What this project is about

This project is part of SFB 1287, Limits of variability in language. The principal investigators are Shravan Vasishth and Ralf Engbert. This project will provide support to the CRC by (a) advising other projects on experiment design, (b) helping projects to preregister their primary statistical analyses using the Open Science Foundation (https://osf.io/), (c) providing training in carrying out these analyses, and (d) helping with the post-publication data management and archiving for use in the public domain. This will be achieved through four work packages that (1) provide support to the CRC for optimising experiment design and analysis, (2) provide statistical education within the CRC, (3) assist in developing and maintaining a data repository to ensure preregistration, open access of data and code, and reproducibility, and (4) develop new statistical methods and computational tools, and software packages needed for the CRC, which will be available in the public domain.

Ticketing system for statistical consulting (this is only for SFB members)

Coming soon.

Preregistration tutorial

click here for the handout

Research output

[1]

Bruno Nicenboim, Timo B. Roettger, and Shravan Vasishth. Using meta-analysis for evidence synthesis: The case of incomplete neutralization in German. Submitted to Journal of Phonetics, 2017. [ pdf | http ]

[2]

Shravan Vasishth, Mary E. Beckman, Bruno Nicenboim, Fangfang Li, and Eun Jong Kong. Bayesian data analysis in the phonetic sciences: A tutorial introduction. Submitted to Journal of Phonetics, 2017. [ pdf | http ]

[3]

Shravan Vasishth, Bruno Nicenboim, Nicolas Chopin, and Robin Ryder. Bayesian Hierarchical Finite Mixture Models of Reading Times: A Case Study. Unpublished MS, 2017. [ DOI | http ]

[4]

Bruno Nicenboim, Shravan Vasishth, Felix Engelmann, and Katja Suckow. Exploratory and confirmatory analyses in sentence processing: A case study of number interference in German. Cognitive Science, 2018. Acccepted pending minor revisions. [ http ]

[5]

Shravan Vasishth, Daniela Mertzen, Lena A. Jäger, and Andrew Gelman. The statistical significance filter leads to overoptimistic expectations of replicability. 2018. [ http ]

[6]

Daniel J. Schad and Shravan Vasishth. The posterior probability of a null hypothesis given a statistically significant result. Submitted, 2019. [ http ]

[7]

Daniel J. Schad, Sven Hohenstein, Shravan Vasishth, and Reinhold Kliegl. How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Submitted, 2019. [ pdf ]

[8]

Daniel J. Schad, Michael Betancourt, and Shravan Vasishth. Towards a principled Bayesian workflow: A tutorial for cognitive science. Unpublished manuscript, 2019. [ pdf ]

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