Specification Curve: Descriptive and Inferential Statistics on All Reasonable Specifications

197 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results (2018, background): “the analyst in effect tries to come up with every different defensible analysis he or she can, runs them all, and then computes the likelihood that the number of observed significant results would be seen if there really is no effect (Simonsohn et al., 2016).”
The association between adolescent well-being and digital technology use (2019, methodology): “The study implements the SCA method to examine the correlation between our explanatory (digital technology engagement) and criterion variables (psychological well-being) using the 3-step SCA approach outlined by Simonsohn et al. 25 and applied in a recent paper by Rohrer et al. 26 .”
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (2018, methodology): “The “extreme bound analysis” of Leamer (1983) focuses on covariate combinations, the “speciﬁcation curve” of Simonsohn et al. (2015) proposes a graphical display of all effect estimates and a method for conducting joint inference across all speciﬁcations; and the method of Young and Holsteen (2017)…”
Hop to it! The impact of organization type on innovation response time to the COVID-19 crisis (2020, unknown): “By implementing a specification curve analysis (Simonsohn et al., 2015, 2020), we address the findings’ dependence on our decisions, leading to the models in Table 2.”