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• 22 May 2024: The new development version (specr version 1.0.1)includes new functions to conduct inferences on the specificationcurve analysis (the third step outlined Simohnsohn et al., 2020). Seethisvignette forfurther details.

• 20 January 2022: specr version 1.0.0 is now available viaCRAN. This is a majorupdate with several new features and functions. Note: it introduces anew framework for conduction specification curve analyses compared toearlier versions (seeversionhistory for moredetails).

• 4 December 2020: specr development version 0.2.2 is available viagithub. Mostly minor updates and bug fixes.

• 25 May 2020: specr version 0.2.1 has been released on CRAN.

The goal of specr is to facilitate specification curve analyses(Simonsohn, Simmons & Nelson, 2020; also known as multiverse analyses,see Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016). The package can beused to investigate how different (theoretically plausible) analyticalchoices affect outcome statistics within the universe of one single dataset. It provides functions to setup, run, evaluate, and plot themultiverse of specifications. A simple example of how to use specr isprovided below. For more information about the various functions andspecific vignettes and use cases, visit thedocumentation.

We do see a lot of value in investigating how analytical choices affecta statistical outcome of interest. However, we strongly caution againstusingspecr as a tool to somehow arrive at a better estimate. Runninga specification curve analysis does not make your findings any morereliable, valid or generalizable than a single analysis. The method isonly meant to inform about the effects of analytical choices on results,and not a better way to estimate a correlation or effect.

Install specr from CRAN:

install.packages("specr")

Or install the most recent development version fromGitHub with:

# install.packages("devtools")devtools::install_github("masurp/specr")

Using specr is comparatively simple. The two main function aresetup(), in which analytic choices are specified as arguments, andspecr(), which fits the models across all specifications. The lattercreates a class called “specr.object”, which can be summarized andplotted with generic function such assummary orplot.

# Load package ----library(specr)# Setup Specifications ----specs<- setup(data=example_data,y= c("y1","y2"),x= c("x1","x2"),model= c("lm"),controls= c("c1","c2"),subsets=list(group1= unique(example_data$group1),group2= unique(example_data$group2)))# Run Specification Curve Analysis ----results<- specr(specs)# Plot Specification Curve ----plot(results)

citation("specr")#>#> To cite 'specr' in publications use:#>#>   Masur, Philipp K. & Scharkow, M. (2020). specr: Conducting and#>   Visualizing Specification Curve Analyses. Available from#>   https://CRAN.R-project.org/package=specr.#>#> A BibTeX entry for LaTeX users is#>#>   @Misc{,#>     title = {specr: Conducting and Visualizing Specification Curve Analyses (Version 1.0.1)},#>     author = {Philipp K. Masur and Michael Scharkow},#>     year = {2020},#>     url = {https://CRAN.R-project.org/package=specr},#>   }

• Simonsohn, U., Simmons, J.P. & Nelson, L.D. (2020). Specificationcurve analysis.Nature Human Behaviour, 4, 1208–1214.https://doi.org/10.1038/s41562-020-0912-z

• Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016).Increasing Transparency Through a Multiverse Analysis.Perspectiveson Psychological Science, 11(5), 702-712.https://doi.org/10.1177/1745691616658637

The following papers and websites are interesting resources to explorethe method further and learn about potential promises and pitfalls:

• Good discussion of the multiverse analysis method: Del Giudice, M., &Gangestad, S. W. (2021). A traveler’s guide to the multiverse:Promises, pitfalls, and a framework for the evaluation of analyticdecisions.Advances in Methods and Practices in PsychologicalScience, 4(1).https://journals.sagepub.com/doi/abs/10.1177/2515245920954925

• A systematic comparison of visualization techniques for multiverseanalyses: Hall, B. D., Liu, Y., Jansen, Y., Dragicevic, P., Chevalier,F., & Kay, M. (2022,). A survey of tasks and visualizations inmultiverse analysis reports. InComputer Graphics Forum (Vol. 41,No. 1,pp. 402-426).https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14443

