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Reporting errors and biases in published empirical findings: Evidence from innovation research

Stephan B. Bruns, Igor Asanov, Rasmus Bode, Melanie Dunger, Christoph Funk, Sherif Hassan (), Julia Hauschildt, Dominik Heinisch, Karol Kempa, Johannes König, Johannes Lips, Matthias Verbeck, Eva Wolfschütz and Guido Buenstorf ()

Research Policy, 2019, vol. 48, issue 9, -

Abstract: Errors and biases in published results compromise the reliability of empirical research, posing threats to the cumulative research process and to evidence-based decision making. We provide evidence on reporting errors and biases in innovation research. We find that 45% of the articles in our sample contain at least one result for which the provided statistical information is not consistent with reported significance levels. In 25% of the articles, at least one strong reporting error is diagnosed where a statistically non-significant finding becomes significant or vice versa using the common significance threshold of 0.1. The error rate at the test level is very small with 4.0% exhibiting any error and 1.4% showing strong errors. We also find systematically more marginally significant findings compared to marginally non-significant findings at the 0.05 and 0.1 thresholds of statistical significance. These discontinuities indicate the presence of reporting biases. Explorative analysis suggests that discontinuities are related to authors’ affiliations and to a lesser extent the article’s rank in the issue and the style of reporting.

Keywords: Reporting bias; Reporting error; Innovation; p-hacking; Publication bias; Caliper test (search for similar items in EconPapers)
JEL-codes: C10 C12 C18 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.respol.2019.05.005

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