Publication bias in the social sciences: Unlocking the file drawer

By Annie Franco, Neil Malhotra, Gábor Simonovits. Science, 2014

1,304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5aada9835d93e361e4e1941ff01352af1e85da8e

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Citations in the corpus

Replication Research and Special Education (2016, background): “Franco, Malhotra, and Simonovits (2014), for example, reported rigorous studies that fail to find an effect are much less likely to be published than studies that find effects with less rigorous methods.”
An Agenda for Open Science in Communication (2020, background): “Similarly, there exist several accounts of publication bias in Communication (Franco, Malhotra, & Simonovits, 2014; Keating & Totzkay, 2019), which indicates a preference for novel and statistically significant results.”
Meta-Analysis of Emotional Designs in Multimedia Learning: A Replication and Extension Study (2020, background): “Studies with non-significant findings are either not published as frequently as studies with significant findings (Franco et al. 2014; Rosenthal 1979).”
Models Are Stupid, and We Need More of Them (2017, background): “Some propose that all results should be published, to ensure that a "file drawer effect" doesn't lead to over-representation of positive results (Franco et al. 2014), while others are skeptical of the value of failed replications because replication studies may have diminished power (Kahneman 2011; ”
Why You Should Always Include a Random Slope for the Lower-Level Variable Involved in a Cross-Level Interaction (2019, background): “Proving this is difficult, since about 60% of null-results are never written up (Franco et al., 2014).”
Optimal Inference After Model Selection (2014, background): “However, only the large estimates are ever published—a caricature which may not be too far from the truth, as recently demonstrated by Franco et al. (2014).”
Transparency, Reproducibility, and the Credibility of Economics Research (2018, neutral): “Figure 1 reproduces some of the main patterns from Franco, Malhotra, and Simonovits (2014), as described in (Mervis 2014b).”
An Excess of Positive Results: Comparing the Standard Psychology Literature With Registered Reports (2020, background): “…and in longitudinal data from a National Science Foundation grant program that found a file-drawering effect for studies with negative results (Franco et al., 2014, 2016), and QRPs have been admitted by scientists in several survey studies (Agnoli et al., 2017; Fiedler & Schwarz, 2016; Fraser…”
HARK Side of Deep Learning - From Grad Student Descent to Automated Machine Learning (2019, background): “As stated by Nissen et al., even if authors’ behavior is the main contributor to publication bias (there is evidence supporting this in other ﬁelds [27, 28]), they may simply be responding to the editorial preferences for positive results [29].”