Measurement error and the replication crisis

By E. Loken, Andrew Gelman. Science, 2017

545 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8df836bae85482ea485007d69fb76f746e18b3e6

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Citations in the corpus

Replicability Crisis in Social Psychology: Looking at the Past to Find New Pathways for the Future (2017, background): “…assess “noisy effects” (i.e., where there are systematic random variations associated with the way variables are measured) can often yield overestimated effect sizes, whose achieved statistical significance capitalizes on random error rather than on the true parameter value (Loken & Gelman, 2017).”
Power Analysis and Effect Size in Mixed Effects Models: A Tutorial (2018, background): “The consequences of underpowered studies are becoming increasingly clear (Loken & Gelman, 2017).”
Some Natural Solutions to the p-Value Communication Problem—and Why They Won’t Work (2017, background): “This problem is central to the recent replication crisis in science (see Button et al. 2013; Loken and Gelman 2017) but is not at all touched by concerns of practical significance.”
Your Coefficient Alpha Is Probably Wrong, but Which Coefficient Omega Is Right? A Tutorial on Using R to Obtain Better Reliability Estimates (2020, background): “Measurement is an important aspect of the replication crisis facing psychology and related fields (Fried & Flake, 2018; Loken & Gelman, 2017), and it is well known that measurement error produces biased estimates of the associations among constructs that observed variables represent (e.g., Cole &…”
The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences (2017, background): “A less intuitive consequence is that the published literature can overestimate effects (Loken & Gelman, 2017).”
The Replication Crisis in Psychology: An Overview for Theoretical and Philosophical Psychology (2019, background): “Several critics, for example, argue that problematic measurement and inattention to measurement error are likely culprits in limiting the replicability of research findings (Loken & Gelman, 2017; Tackett, Brandes, King, & Markon, 2019).”