• Proposal to include data collection decisions as choices within themultiverse: Harder, J. A. (2020). The multiverse of methods: Extendingthe multiverse analysis to address data-collection decisions.Perspectives on Psychological Science, 15(5), 1158-1177.https://journals.sagepub.com/doi/abs/10.1177/1745691620917678

• Proposal on how the multiverse approach can be implemented in studentresearch projects: Heyman, T., & Vanpaemel, W. (2022). Multiverseanalyses in the classroom.Meta-Psychology, 6.https://open.lnu.se/index.php/metapsychology/article/view/2718

• Overview of different packages for specification curve/multiverseanalysis:https://urisohn.com/specification-curve/

If you have published a paper in which you usedspecr and you wouldlike to be included in the following list, please send an email toPhilipp.

• Akaliyski, P., Minkov, M., Li, J., Bond, M. H., & Gehring, S. (2022).The weight of culture: Societal individualism and flexibility explainlarge global variations in obesity.Social Science & Medicine, 307.https://doi.org/10.1016/j.socscimed.2022.115167

• Ballou, N., & van Rooij, A. J. (2021). The relationship between mentalwell-being and dysregulated gaming: a specification curve analysis ofcore and peripheral criteria in five gaming disorder scales.TheRoyal Society Open Science.https://doi.org/10.1098/rsos.201385

• Ballou, N., & Zendle, D. (2022). “Clinically significant distress” ininternet gaming disorder: An individual participant meta-analysis.Computers in Human Behavior, 129.https://doi.org/10.1016/j.chb.2021.107140

• Burton, J.W., Cruz, N. & Hahn, U. (2021). Reconsidering evidence ofmoral contagion in online social networks.Nature Human Behaviour.https://doi.org/10.1038/s41562-021-01133-5

• Cantone, G. G., & Tomaselli, V. (2023). Theory and methods of themultiverse: an application for panel-based models.Quality &Quantity, 1-34.

• Cantone, G. G., & Tomaselli, V. (2024). Characterisation andCalibration of Multiversal Models. Preprint:https://osf.io/download/6627aeecc5851a0791f66f6c/

• Cosme, D., & Lopez, R. B. (2023). Neural indicators of food cuereactivity, regulation, and valuation and their associations with bodycomposition and daily eating behavior. Social Cognitive and AffectiveNeuroscience, 18(1).https://doi.org/10.1093/scan/nsaa155

• Del Giudice, M., & Gangestad, S. W. (2021). A Traveler’s Guide to theMultiverse: Promises, Pitfalls, and a Framework for the Evaluation ofAnalytic Decisions.Advances in Methods and Practices inPsychological Science.https://doi.org/10.1177/2515245920954925

• De Vries, I., Baglivio, M., & Reid, J. A. (2024). Examining individualand contextual correlates of victimization for juvenile humantrafficking in Florida.Journal of interpersonalviolence, 08862605241243332.https://journals.sagepub.com/doi/abs/10.1177/08862605241243332

• Haehner, P., Kritzler, S., & Luhmann, M. (2023). Can Perceived andObjective-Descriptive Event Characteristics Explain IndividualDifferences in Changes in Subjective Well-Being After Negative LifeEvents? A Specification Curve Analysis.

• Henson, P., Rodriguez-Villa, E., Torous, J. (2021). InvestigatingAssociations Between Screen Time and Symptomatology in IndividualsWith Serious Mental Illness: Longitudinal Observational StudyJournalof Medical Internet Research, 23(3), e23144.https://doi.org/10.2196/23144

• Huang, S., Lai, X., Zhao, X., Dai, X., Yao, Y., Zhang, C., & Wang, Y.,(2022). Beyond screen time: Exploring associations between types ofsmartphone use content and adolescents’ social relationships.International Journal of Environmental Research and Public Health,19, 8940.https://doi.org/10.3390/ijerph19158940

• Jones, A., Petrovskaya, E., & Stafford, T. (2024). Exploring themultiverse of analysis options for the alcohol Stroop.BehaviorResearch Methods, 1-11.https://link.springer.com/article/10.3758/s13428-024-02377-5

• Kleinert, M. (2024). Reconsidering the Relationship BetweenAnti-immigration Attitudes and Preferences for the AfD Using ImplicitAttitudes Measures.Politische Vierteljahresschrift, 65(1), 71-98.

