Design Sensitivity: Statistical Power for Applied Experimental Research

Author(s):  
Mark Lipsey ◽  
Sean Hurley
2021 ◽  
pp. 133-151
Author(s):  
R. Barker Bausell

While replication of research is the ultimate arbitrator of reproducibility, the process is a bit more complex than it appears. And, like any empirical study, a replication can itself be wrong. However, replications are the best tool available for determining reproducibility if (a) they employ sufficient statistical power; (b) they follow the original study procedures as closely as possible (sans any questionable research practices present therein); (c) their investigators are able to obtain the necessary information, advice, and materials from the original authors; and (d) the replication protocol is preregistered. The chapter describes different types of replications, such as exact (seldom possible for experimental research), direct (the recommended approach, which involves employing the same methodological procedures, outcome variables, and statistical approaches as the original study), conceptual (not recommended since they customarily presume the original results to be correct and are conducted to determine the extent to which said results can be extended), self (primarily useful for the original investigators to convince themselves of the validity of a finding via a replication of an original study to ensure that its results are reproducible), and partial (seldom necessary but useful when there is no alternative, such as when all of the procedures cannot be duplicated for ethical reasons).


2019 ◽  
Author(s):  
Esther Maassen ◽  
Jelte M. Wicherts

This is our commentary on the "What is IQ? Life Beyond "General Intelligence"" paper by Kovacs and Conway (2019- https://doi.org/10.1177/0963721419827275). We advocate for a latent variable approach in experimental research, and illustrate both conceptual and statistical benefits of using this approach in context of research on cognition. Benefits include the availability of more information about the constructs being measured, the ability to check for specific or general effects of covariates, and larger statistical power to detect mean differences between groups.


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