著者
Daniël Lakens
出版者
心理学評論刊行会
雑誌
心理学評論 (ISSN:03861058)
巻号頁・発行日
vol.62, no.3, pp.221-230, 2019 (Released:2021-02-28)
参考文献数
43

For over two centuries researchers have been criticized for using research practices that makes it easier to present data in line with what they wish to be true. With the rise of the internet it has become easier to preregister the theoretical and empirical basis for predictions, the experimental design, the materials, and the analysis code. Whether the practice of preregistration is valuable depends on your philosophy of science. Here, I provide a conceptual analysis of the value of preregistration for psychological science from an error statistical philosophy (Mayo, 2018). Preregistration has the goal to allow others to transparently evaluate the capacity of a test to falsify a prediction, or the severity of a test. Researchers who aim to test predictions with severity should find value in the practice of preregistration. I differentiate the goal of preregistration from positive externalities, discuss how preregistration itself does not make a study better or worse compared to a non-preregistered study, and highlight the importance of evaluating the usefulness of a tool such as preregistration based on an explicit consideration of your philosophy of science.

言及状況

外部データベース (DOI)

Twitter (34 users, 47 posts, 186 favorites)

Severity seems under-appreciated in political science. Here's a link to another @lakens paper that discusses severity in more detail: https://t.co/jv9CKCjUse https://t.co/fbgTmCcIVC
For the second goal (increase our ability to evaluate severity of a test), the earliest pub is Mayo's book in 2018 (https://t.co/0QlTO2XmrL) and then more explicitly by Lakens in 2019 https://t.co/o68jl1kOrh (4/5)
Mayo's work on severity has been a guiding principle in my philosophy on statistics, and consequently, in my work on how to design better studies (such as when and why to preregister https://t.co/21k6F1tKA7) - go and listen! https://t.co/wPc9oeUQib
@JessicaHullman I think the most coherent view on this comes from Mayo's work on severe testing and an error-statistical philosophy of inferences. I would recommend her 2018 book. I translate this idea to preregistration here (see screenshot) https://t.co/DbU4g0hrPj https://t.co/9GvtsXXp2n
@RafMBatista @OlegUrminsky @uri_sohn @jpsimmon @m_sendhil @betsylevyp @alexoimas See the answer in my follow up tweets (and to repeat:) my paper here https://t.co/bkuQjA5MSC and especially the section in this screenshot https://t.co/kfqx6pxWEX
@RafMBatista If you want to read more about this, my best explanation is in: https://t.co/bkuQjA5MSC The screenshot is exactly the scenario you described in these tweets, so hopefully that discussion helps: https://t.co/w9skSnhPKT
@vineettiruvadi I wrote https://t.co/21k6F1uipF which i think is one of the few good papers on preregistration that are out there.
@will_ngiam @ReproducibiliT You might find this paper by D. Lakens interesting. https://t.co/akJwzHvDYM
You always need to evaluate the severity of tests, and researchers should discuss test severity more explicitly. But it gets easier as you learn more about your topic of interest, and pre-reg makes the evaluation process easier throughout. For more, see https://t.co/DbU4g0hrPj
@rfarmer27 @eba_psych @OSFramework If you want to be state-of-the-art, you can try to write a machine readable hypothesis test: https://t.co/jdN5esl7mn. And you might want to read what preregistration is actually for - what it achieves, and if that is even what you want to achieve: https://t.co/21k6F1tKA7
@WynekenHenry @tunc_necip @uygun_tunc The answer is, as always, it seems: It depends. I discuss a related hypothetical situation in my paper on preregistration and test severity: https://t.co/2nIvp3nYnW https://t.co/5pSAwSc6dg
The value of preregistration for psychological science: A conceptual analysis https://t.co/x3SzJ1Bmpp
@IoanaA_Cristea This latest from @lakens is relevant here: https://t.co/hN4wGg55ka HT @IMLahart There’s the general issue of preregistration for pilot/exploratory/audit/descriptive studies vs. studies testing hypotheses/making inferences. https://t.co/VavPecVZkS

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