processed data sets corresponding to a large set of reasonable scenarios. Using an example focusing on the effect of fertility on religiosity and political attitudes, we show that analyzing a single data set can be misleading and propose a multiverse analysis as an alternative practice. A multiverse analysis offers an idea of how much the conclusions change because of arbitrary choices in data construction and gives pointers as to which choices are most consequential in the fragility of the result.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Journal of personality and social psychology
Armstrong, J. S. (1988) · International Journal of Forecasting · doi:10.1016/0169-2070(88)90126-4 · Cited by 10,485
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567
Moffitt, R. A. (2009) · American Economic Review · doi:10.1257/aer.99.2.660 · Cited by 4,694
On the practice of dichotomization of quantitative variables.
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.19 · Cited by 3,538
Promoting an open research culture
Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., Buck, S., Chambers, C. D., Chin, G., Christensen, G., et al. (2015) · Science · doi:10.1126/science.aab2374 · Cited by 2,708
Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling
John, L. K., Loewenstein, G., & Prelec, D. (2012) · Psychological Science · doi:10.1177/0956797611430953 · Cited by 2,239
Let's Take the Con out of Econometrics
Leamer, E. E. (2026) · Journal of Business Cycle Research · doi:10.1007/s41549-026-00137-2 · Cited by 2,202
(2011) · Cited by 2,025
Cited by
The JASP guidelines for conducting and reporting a Bayesian analysis
van Doorn, J., van den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A., Evans, N. J., Gronau, Q. F., Haaf, J. M., et al. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01798-5 · Cited by 1,114
Variability in the analysis of a single neuroimaging dataset by many teams
(2020) · doi:10.1038/s41586-020-2314-9 · Cited by 1,097
Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
Rohrer, J. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917745629 · Cited by 1,002
“Jackson, Thoemmes, Jonkmann, Lüdtke, and Trautwein (2012) were interested in the effects of military training (in comparison with civilian community service) on personality.”
BackgroundMeasurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them
Flake, J. K., & Fried, E. I. (2020) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245920952393 · Cited by 969
Wicherts, J. M., Veldkamp, C. L. S., Augusteijn, H. E. M., Bakker, M., van Aert, R. C. M., & van Assen, M. A. L. M. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01832 · Cited by 754
Metadata
- Authors
- Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W.
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2016-09
- Volume
- 11
- Issue
- 5
- Pages
- 702-712
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- OpenAlex
- W2526501380
- Cited by
- 1,372
- References
- 44
- FWCI
- 53.24
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Increasing Transparency Through a Multiverse Analysis. https://app.cochat.ai/papers/10.1177/1745691616658637
- The paper
- Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing Transparency Through a Multiverse Analysis. Perspectives on Psychological Science. https://doi.org/10.1177/1745691616658637
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
