When Pre-Registration Isn’t Practical

[From the working paper, “Sound Inference in Complicated Research: A Multi-Strategy Approach” by Sanjay Srivastava, posted at PsyArXiv Preprints]
“Preregistration is effective because it creates decision independence: analytic decisions are the same regardless of the specific and potentially spurious features of the data being analyzed, instead of being overfit to them. But preregistration can be difficult in practice for some complicated research paradigms, including longitudinal studies, statistical modeling, machine learning, and other intensively multivariate designs and analyses where key decisions may be difficult to anticipate or make in advance.”
“When simple preregistration is not practical, other strategies that create decision independence can be used to augment it or in place of it. This manuscript discusses standardization, blind analyses, data partitioning, supporting studies, coordinated analysis, and multiverse analyses as additional strategies that can be used to create adaptive preregistrations or to handle decisions that were not anticipated.”
To read the paper, click here.

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