• Kritzler, S., & Luhmann, M. (2021, March 25). Be Yourself and BehaveAppropriately: Exploring Associations Between Incongruent PersonalityStates and Positive Affect, Tiredness, and Cognitive Performance.https://doi.org/10.31234/osf.io/9utyj

• Mao, Z. F., Li, Q. W., Wang, Y. M., & Zhou, J. (2024). Pro-religionattitude predicts lower vaccination coverage at country level.Humanities and Social Sciences Communications, 11(1), 1-9.

• Masur, P. K. (2021). Understanding the Effects of Conceptual andAnalytical Choices on ‘Finding’ the Privacy Paradox: A SpecificationCurve Analysis of Large-Scale Survey Data.Information, Communication& Society.https://doi.org/10.1080/1369118X.2021.1963460

• Masur, P. K., & Ranzini, G. (2024). Privacy Calculus, Privacy Paradox,and Context Collapse: A Replication of Three Key Studies inCommunication Privacy Research.SocArXiv.https://osf.io/preprints/socarxiv/8tr2k

• Prasad, S., Knight, E. L., Sarkar, A., Welker, K. M., Lassetter, B., &Mehta, P. H. (2021). Testosterone fluctuations in response to ademocratic election predict partisan attitudes toward the electedleader.Psychoneuroendocrinology, 133, 105396.

• Rauvola, R. S., & Rudolph, C. W. (2023). Worker aging, control, andwell-being: A specification curve analysis.Acta Psychologica, 233.

• Sekścińska, K., Jaworska, D., & Rudzinska‐Wojciechowska, J. (2024).The effect of state and trait power on financial risk taking: Themediating and moderating roles of focus on rewards versus threats.Journal of Behavioral Decision Making, 37(1), e2363.https://doi.org/10.1002/bdm.2363

• Tisdall, L., & Mata, R. (2023). Age differences in the neural basis ofdecision-making under uncertainty.Cognitive, Affective, & BehavioralNeuroscience.https://doi.org/10.3758/s13415-022-01060-6

• Tünte, M. R., Hoehl, S., Wunderwald, M., Bullinger, J., Boyadziheva,A., Maister, L., … & Kayhan, E. (2023). Respiratory and CardiacInteroceptive Sensitivity in the First Two Years of Life.eLife, 12.https://elifesciences.org/reviewed-preprints/91579

• van Veelen, H.P.J., Ibáñez-Álamo, J.D., Horrocks, N.P.C. etal. (2023). Cloacal microbiota are biogeographically structured inlarks from desert, tropical and temperate areas.BMC Microbiol23(40).https://doi.org/10.1186/s12866-023-02768-2

• Visontay, R., Mewton, L., Sunderland, M., Bell, S., Britton, A.,Osman, B., … & Slade, T. (2023). A comprehensive evaluation of thelongitudinal association between alcohol consumption and a measure ofinflammation: Multiverse and vibration of effects analyses.Drug andAlcohol Dependence, 247.https://doi.org/10.1016/j.drugalcdep.2023.109886

• Wang, Y., Pitre, T., Wallach, J. D., de Souza, R. J., Jassal, T.,Bier, D., … & Zeraatkar, D. (2024). Grilling the data: Application ofspecification curve analysis to red meat and all-cause mortality.Journal of Clinical Epidemiology, 111278.https://doi.org/10.1016/j.jclinepi.2024.111278

• Yu, R. P. (2024). Divides in News Verification: Antecedents andPolitical Outcomes of News Verification by Age.Digital Journalism,1-21.https://www.tandfonline.com/doi/abs/10.1080/21670811.2024.2314582

• Yuan, Q., Li, H., Du, B., Dang, Q., Chang, Q., Zhang, Z., … & Guo, T.(2023). The cerebellum and cognition: further evidence for its role inlanguage control.Cerebral Cortex, 33(1), 35-49.https://doi.org/10.1093/cercor/bhac051